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On the relation between decision quality and autonomy in times of patient-centered care: a case study

• Scientific Contribution
• Published: 09 August 2022
• Volume 25 , pages 629–639, ( 2022 )

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• Jasper Debrabander   ORCID: orcid.org/0000-0002-2084-9090 1  

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A Correction to this article was published on 05 December 2022

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It is commonplace that care should be patient-centered. Nevertheless, no universally agreed-upon definition of patient-centered care exists. By consequence, the relation between patient-centered care as such and ethical principles cannot be investigated. However, some research has been performed on the relation between specific models of patient-centered care and ethical principles such as respect for autonomy and beneficence. In this article, I offer a detailed case study on the relationship between specific measures of patient-centered care and the ethical principle of respect for autonomy. Decision Quality Instruments (DQIs) are patient-centered care measures that were developed by Karen Sepucha and colleagues. The model of patient-centered care that guided the development of these DQIs pays special attention to the ethical principle of respect for autonomy. Using Jonathan Pugh’s theory of rational autonomy, I will investigate how the DQIs relate to patient autonomy. After outlining Pugh’s theory of rational autonomy and framing the DQIs accordingly (Part I), I will investigate whether the methodological choices made while developing these DQIs align with respect for autonomy (Part II). My analysis will indicate several tensions between DQIs and patient autonomy that could result in what I call “structural paternalism.” These tensions offer us sufficient reasons, especially given the importance of the ethical principle of respect for autonomy, to initiate a more encompassing debate on the normative validity of Decision Quality Instruments. The aim of the present paper is to highlight the need for, and to offer a roadmap to, this debate.

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Shared Decision Making

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Change history, 05 december 2022.

A Correction to this paper has been published: https://doi.org/10.1007/s11019-022-10131-x

Besides, many terminological differences exist as well. Related terms such as “person-centered care” and “family-centered medicine” have been coined (e.g. Louw et al. 2017 ). As “patient-centered care” is the term used in the case and I have no ambition to review differences and similarities between “patient-centered care” and its cognates, I will restrict myself to that term.

I take the latter use implicitly for granted in what follows.

As I suggested elsewhere, similar tools might also be used to evaluate some effects of Clinical Decision Support Systems (CDSS) (Debrabander and Mertes 2021 )

In what follows I use “decisional autonomy” as shorthand for “the decisional dimension of autonomy.”

The picture is slightly more complex as theoretical rationality not only concerns the beliefs about the choice options, but also the evaluative beliefs a person holds (e.g. how important is longevity to me?). By consequence, theoretical and practical rationality are “not entirely separate domains” as the former is in part about the evaluative beliefs in light of which the practical rationality of people’s choices is judged (Pugh 2020 , p. 43). Still, they are independent in the sense that the degree of theoretical rationality does not influence the degree of practical rationality and vice versa (Pugh 2020 , pp. 21–25).

For the remainder of this paper, I use patients’ “goals,” “values” and “preferences” interchangeably, unless indicated otherwise.

Nevertheless, they are not unambiguous on this point. In the same article, they note that “The purpose of the Breast Cancer Surgery Decision Quality Instrument is to provide a comprehensive assessment of the extent to which patients make informed decisions and receive treatments that match their goals” (Sepucha et al. 2012a , p. 7; my emphasis).

Further considerations on setting a threshold for well-informedness will be mentioned at the end of Section “ Relating knowledge and concordance ”.

Even if known cognitive biases could explain the low knowledge scores, the possibility to mitigate the influence of these biases should not be overlooked, albeit I am aware of the limited success of such “debiasing strategies” (Blumenthal-Barby 2021 , pp. 107–109).

One might wonder whether the inclusion of value items such as “spouse’s goals” would not legitimate problematic influences of patients’ spouses, thereby reinforcing problematic power imbalances between partners. In my opinion, including such a value item might, quite to the contrary, allow us to trace some of these power imbalances given that they could result in problematically high scores for that value item. I thank an anonymous reviewer for raising this point.

In this paragraph I use “treatment preference” to indicate the preference a patient has with regard to a treatment (e.g. mastectomy or lumpectomy plus radiation). This contrasts with the previous use of “preference” as interchangeable with “value” and “goal”, indicating the importance of an aspect or consequence of a treatment (e.g. survival rate, impact on quality of life, …).

Ahmed, Sadia, Andrea Djurkovic, Kimberly Manalili, Balreen Sahota, and Maria J. Santana. 2019. A qualitative study on measuring patient-centered care: Perspectives from clinician-scientists and quality improvement experts. Health Science Reports . https://doi.org/10.1002/hsr2.140 .

Article   Google Scholar  

Alexandrova, Anna. 2017. A philosophy for the science of well-being . Oxford: Oxford University Press.

Book   Google Scholar  

Alexandrova, Anna, and Daniel M. Haybron. 2016. Is construct validation valid? Philosophy of Science 83 (5): 1098–1109. https://doi.org/10.1086/687941 .

Banner, Natalie F., and George Szmukler. 2013. “Radical interpretation” and the assessment of decision-making capacity: “Radical interpretation” and decision-making capacity. Journal of Applied Philosophy 30 (4): 379–394. https://doi.org/10.1111/japp.12035 .

Barry, Michael J., Susan Edgman-Levitan, and Karen Sepucha. 2018. Shared decision-making: Staying focused on the ultimate goal. NEJM Catalyst . https://doi.org/10.1056/CAT.18.0097 .

Blumenthal-Barby, Jennifer S. 2021. Good ethics and bad choices: The relevance of behavioral economics for medical ethics . Cambridge: MIT Press.

Christman, John Philip. 2009. The politics of persons: Individual autonomy and socio-historical selves . Cambridge: Cambridge University Press.

Debrabander, Jasper, and Heidi Mertes. 2021. Watson, autonomy and value flexibility: Revisiting the debate. Journal of Medical Ethics . https://doi.org/10.1136/medethics-2021-107513 .

Dowie, Jack, and MetteKjer Kaltoft. 2020. Decision quality is a preference-sensitive formative concept: How do some existing measures compare? In Digital personalized health and medicine , ed. Pape-Haugaard, et al., 562–566. Amsterdam: IOS Press.

Google Scholar  

Durand, Marie-Anne., Renata W. Yen, A. James O’Malley, Danielle Schubbe, Mary C. Politi, Catherine H. Saunders, Shubhada Dhage, et al. 2021. What matters most: Randomized controlled trial of breast cancer surgery conversation aids across socioeconomic strata. Cancer 127 (3): 422–436. https://doi.org/10.1002/cncr.33248 .

Ells, Carolyn, Matthew R. Hunt, and Jane Chambers-Evans. 2011. Relational autonomy as an essential component of patient-centered care. IJFAB: International Journal of Feminist Approaches to Bioethics 4 (2): 79–101. https://doi.org/10.3138/ijfab.4.2.79 .

Elwyn, Glyn, Benjamin Elwyn, and Talya Miron-Shatz. 2009. Measuring “decision quality”: Irresolvable difficulties and an alternative proposal. In Shared decision-making in health care. Achieving evidence-based patient choice , ed. Adrian Edwards and Glyn Elwyn, 143–149. Oxford: Oxford University Press.

Faden, Ruth R., Tom L. Beauchamp, and Nancy M. P. King. 1986. A history and theory of informed consent . New York: Oxford University Press.

Giusti, Alessandra, Kennedy Nkhoma, Ruwayda Petrus, Inge Petersen, Liz Gwyther, Lindsay Farrant, Sridhar Venkatapuram, and Richard Harding. 2020. The empirical evidence underpinning the concept and practice of person-centred care for serious illness: A systematic review. BMJ Global Health 5 (12): e003330. https://doi.org/10.1136/bmjgh-2020-003330 .

Grisso, Thomas, Paul S. Appelbaum, and Carolyn Hill-Fotouhi. 1997. The MacCAT-T: a clinical tool to assess patients’ capacities to make treatment decisions. Psychiatric Services 48 (11): 1415–1419. https://doi.org/10.1176/ps.48.11.1415 .

Hansson, Sven Ove, and Barbro Fröding. 2021. Ethical conflicts in patient-centred care. Clinical Ethics 16 (2): 55–66. https://doi.org/10.1177/1477750920962356 .

Hawley, Sarah T., Yun Li, Lawrence C. An, Kenneth Resnicow, Nancy K. Janz, Michael S. Sabel, Kevin C. Ward, et al. 2018. Improving breast cancer surgical treatment decision making: The icandecide randomized clinical trial. Journal of Clinical Oncology 36 (7): 659–666. https://doi.org/10.1200/JCO.2017.74.8442 .

Institute of Medicine. 2001. Envisioning the national health care quality report . Washington: National Academies Press. https://doi.org/10.17226/10073 .

Kaltoft, Mette Kjer, Jesper Bo Nielsen, Glenn Salkeld, and Jack Dowie. 2015. Who should decide how much and what information is important in person-centred health care? Journal of Health Services Research & Policy 20 (3): 192–195. https://doi.org/10.1177/1355819614567911 .

Krumins, Peter E., Stephan D. Fihn, and Daniel L. Kent. 1988. Symptom severity and patients’ values in the decision to perform a transurethral resection of the prostate. Medical Decision Making 8 (1): 1–8. https://doi.org/10.1177/0272989X8800800101 .

Louw, Jakobus M., Tessa S. Marcus, and Johannes F.M.. Hugo. 2017. Patient- or person-centred practice in medicine?—A review of concepts. African Journal of Primary Health Care & Family Medicine . https://doi.org/10.4102/phcfm.v9i1.1455 .

Louw, Jakobus M., Tessa S. Marcus, and Jannie Hugo. 2020. How to measure person-centred practice—An analysis of reviews of the literature. African Journal of Primary Health Care & Family Medicine . https://doi.org/10.4102/phcfm.v12i1.2170 .

MGH Health Decision Sciences Center. 2021a. Decision quality instruments. https://mghdecisionsciences.org/tools-training/decision-quality-instruments/ . Accessed November 22, 2021.

MGH Health Decision Sciences Center. 2021b. Decision quality. https://mghdecisionsciences.org/tools-training/decision-quality/ . Accessed November 22, 2021.

Mulley, Albert G. 1989. Assessing patients’ utilities: Can the ends justify the means? Medical Care 27 (Supplement): S269–S281. https://doi.org/10.1097/00005650-198903001-00021 .

Pugh, Jonathan. 2020. Autonomy, rationality, and contemporary bioethics . Oxford: Oxford University Press.

Schwalm, Jon-David., Dawn Stacey, Dan Pericak, and Madhu K. Natarajan. 2012. Radial artery versus femoral artery access options in coronary angiogram procedures: Randomized controlled trial of a patient-decision aid. Circulation: Cardiovascular Quality and Outcomes 5 (3): 260–266. https://doi.org/10.1161/CIRCOUTCOMES.111.962837 .

Sepucha, Karen R., Floyd J. Fowler, and Albert G. Mulley. 2004. Policy support for patient-centered care: the need for measurable improvements in decision quality: Documenting gaps in patients’ knowledge could stimulate rapid change, moving decisions and care closer to a patient-centered ideal. Health Affairs 23 (Suppl2): 54–62. https://doi.org/10.1377/hlthaff.var.54 .

Sepucha, Karen, Elissa Ozanne, Kerry Silvia, Ann Partridge, and Albert G. Mulley. 2007. An approach to measuring the quality of breast cancer decisions. Patient Education and Counseling 65 (2): 261–269. https://doi.org/10.1016/j.pec.2006.08.007 .

Sepucha, Karen R., Jeffrey K. Belkora, Yuchiao Chang, Carol Cosenza, Carrie A. Levin, Beverly Moy, Ann Partridge, and Clara N. Lee. 2012a. Measuring decision quality: Psychometric evaluation of a new instrument for breast cancer surgery. BMC Medical Informatics and Decision Making 12 (1): 51. https://doi.org/10.1186/1472-6947-12-51 .

Sepucha, Karen R., Sandra Feibelmann, William A. Abdu, Catharine F. Clay, Carol Cosenza, Stephen Kearing, Carrie A. Levin, and Steven J. Atlas. 2012b. Psychometric evaluation of a decision quality instrument for treatment of lumbar herniated disc. Spine 37 (18): 1609–1616. https://doi.org/10.1097/BRS.0b013e3182532924 .

Sepucha, Karen R., Daniel D. Matlock, Celia E. Wills, Mary Ropka, Natalie Joseph-Williams, Dawn Stacey, ChirkJenn Ng, et al. 2014. “It’s valid and reliable” is not enough: Critical appraisal of reporting of measures in trials evaluating patient decision aids. Medical Decision Making 34 (5): 560–566. https://doi.org/10.1177/0272989X14528381 .

Solberg, Leif I., Stephen E. Asche, Louise H. Anderson, N. Karen Sepucha, Marcus Thygeson, Joan E. Madden, Larry Morrissey, and Karen K. Kraemer. 2009. Evaluating preference-sensitive care for uterine fibroids: It’s not so simple. Journal of Women’s Health 18 (7): 1071–1079. https://doi.org/10.1089/jwh.2008.0948 .

Stacey, Dawn, France Légaré, Krystina Lewis, Michael J. Barry, Carol L. Bennett, Karen B. Eden, Margaret Holmes-Rovner, et al. 2017. Decision aids for people facing health treatment or screening decisions. Edited by Cochrane Consumers and Communication Group. Cochrane Database of Systematic Reviews . https://doi.org/10.1002/14651858.CD001431.pub5 .

Tanenbaum, Sandra J. 2015. What is patient-centered care? A typology of models and missions. Health Care Analysis 23 (3): 272–287. https://doi.org/10.1007/s10728-013-0257-0 .

Trenaman, Logan, Jesse Jansen, Jennifer Blumenthal-Barby, Mirjam Körner, Joanne Lally, Daniel Matlock, Lilisbeth Perestelo-Perez, et al. 2021. Are we improving? Update and critical appraisal of the reporting of decision process and quality measures in trials evaluating patient decision aids. Medical Decision Making 41 (7): 954–959. https://doi.org/10.1177/0272989X211011120 .

Winn, Karen, Elissa Ozanne, and Karen Sepucha. 2015. Measuring patient-centered care: An updated systematic review of how studies define and report concordance between patients’ preferences and medical treatments. Patient Education and Counseling 98 (7): 811–821. https://doi.org/10.1016/j.pec.2015.03.012 .

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Acknowledgements

I would like to thank Heidi Mertes, Seppe Segers, Erik Weber and two anonymous reviewers for their valuable comments on earlier drafts.

This research was made possible by grant BOFSTG2020002501 of Ghent University’s Special Research Fund.

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Debrabander, J. On the relation between decision quality and autonomy in times of patient-centered care: a case study. Med Health Care and Philos 25 , 629–639 (2022). https://doi.org/10.1007/s11019-022-10108-w

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• Published: 22 August 2012

Determinants of patient choice of healthcare providers: a scoping review

• Aafke Victoor 1 ,
• Diana MJ Delnoij 2 , 3 ,
• Roland D Friele 1 , 2 &
• Jany JDJM Rademakers 1  

BMC Health Services Research volume  12 , Article number:  272 ( 2012 ) Cite this article

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In several northwest European countries, a demand-driven healthcare system has been implemented that stresses the importance of patient healthcare provider choice. In this study, we are conducting a scoping review aiming to map out what is known about the determinants of patient choice of a wide range of healthcare providers. As far as we know, not many studies are currently available that attempt to draw a general picture of how patients choose a healthcare provider and of the status of research on this subject. This study is therefore a valuable contribution to the growing amount of literature about patient choice.

We carried out a specific type of literature review known as a scoping review. Scoping reviews try to examine the breadth of knowledge that is available about a particular topic and therefore do not make selections or apply quality constraints. Firstly, we defined our research questions and searched the literature in Embase, Medline and PubMed. Secondly, we selected the literature, and finally we analysed and summarized the information.

Our review shows that patients’ choices are determined by a complex interplay between patient and provider characteristics. A variety of patient characteristics determines whether patients make choices, are willing and able to choose, and how they choose. Patients take account of a variety of structural, process and outcome characteristics of providers, differing in the relative importance they attach to these characteristics.

Conclusions

There is no such thing as the typical patient: different patients make different choices in different situations. Comparative information seems to have a relatively limited influence on the choices made by many patients and patients base their decisions on a variety of provider characteristics instead of solely on outcome characteristics. The assumptions made in health policy about patient choice may therefore be an oversimplification of reality. Several knowledge gaps were identified that need follow-up research.

Peer Review reports

In most European countries, patients were not encouraged to actively choose their healthcare provider. Patient choice has only recently gained importance in a number of northwest European countries, such as the Netherlands and the UK [ 1 , 2 ]. Important reasons for promoting patient choice were to reduce waiting times and to encourage competition between providers. Competition was expected to make care more responsive to patients and, among other things, improve efficiency (including cost decreases), quality and (in the UK) equity of healthcare [ 2 – 4 ]. In the Netherlands in 2006 for example, a demand-driven healthcare system was implemented to enhance competition between providers as a means of helping to achieve these goals. Another goal of emphasizing patient choice was to protect and promote the position of patients in healthcare [ 5 ]. It should be noted that some studies have shown that consumer-directed healthcare does not control costs better than other healthcare systems [ 6 ] and that its effects on quality are mixed [ 7 ]. This is, however, beyond the scope of this study.

The principle through which patient choice is assumed to bring about competition between healthcare providers is ‘voting with your feet’ [ 8 ]. This means that patients who are looking for high-quality care while minimizing costs will directly compare the prices and quality of different providers against each other and actively choose the provider that best fits their preferences and needs. In this context, ‘actively’ means that patients invest effort in acquiring information and making a conscious decision based on that information. If the money follows the patients, this selection process will encourage providers to compete for patients by improving their quality and decreasing their costs [ 9 – 12 ], which eventually helps ensure the quality, efficiency and equity of healthcare [ 11 , 13 , 14 ]. This line of reasoning applies not only to northwest European countries [ 2 , 3 , 5 , 15 – 17 ] but also to the USA, where patient choice was already an important element in the healthcare system [ 18 ].

For patients to be able to actively choose the best provider, they need to be informed about the quality of providers. Quality indicators were therefore developed. A quality indicator is a measurable aspect of care that gives an indication of the quality of care [ 19 ] and may concern the structure, process or outcomes of care delivered by a provider [ 20 , 21 ]. Structure indicators concern the organization of healthcare, whereas process indicators relate to the care delivery process and outcome indicators indicate the effect of the care delivered. Because patients have different information preferences, comparative information for all indicators is developed to enable patients to select the information that is relevant for them and to choose a provider based on that information [ 5 , 20 ].

Although patients are given a large amount of comparative information and are expected to choose the best provider based on this information plus information about prices, it is however questionable whether patients are indeed willing and capable to act as assumed. Questions arise such as whether patients do indeed actively choose their providers, whether they use the information provided, and whether a country’s health insurance system gives them enough opportunity and freedom to choose.

Research focus

Although patient choice of healthcare providers is gaining importance in northwest European countries, it is not certain whether patients do behave as assumed. It is therefore high time that information is gathered on what is already known about this subject. In the current study, we are conducting a scoping review with the goals of describing the findings and range of research concerning patient choice of a wide range of healthcare providers in more detail (no studies were excluded based on the provider type) and of identifying knowledge gaps in the existing literature. We have not made selections or applied any quality constraints [ 22 ]. To our knowledge, not many studies exist that share this goal. This study is therefore contributing to the growing amount of literature on this subject. The three research questions we aim to answer are: (1) Do patients actively choose their healthcare providers? (2) How do patients choose their preferred healthcare provider? and (3) Which provider characteristics do they base their choice on?

Scoping review

We conducted a scoping review. A scoping review is a kind of literature review that is used when: a) a narrow review question cannot be defined; b) studies have employed a range of data collection and analysis techniques; c) no prior synthesis has been undertaken on the topic; and d) the reviewers are not going to assess the quality of the studies reviewed [ 23 ].

Search strategy and selection of the literature

The search was conducted on 17 August 2011 by one of the authors (AV). The databases used were Embase, Medline and PubMed. The keywords (i.e. patient, consumer, choice, provider, hospital, physician, doctor and their plurals) were determined after an initial broad search of the literature and consultations with a librarian and an expert on literature reviews. We decided to use a narrowly defined search string because otherwise the numerous irrelevant studies concerning choice of a health plan or treatment would outweigh the studies concerning patients’ choice of a provider. Only studies written in English were included, which can be justified by the observation that almost all references cited by the studies identified in the initial broad search were in English. This suggests that the most important sources are available in English. We only included studies from Western countries because the health insurance systems of other countries differ too much. For example, access to healthcare may be limited or healthcare services may not be well developed [ 24 ]. As healthcare systems have changed a great deal over past decades, we only included scientific papers from 1995 and later. The inclusion and exclusion criteria and the search string are shown in Table 1 . This table also shows that post-hoc exclusion criteria were developed after a first review round and then applied in a second round. The development of such ‘post hoc’ criteria is central to the scoping review process as it is unlikely that researchers will be able to identify parameters for exclusion at the outset [ 23 ]. The selection method and search flow are represented in Figure 1 .

Search strategy and results.

Data extraction

A spreadsheet was created to chart the information that contributed to answering the research questions. Details of publication information, the choice situation, the study sample, the country in which the study took place and the kind of provider for which the preferences were assessed were recorded along with this information. This process was carried out by one of the authors (AV). The information extracted that helped answer the research questions was discussed with the other authors during team meetings in order to work towards an overall perspective on the factors emerging from the literature. Disagreements were discussed until a consensus was reached.

Search flow

As shown in Figure 1 , a total of 1877 publications were identified from the databases, of which 973 were duplicates. At the end of the selection process, 118 studies remained for further analysis (Figure 1 ). In Table 2 , an overview of the characteristics of these studies is given.

Study characteristics

Study sample and choice situation.

Most studies (n = 70) used only patients as participants, e.g. [ 25 – 30 ]. Other studies looked at the general (adult) population, or a specific subclass of the population such as those in work or with insurance, the elderly or people of a specific ethnicity or gender.

For the choice situation, the majority of studies (n = 49), e.g. [ 31 – 36 ], used discrete choice experiments or questionnaires asking participants about potential choices and preferences, while somewhat fewer studies investigated patient choice in real choice situations (n = 43), e.g. [ 27 , 28 , 37 – 40 ]. Only a few studies combined the analysis of real choice situations with experiments or questionnaires (n = 11) [ 30 , 41 – 50 ].

The majority of studies into patient choice took place in the USA (n = 51), e.g. [ 18 , 25 , 27 , 29 , 51 , 52 ], followed by the Netherlands (n = 29), e.g. [ 30 , 31 , 46 , 53 – 55 ], and the UK (n = 19), e.g. [ 26 , 35 , 38 , 56 – 58 ]. Countries with less research on the subject are Canada, France, Australia, Finland, Sweden, Norway, Belgium and Germany. There are two areas that studies from the USA examined relatively more often than those from Europe: revealed preference research (based on analysis of registration data) about the use of comparative information, and research into the influence of health plans on patients’ choices.

Kind of provider

Many studies do not focus on a particular kind of healthcare provider, but focus on several types of healthcare provider or do not specify what they are focusing on (n = 25), e.g. [ 59 – 64 ]. Of the studies that do focus on a particular kind of provider, choice of healthcare institutions (n = 54), e.g. [ 27 , 29 , 31 , 65 – 67 ], has been investigated more often than choice of individual providers (n = 31), e.g. [ 68 – 73 ]. Most studies that investigated the choice of an institution were investigating the choice of a hospital (n = 46), e.g. [ 27 , 29 , 31 , 51 , 57 , 74 ]. Of the studies investigating the choice of an individual provider, most concerned the choice of a GP, family physician or primary care doctor (n = 12), e.g. [ 3 , 18 , 37 , 68 , 75 , 76 ], followed by the choice of an obstetrician or gynaecologist (n = 7), e.g. [ 43 , 52 , 70 , 72 , 77 , 78 ].

First research question: do patients actively choose their healthcare providers?

Research shows that few patients actively choose their healthcare provider [ 16 , 30 , 41 , 47 – 49 , 64 ]. For example, Schwartz (2005) found that only ten per cent of patients seriously considered an alternative to their local hospital when undergoing surgery [ 49 ]. Generally, patients rely on their GP to choose for them [ 2 , 41 , 49 , 67 , 70 , 79 , 80 ] or go to the nearest provider [ 27 , 59 , 81 ]. Furthermore, patients rely on their previous healthcare experiences when deciding where to receive care [ 25 , 46 , 47 , 49 ]. This seems to apply to both Europe and the USA (for those patients who can choose). However, certain patient groups (such as more highly educated and younger patients [ 59 , 79 , 80 , 82 , 83 ], patients with higher incomes [ 59 , 82 , 83 ] and patients without an existing (satisfactory) relationship with a provider [ 42 , 47 ]) make an active choice more often.

According to several studies, a substantial fraction of the patients does not consider choice to be very important [ 16 , 43 , 64 , 84 , 85 ]. Consequently, these patients are less likely to make an active choice. Even so, they find choosing a GP or hospital more important than choosing a hospital specialist [ 84 ]. The importance patients attach to choice differs between patient groups. For example, according to one study, older patients, female patients, those who live further away from a hospital, less highly educated patients and those with a bad experience with their local hospital are more favourably inclined towards the free choice of hospital [ 47 ]. A second reason for patients not to choose actively is that the degree of choice they experience or their ability to exercise their choice is limited. For example, patients’ perceived degree of choice or ability to choose was found to be influenced positively by family income [ 16 , 85 , 86 ], general state of health [ 85 ] and willingness and ability to travel [ 16 ], and negatively by restrictions imposed by health insurers [85,86], age and female gender [16]. Additionally, some studies found that some patient groups are more likely to be offered a choice of provider by their GP than other patient groups, e.g. Caucasians [ 2 ], healthier patients and patients who need an operation or hospital admission [ 47 ].

Second research question: how do patients choose their preferred healthcare provider?

Patients’ decision-making processes.

Policy makers assume that patients selectively choose high-quality providers based on weighing up the information about the different providers: in other words, that they make a rational choice [ 87 ]. For patients to be able to choose as this assumes, they need complete information, unrestricted cognitive abilities, consistent preferences, willpower and the ability to foresee their needs [ 88 ]. However, several studies suggest that these conditions are rarely satisfied [ 88 – 90 ] and most patients are consequently unable to make a completely rational choice [ 38 , 63 , 88 , 91 – 93 ]. This results in choices based on only some of the provider characteristics and/or irrelevant factors such as their current mood [ 31 , 63 , 88 – 91 ] and often to no choice at all [ 88 , 93 , 94 ]. According to several studies, the degree to which patients are capable of processing the information rationally is influenced by their health literacy (the degree to which they have the capacity to obtain, process and understand the basic health information needed to make appropriate health decisions) and their numeracy (the ability to apply numbers as needed to manage your health) [ 60 , 92 , 95 – 97 ]. For example, low numeracy leads to people being influenced more often by factors that are irrelevant to the choice problem.

Furthermore, a patient’s activation level (i.e. the extent to which patients seek and use healthcare information and actively choose between providers) also influences patients’ choice processes, according to several studies. Some patients actively search for providers, while others rely on their GP for advice [ 42 , 62 , 64 , 76 , 86 , 98 ]. How active patients are depends on their characteristics [ 42 , 47 , 76 , 86 , 98 ]. For example, patients who do not have a strong tie or have an unsatisfactory tie to individual physicians [ 42 , 47 ] are more active consumers. Patients who make more active choices may make use of systematic reasoning using all available information or may make a more intuitive choice using only subsets of the information [ 31 , 90 , 92 ]. Low numeracy leads to less use of systematic reasoning [ 92 ]. However, only a few patients systematically process all information, according to Damman [ 31 ].

Use of information sources

Research shows that patients use various information sources in their decision-making processes. Comparative information is one example of an information source. Findings on whether patients see the relevance of comparative information are mixed (i.e. mutual inconsistency between the studies). One reason for patients finding this information irrelevant is that they expect a high standard everywhere and are unwilling to ‘shop around’ [ 16 , 49 ]. Often, patients who do find this information relevant eventually do not use it, which suggests that there is a difference between what patients say and what they actually do [ 16 , 31 , 64 ]. This difference is confirmed by research that directly compared revealed preferences against stated preferences [ 30 , 45 , 46 , 48 , 49 ]. Patients use more comparative information in future choices and in advice to others than they used in previous choices. Reasons for not using it are that they encounter barriers to its use, e.g. the short time frame in which to select a provider and geographical barriers [ 62 ], unavailability of the right information [ 31 , 74 , 76 , 84 , 90 , 99 ], distrust of the information [ 49 ], information overload [ 31 , 60 , 100 ] and an insufficiently clear presentation of the information [ 30 , 31 , 60 , 92 , 100 , 101 ]. So, although patients indicate that they find comparative information important, research suggests that relatively few patients make use of comparative information, are aware of its existence or understand it [ 16 , 31 , 48 , 62 , 64 , 102 ]. This applies in both Europe and the USA. Patients appear to use comparative information only in certain circumstances, such as when there is a single outcome of major importance and the data can be easily understood, or in the absence of a meaningful and trusting doctor-patient relationship [ 16 , 60 ]. Patients with low health literacy in particular find insufficiently clear presentation formats more of a problem [ 60 , 95 , 96 ]. Nevertheless, according to a few revealed preference studies from the USA, the release of comparative information does result in small changes in providers’ market shares [ 29 , 62 , 103 – 105 ]. However, this effect may be caused by factors other than patients who are actively choosing, for example GP referrals. Finally, research indicates that explicitly giving or making patients aware of comparative information [ 52 , 62 , 78 ] and improving the presentation format [ 63 , 92 , 95 , 97 , 100 , 106 ] increases its use.

Research shows that patients use other information sources more often than comparative information. A patient’s own previous care experience, for example, is the most important information source for many patients [ 42 , 45 , 62 , 107 , 108 ]. A positive experience with a particular provider positively influences the future choice for that provider [ 25 , 30 , 44 , 45 , 47 , 109 ]. Patients’ general care experiences also influence their choices. For example, two studies found that positive experience with female physicians positively influences patient preference for a female physician [ 72 , 110 ] and that patients who had bypassed their closest rural hospital once are more likely to bypass it again [ 111 ]. Social influence (e.g. a provider’s general reputation, the influence of someone’s referring physician or the recommendations of friends and acquaintances) is a third important information source [ 46 , 59 , 66 , 67 , 76 , 112 ]. However, different studies find different effects of this information source. Only the influence of a referring physician has a consistent strong positive effect.

Which of these information sources are used differs between patients [ 28 , 42 , 45 , 86 , 108 , 113 ]. For example, older [ 28 , 42 ] and less highly educated patients [ 113 ] are more likely to follow the advice of their physician. Older, less highly educated, less literate [ 60 , 84 , 92 , 106 ] patients and those already in the healthcare system [ 62 ] generally use less comparative information.

Third research question: which provider characteristics do patients base their choice of healthcare provider on?

Because the nature of this research question is suitable for quantitative analysis, we quantitatively analysed the studies that investigated the influence of provider characteristics on patients’ choices. In 101 studies, the influence of provider characteristics on patients’ choices was investigated. The structure-process-outcome model of quality care [ 21 ] is used in this review in order to summarize the characteristics influencing this theme. The factors studied most often are those related to structure (n = 86), followed by process (n = 60) and outcome (n = 43). Because of the relatively large amount of literature on structure, we have paid more attention to this factor. The importance that patients attach to the different factors differs between patients, depending on their socio-demographic (n = 44) and disease (n = 31) characteristics and their knowledge, attitudes and beliefs (n = 12). When we discuss the specific provider characteristics below, we will only go into detail about the influences that have been investigated relatively often. Given the large number of sources included in this review, for the sake of manageability we will cite no more than six at a time.

Seven factors can be distinguished for the structure aspect, namely the availability of providers, the accessibility of the providers, the type and size of the providers, the availability/experience/quality of the staff, the organization of healthcare, the cost of treatment and socio-demographic factors of the individual doctors.

Availability (n = 29): it was commonly reported that the availability of providers influences choice (n = 18). Some patients have only a few providers to choose from and for some patients the number of providers they can actually choose from is limited because of, for example, language difficulties [ 2 , 3 , 16 , 48 , 65 , 102 ]. Whether or not a given provider is available for patients depends on their insurance plan, especially for patients in the USA. If patients have to make co-payments or do without certain benefits when receiving care from a particular provider, they are less likely to choose that provider (n = 10) [ 40 , 53 , 69 , 73 , 86 , 108 ]. This incentivizing by insurers does not affect all patients’ decisions equally. Examples of observed effects are that being female [ 53 ] or having a lower income [ 73 , 109 ] positively affect, and that already having a provider [ 114 ] or being in poor health [ 73 ] negatively affect responsiveness to insurer incentivizing.

Accessibility (n = 55): the issue most discussed is distance or convenient location (n = 50). Generally, patients are averse to travel time and prefer a provider that is close by and not abroad (n = 44) [ 30 , 66 , 67 , 82 , 111 , 115 ]. Another important issue is that patients prefer a provider that is accessible by their own transport or public transport (n = 11) [ 28 , 30 , 38 , 64 , 112 , 116 ]. Other issues are parking (n = 4) [ 2 , 30 , 46 , 112 ] and transport that is organized or paid for (n = 4) [ 16 , 59 , 82 , 117 ]. Studies found a positive relationship between age and the importance of distance, easy access by transport and parking facilities (n = 12) [ 30 , 38 , 51 , 82 , 111 , 118 ]. Furthermore, being more highly educated (n = 8) [ 30 , 47 , 51 , 82 , 111 , 119 ] and being willing to travel (n = 3) [ 47 , 59 , 64 ] negatively influence the importance attached to distance. The specific disease influences the importance attached to distance (n = 6) [ 30 , 59 , 81 , 119 – 121 ], e.g. distance is more important for patients who need cataract surgery than for patients who need hip or knee surgery [ 119 ].

Type and size of the institution (n = 37): the issue most discussed was provider ownership/affiliation (n = 17). It was generally found that this aspect influences choice (n = 15) [ 44 , 65 , 74 , 120 – 122 ]. For example, research indicates that patients prefer an individual provider that is affiliated to an (academic) hospital [ 62 , 70 ]. Besides, American patients prefer private, non-profit providers over public and commercial ones [ 27 , 65 , 120 , 121 ], whereas patients from the UK prefer public hospitals [ 66 ]. However, findings are mixed on whether patients prefer a university medical centre [ 45 , 81 , 118 , 122 ]. Two studies found that patients prefer a university medical hospital [ 45 , 81 ], while two others found that they do not [ 118 , 122 ]. Two other important issues are the range and quality of facilities (n = 22) [ 30 , 61 , 74 , 111 , 120 , 121 ] and the provider size (n = 11) [ 27 , 30 , 75 , 111 , 121 , 122 ]. Patients generally prefer clean hospitals with complex, high-quality services. Findings on preferred provider size are mixed. For example, Bouche found that patients were more likely to choose low-volume hospitals [ 123 ], while the number of beds does not influence choice of hospital according to Roh [ 120 ]. Bornstein found that patients prefer GP practices with several doctors [ 75 ]. Comparison of the studies reviewed could not let us show why findings are mixed, as there are so many differences between them. Examples of differences are the kind of healthcare provider that studies focused on and the methods used to acquire patients’ preferences.

Staff (n = 35): a large number of studies found that the medical qualification/expertise of providers is an important determinant of choice (n = 27) [ 52 , 77 , 78 , 86 , 109 , 112 ]. Patients prefer providers with a quality certificate and qualified physicians. Furthermore, patients prefer experienced providers (n = 10) [ 30 , 33 , 43 , 52 , 70 , 113 ]. Yet other factors that patients prefer are that the provider’s specialization/interest fits their care needs (n = 6) [ 37 , 59 , 64 , 70 , 75 , 119 ] and the availability of sufficient staff per patient (n = 3) [ 62 , 113 , 124 ].

Organization of healthcare (n = 27): some of the factors that positively influence the preference for a provider are related to the organization of healthcare [ 45 , 53 , 59 , 61 , 75 , 98 ]:

whether you can be treated at a convenient time or place or by the doctor of choice (n = 15) [ 36 , 53 , 75 , 86 , 119 ];

actions to improve service quality and efficiency (n = 12) [ 76 , 83 , 113 , 115 , 125 , 126 ]. Aspects in this category are regularly inviting patients for checkups, making house calls, providing bulk billing services, having practice assistants available, spending enough time on personal care, and complaint handling;

whether a provider is accessible by phone and Internet (n = 5) [ 66 , 86 , 109 , 127 , 128 ].

Costs (n = 12): the evidence about the influence of cost on choice is mixed [ 26 , 28 , 69 , 75 , 86 , 113 ]. Differences may be caused by whether the care provided by a certain provider is insured or not, as the cost of treatment generally only influences choice when patients also have to make payments themselves. For example, Combier (2004) found that women do not take costs into account when choosing a maternity hospital because they do not have any out-of-pocket expenses [ 28 ], whereas research by Kiiskinen (2010) indicates that patients do take out-of-pocket costs into account when choosing a dentist [ 83 ].

Socio-demographic factors (n = 18): the two most extensively studied factors are gender (mostly whether the direct care provider has the same gender as the patient) (n = 16) and age (n = 7) of the provider [ 37 , 43 , 52 , 75 , 76 , 84 ]. It is generally found that a physician’s demographic parameters do influence choice, but that other factors are usually perceived to be more important [ 25 , 37 , 43 , 70 , 76 ]. This is confirmed by the finding that explicitly giving or making patients aware of comparative information reduces the influence that variables such as the age and gender of the individual providers have on choice [ 52 , 62 , 78 ]. The characteristics that patients attribute to women, such as positive social skills, positively influence their preferences for women [ 25 , 55 , 110 ].

Five factors can be distinguished for the process aspect, namely interpersonal factors, availability of information, continuity of treatment, waiting time and the quality of treatment.

Interpersonal factors (n = 40): the issue most discussed was the physician’s communication style (n = 36). Most studies found that this factor influences choice (n = 36) [ 45 , 62 , 66 , 78 , 92 , 115 ]. Generally, patients prefer a provider with a friendly and understanding communication style who listens to the patient and with whom the patient has a good relationship or feels a personal click. Other factors that are found to influence choice positively are whether the patient is involved in decision making about care (n = 12) [ 26 , 34 , 37 , 62 , 76 , 99 ] and a friendly provider atmosphere (n = 7) [ 30 , 32 , 33 , 46 , 62 , 76 ]. Age positively influences the importance attached to interpersonal characteristics according to several studies (n = 6) [ 26 , 30 , 34 , 76 , 119 , 126 ], while education negatively influences the importance of interpersonal characteristics (n = 6) [ 26 , 30 , 33 , 34 , 76 , 126 ]. Research into the influence of disease characteristics shows that patients with more complex or severe diseases attach more importance to interpersonal characteristics [ 26 , 50 , 113 , 129 ] and that the specific disease influences the importance the patient attaches to interpersonal characteristics [ 30 , 80 , 98 , 129 ].

Information provision (n = 10): most studies found that whether and how information is provided is a determinant of choice (n = 7) [ 30 , 36 , 59 , 61 , 99 , 119 ]. Continuously giving relevant information during and before treatment has a positive influence on choice.

Continuity (n = 10): being able to keep seeing the same doctor has a positive influence on the choice of provider [ 26 , 34 , 36 , 99 , 116 , 127 ].

Waiting time (n = 30): most studies found a negative influence of the time spent on waiting lists and time in the waiting room (n = 27) [ 26 , 30 , 35 , 46 , 59 , 130 ]. However, the specific disease influences the importance a patient attaches to waiting time (n = 4) [ 30 , 33 , 80 , 119 ].

Quality of treatment (n = 12): this factor has to do with the quality of the medical treatment (n = 8). All studies found at least some positive influence of this factor on choice [ 26 , 30 , 41 , 61 , 99 , 119 ]. Examples are whether medical treatment is high quality and whether care is delivered as agreed, the number of cancelled operations and whether patients have a clear care plan. Additionally, three studies show that the rules or activities implemented in order to deliver good care are an important issue, e.g. the clinical standards used, whether care is interdisciplinary, and the protocols and procedures a provider has implemented [ 45 , 61 , 66 ].

Although many studies (n = 30) found that outcome indicators such as mortality or pressure sore rates had a strong or moderate influence on choice [ 18 , 27 , 50 , 64 , 98 , 102 ], about half that number (n = 15) found that the influence was weak or that there was no influence at all [ 16 , 46 , 48 , 54 , 64 , 102 ]. Generally, other characteristics are found to be more important than outcome, such as GP referral and distance [ 16 , 30 , 41 , 46 , 64 , 67 ]. Differences in the importance attached to outcome indicators are partly explained by the differences between the characteristics that patients say are important and the ones they act upon in a real choice situation. These differences have often been uncovered by research that directly compared revealed preferences against stated preferences [ 62 ]. For example, patients indicate that they are willing to use more quality information items, including outcome indicators, in future choices than they actually used in previous choices [ 30 , 46 , 48 , 49 ]. Additionally, outcome indicators influence the advice they would give to friends, whereas they did not have a strong influence on their own previous choices [ 45 , 49 ]. It is however difficult to indicate whether this phenomenon accounts for all the inconsistencies in the findings between the studies reviewed, as there are also many other differences between them. Several studies (n = 10) found a positive relationship between the level of education and the importance attached to outcome characteristics [ 28 , 33 , 67 , 113 , 119 , 124 ]. Patients with more complex or severe diseases attach less importance to outcome characteristics (n = 2) [ 29 , 113 ] and the specific disease influences the importance that the patient attaches to outcome characteristics (n = 7) [ 30 , 33 , 45 , 46 , 98 , 119 ].

Choice of a healthcare provider does not seem to be as straightforward a process as is sometimes assumed in health policy, i.e. that patients look for high-quality care while minimizing cost and ‘vote with their feet’ by choosing the provider that best fits their needs and preferences [ 2 , 11 , 13 , 18 , 131 , 132 ]. As this review shows, whether and how patients choose a provider and their eventual choices are determined by the interplay between patient and provider characteristics. This review has answered three questions.

The first research question concerns whether patients actively choose their healthcare providers. Research indicates that patients do not generally choose actively [ 47 , 49 ]. Reasons are that a substantial proportion of patients do not find choice very important [ 16 , 64 , 84 , 85 ], that the degree of choice for some patients is limited [ 2 , 16 , 47 , 85 , 86 ] and that the available information is not enough or unsuitable to base decisions on [ 30 , 31 , 60 , 92 , 100 , 101 ]. Especially because of the last two factors mentioned, there is a difference between the characteristics that patients state as being important and the characteristics they act upon in a real choice situation. The second research question is about how patients choose. Policy makers assume that patients, as they aim for high-quality care while minimizing costs, will actively choose the best provider. However, research shows that most patients are unable and/or unwilling to make a completely rational choice. This is supported both by research in healthcare (e.g. health plans, treatments, and health-related behaviour) and in other areas (e.g. personal finance, which school to attend) [ 133 – 137 ]. Instead, choices are based on only some of the provider characteristics [ 31 , 63 , 88 – 91 ] and patients choose a provider that is good enough, or make no active choice at all [ 88 , 93 , 94 ]. Furthermore, their degree of activation [ 42 , 62 , 64 , 76 , 86 , 98 ], the information sources they use and how systematically they compare the information about the characteristics of the various providers also differ [ 31 ]. Apparently, most patients do not look for the highest quality, as only a few go systematically through all the comparative information [ 31 ]. Instead, they only take information into account that confirms their expectations, they often stay with their current provider [ 25 , 90 ] and they rely on others’ experiences [ 108 ] or their GP’s advice [ 98 , 117 ]. Finally, in the investigations for the third research question, namely the provider characteristics that patients base their choices on, it transpires that patients base their choices on a variety of structural, process and outcome quality indicators. In fact, structure and – in particular – process indicators are more important than outcome indicators [ 50 , 80 ]. The importance attached to the different characteristics differs between the various patient groups.

Because the USA has a longer history than countries in Europe [ 64 ] of competition in various areas and of publishing information on the quality of care among different providers, it might be expected that American patients would make more active choices for high-quality providers. However, in practice, the choices made by both European and American patients are determined by a complex interplay between a variety of patient and provider characteristics and different patients make different choices - generally passive ones - in different situations. Nevertheless, differences between the choice processes and choices of American and European patients do exist, often resulting from the distinct healthcare systems of the two continents. For example, in the USA, insurers traditionally have an important role as prudent buyers of care on behalf of their members and research suggests that they partly determine the specific providers that are available to patients [ 86 ].

Differences between studies

Scoping reviews analyse studies that use a range of data collection techniques. Different techniques may lead to different results. For example, it is to be expected that results from stated preference research differ from those from revealed preference studies. For outcome indicators, for example, most studies investigating hypothetical choices found that outcome indicators influence patients’ choices. However, most studies investigating real choices found that outcome indicators have a limited influence on patients’ choices. This difference is confirmed by research that directly compared revealed preferences against stated preferences [ 30 , 45 , 46 , 48 , 49 ]. Exceptions are results from studies analysing patient registration data. Most studies found that more patients are admitted to providers that perform better (on outcome indicators) and fewer to providers performing less well. However, this effect may be caused by factors other than patients choosing actively, for example by GP referrals.

It is also to be expected that the characteristics patients consider to be important will differ for individual providers and institutions. Fung (2008), for example, found that public reporting of performance data did not affect selection of hospitals, while it did affect selection of individual providers [ 105 ]. Interpersonal indicators are also found to influence choice of an individual provider more often than choice of an institution. These differences can, however, partly be explained by the research methods used in the specific studies. Studies investigating the choice of individual providers study the importance of interpersonal indicators more often. For example, Newton (2007) found that patients focus on interpersonal factors when choosing a GP but not when choosing a medical clinic facility. Patients’ perceived importance of interpersonal indicators was, however, not investigated when choosing a medical clinic facility [ 115 ]. This underlines the difficulty of indicating the exact causes of the differences found between the studies under review, as there are numerous differences in their data collection and analysis techniques.

Knowledge gaps

We identified several knowledge gaps. Firstly, despite the fact that there is an increasing amount of literature from behavioural economics and psychology, the behavioural economics of provider choice have received relatively little attention compared to the literature, which assumes that patients choose their providers more or less rationally. Although policy makers assume that patients’ information processing proceeds rationally, the results of several studies suggest that patients are often not capable of making rational choices [ 136 ]. This also indicates the relevance of the context in which the relationships occur that were found by the studies. Many studies do not explicitly address the issue that their findings may depend on the specific decision-making context, e.g. that they focus on a hospital or GP, that they asked for patients’ preferences or the attributes they based their decision on, whether patients were ill or not, etcetera. We recommend that researchers should specify the influence of the research context on the research findings and explain any discrepancies between their findings and the findings of other studies, given the differences in context. A final gap in the current state of knowledge is that relatively few studies analysed choice in a real choice situation, instead using an experimental design. More research should be conducted into the provider characteristics that patients take into account in real choice situations, especially because preferences are not static but depend on the decision context. As this review shows, there is a difference between the factors that patients say they find important and the ones they actually base their decisions on. However, we are aware of the difficulty of setting up such a study.

Strengths, limitations and follow-up research

A strong point of this review is that it has a broad scope and attempts to draw a picture of how patients choose healthcare providers and what determines their choice. We have tried to point out the factors that are important determinants of patient choice according to the existing literature, without making selections or excluding any studies because of their lower quality. Additionally, the search and inclusion process, which included developing a search strategy in consultation with a librarian and literature review expert and having two reviewers for a proportion of the entire source texts, is a strong point.

One limitation of this review is that its scope may not be broad enough because only scientific papers were included. Additionally, because of our narrow search string, we may have missed some relevant papers on the subject. However, the papers that we read in a later stage of the review did not add any significant new insights. Furthermore, the range of data collection and analysis techniques used in the studies under review makes them hard to compare and makes the mixed results hard to interpret. The results of any particular reviewed study may have been influenced by the exact kind of provider and provider characteristic studied and the method used for obtaining the data. For example, Groenewoud (2008) found that GP recommendations do not influence choices much, whereas Plunkett (2002) found that they do. The latter analysed real choice situations and the former asked for patients’ preferences regarding certain provider characteristics [ 32 , 70 ]. However, other aspects also differed between the two studies, so we could not clarify this mixed result.

A related issue is that a scoping review cannot present absolute truths, because no exhaustive search has been done and we did not conduct a quality assessment of reviewed sources. The results should therefore be interpreted with some caution. Nevertheless, due to the large number of studies included, we believe that the current review provides a thorough survey of the available literature on the factors that influence patient choice and the range of research conducted into the subject.

Patients’ choices are determined by a complex interplay between a variety of patient and provider characteristics. There is no such thing as the typical patient: different patients make different choices in different situations. Patients often attach greater importance to their own previous healthcare experiences or to GP recommendations than to comparative information. Additionally, patients base their decisions not only on outcome indicators but on a variety of provider characteristics. It can thus be argued that the choice process is much more complex than is often assumed. This is true for both Europe and the USA. Most patients are unable and/or unwilling to make a completely rational choice [ 134 – 137 ]. A number of gaps in current knowledge were identified.

Ranerup A, Noren L, Sparud-Lundin C: Decision support systems for choosing a primary health care provider in Sweden. Patient Educ Couns. 2011, 86: 342-347.

PubMed   Google Scholar  

Dixon A, Robertson R, Bal R: The experience of implementing choice at point of referral: a comparison of the Netherlands and England. Health Econ Policy Law. 2010, 5: 295-317. 10.1017/S1744133110000058.

Grytten J, Sorensen RJ: Patient choice and access to primary physician services in Norway. Health Economics, Policy and Law. 2009, 4: 11-27. 10.1017/S1744133108004623.

Google Scholar  

Vrangbaek K, Robertson R, Winblad U, Van de Bovenkamp H, Dixon A: Choice policies in Northern European health systems. Health Econ Policy Law. 2012, 7: 47-71. 10.1017/S1744133111000302.

Victoor A, Friele R, Delnoij D, Rademakers J: Free choice of healthcare providers in the Netherlands is both a goal in itself and a precondition: modelling the policy assumptions underlying the promotion of patient choice through documentary analysis and interviews. BMC Health Serv Res. in press

Parente ST, Feldman R, Christianson JB: Evaluation of the effect of a consumer-driven health plan on medical care expenditures and utilization. BMC Health Serv Res. 2004, 39: 1189-1210.

Buntin MB, Damberg C, Haviland A, Kapur K, Lurie N, McDevitt R, et al: Consumer-directed health care: early evidence about effects on cost and quality. Health Aff. 2006, 25: 530.

Hirschman AO: Exit, Voice, and Loyalty: Responses to Decline in Firms, Organizations, and States. 1970, Cambridge, MA: Harvard University Press

Burge P, Devlin N, Appleby J, Gallo F, Nason E, Ling T: Understanding Patients' Choices at the Point of Referral. 2006, Cambridge: Rand Europe

Ministerie van VWS: Beleidsagenda 2005. 2005, Den Haag: ministerie van VWS

NZa: het belang van de consument: Het consumentenprogramma van de NZa. 2007, Utrecht: NZa

Tweede Kamer: 30186 nr. 8. 2005, Den Haag: Tweede Kamer

Tweede Kamer: 30186 nr. 3. 2005, Den Haag: Tweede Kamer

Tweede Kamer: 30186 nr. 2. 2005, Den Haag: Tweede Kamer

Dixon A, Robertson R, Appleby J, Burge P, Devlin N, Magee H: Patient choice: how patients choose and how providers respond. 2010, London: The King's Fund

Fotaki M, Roland M, Boyd A, McDonald R, Scheaff R, Smith L: What benefits will choice bring to patients? Literature review and assessment of implications. J Health Serv Res Policy. 2008, 13: 178-184. 10.1258/jhsrp.2008.007163.

Duggal A: Policy summary: NHS and Public Health Outcomes Frameworks. http://ukpolicymatters.thelancet.com/?p%20=%20895 .

Fung C, Elliott M, Hays R, Kahn K, Kanouse D, McGlynn E, et al: Patients' preferences for technical versus interpersonal quality when selecting a primary care physician. Health Serv Res. 2005, 40: 957-977. 10.1111/j.1475-6773.2005.00395.x.

PubMed   PubMed Central   Google Scholar  

Colsen P, Casparie A: Indicatorregistratie. Een model ten behoeve van integrale kwaliteitszorg in een ziekenhuis. Medisch Contact. 1995, 50: 297-299.

Claessen SJJ, Francke AL, Brandt HE, Pasman HRW, Van der Putten MJA, Deliens L: Ontwikkeling en toetsing van een set kwaliteitsindicatoren voor de palliative zorg. Nederlands Tijdschrift voor Palliatieve Zorg. 2010, 10: 3-10.

Donabedian A: Evaluating the quality of medical care, 1966. Milbank Q. 2005, 83: 691-729. 10.1111/j.1468-0009.2005.00397.x.

Arksey H, O'Malley L: Scoping studies: towards a methodological framework. InternationalJournal of Social Research Methodology. 2005, 8: 19-32. 10.1080/1364557032000119616.

Crooks V, Kingsbury P, Snyder J, Johnston R: What is known about the patient's experience of medical tourism? A scoping review. BMC Health Serv Res. 2010, 10: 266-10.1186/1472-6963-10-266.

Burnett A, Fassil J: Meeting health needs of refugee and asylum seekers in the UK. 2000, London: Directorate of Health and Social Care Department of Health

Chandler P, Chandler C, Dabbs M: Provider gender preference in obstetrics and gynecology: a military population. Mil Med. 2000, 165: 938-940.

CAS   PubMed   Google Scholar  

Cheraghi-Sohi S, Hole A, Mead N, McDonald R, Whalley D, Bower P, et al: What patients want from primary care consultations: a discrete choice experiment to identify patients' priorities. Annals of Family Medicine. 2008, 6: 107-115. 10.1370/afm.816.

Chernew M, Scanlon D, Haywerd R: Insurance type and choice of hospital for Coronary artery bypass graft surgery. Health Serv Res. 1998, 33: 447-466.

CAS   PubMed   PubMed Central   Google Scholar  

Combier E, Zeitlin J, de Courcel N, Vasseur S, Lalouf A, Amat-Roze J, et al: Choosing where to deliver: decision criteria among women with low-risk pregnancies in France. Soc Sci Med. 2004, 58: 2279-2289. 10.1016/j.socscimed.2003.08.015.

Cutler D, Huckman R, Landrum M: The role of information in medical markets: an analysis of publicly reported outcomes in cardiac surgery. Am Econ Rev. 2004, 94: 342-346. 10.1257/0002828041301993.

Dijs-Elsinga J, Otten W, Versluijs M, Smeets H, Kievit J, Vree R, et al: Choosing a hospital for surgery: the importance of information on quality of care. Med Decis Making. 2010, 30: 544-10.1177/0272989X09357474.

Damman O, Hendriks M, Rademakers J, Delnoij D, Groenewegen P: How do health care consumers process and evaluate comparative health care information? A qualitative study using cognitive interviews. BMC Public Health. 2009, 9: 423-10.1186/1471-2458-9-423.

Groenewoud AS: "Quot Capita, tot Sensus?" An Investigation of the Choice Processes of Patients seeking for a Health Care Provider, using Q-Methodology . In It's your Choice! A study of search and selection processes, and the use of performance indicators in different patient groups. 2008, Rotterdam: Erasmus Universiteit Rotterdam, 187-PhD thesis.

Marang-Van-De-Mheen P, Dijs-Elsinga J, Otten W, Versluijs M, Smeets H, Vree R, et al: The relative importance of quality of care information when choosing a hospital for surgical treatment: a hospital choice experiment. Med Decis Making. 2010, Epub ahead of print

Morrison M, Murphy T, Nalder C: Consumer preference for general practitioner services. Health Mark Q. 2003, 20: 2-19.

Ryan M, McIntosh E, Dean T, Old P: Trade-offs between location and Isle of Wight. J Public Health (Oxf). 2000, 22: 202-210. 10.1093/pubmed/22.2.202.

CAS   Google Scholar  

Albada A, Triemstra M: Patients' priorities for ambulatory hospital care centres. A survey and discrete choice experiment among elderly and chronically ill patients of a Dutch hospital. Health Expect. 2009, 12: 92-105. 10.1111/j.1369-7625.2009.00533.x.

Bernard ME, Sadikman JC, Sadikman CL: Factors influencing patients' choice of primary medical doctors. Minn med. 2006, 89: 46-50.

Haynes R, Lovett A, Sünnenberg G: Potential accessibility, travel time and consumer choice: geographical variations in general medical practice registrations in Eastern England. Environment and Planning A. 2003, 35: 1733-1750. 10.1068/a35165.

Nguyen L, Häkkinen U: Choices and utilization in dental care: public vs private dental sectors, and the impact of a two-channel financed health care system. Health Econ. 2006, 7: 99-106. 10.1007/s10198-006-0344-3.

Scanlon D, Lindrooth R, Christianson J: Steering patients to safer hospitals? The effect of a Tiered Hospital Network on hospital admissions. Health Serv Res. 2008, 43: 1849-1868. 10.1111/j.1475-6773.2008.00889.x.

De Groot I, Otten W, Smeets H, Marang-van de Mheen P: Is the impact of hospital performance data greater in patients who have compared hospitals?. BMC Health Serv Res. 2011, 11: 214-224. 10.1186/1472-6963-11-214.

Harris KM: How do patients choose physicians? Evidence from a national survey of enrollees in employment-related health plans. BMC Health serv res. 2003, 38: 711-732.

Johnson A, Schnatz P, Kelsey A, Ohannessian C: Do women prefer care from female or male obstetrician-gynecologists? A study of patient gender preference. BMC Med Educ. 2005, 105: 369-379.

Laamanen R, Simonsen-Rehn N, Suominen S, Brommels M: Does patients' choice of health centre doctor depend on the organization? A comparative study of four municipalities with different forms of service provision in Finland. Scand J Public Health. 2010, 38: 715-723. 10.1177/1403494810379169.

Lux MP, Fasching PA, Schrauder M, Lohberg C, Thiel F, Bani MR, et al: The era of centers: the influence of establishing specialized centers on patients' choice of hospital. Arch Gynecol Obstet. 2011, 283: 559-568. 10.1007/s00404-010-1398-0.

Marang-Van-De Mheen PJ, Dijs-Elsinga J, Otten W, Versluijs M, Smeets HJ, Van der Made WJ, et al: The importance of experienced adverse outcomes on patients' future choice of a hospital for surgery. Qual Saf Health Care. 2010, 19: 1-6.

PubMed Central   Google Scholar  

Robertson R, Burge P: The impact of patient choice of provider on equity: analysis of a patient survey. J Health Serv Res Policy. 2011, 16: 22-28. 10.1258/jhsrp.2010.010084.

Schneider EC, Epstein AM: Use of public performance reports: a survey of patients undergoing cardiac surgery. JAMA. 1998, 279: 1638-1642. 10.1001/jama.279.20.1638.

Schwartz L, Woloshin S, Birkmeyer J: How do elderly patients decide where to go for major surgery? Telephone interview survey. BMJ. 2005, 331: 821-827. 10.1136/bmj.38614.449016.DE.

Van Empel I, Dancet E, Koolman X, Nelen W, Stolk E, Sermeus W, et al: Physicians underestimate the importance of patient-centredness to patients: a discrete choice experiment in fertility care. Hum Reprod. 2011, 26: 584-593. 10.1093/humrep/deq389.

Finlayson S, Birkmeyer J, Tosteson A, Nease R: Patient preferences for location of care, implications for regionalization. Med Care. 1999, 37: 204-209. 10.1097/00005650-199902000-00010.

Guile M, Schnatz P, O'Sullivan D: Relative importance of gender in patients' selection of obstetrics and gynecology provider. Conn Med. 2007, 71: 325-332.

Boonen LHHM, Schut F, Koolman X: Consumer channeling by health insurers: natural experiments with preferred providers in the Dutch pharmacy market. Consumer channeling in health care: (im)possible? Consumentensturing in de zorg: (on)mogelijk?. 2009, Rotterdam: Erasmus Universiteit Rotterdam, 37-64.

Groenewoud AS: Patients suffering from Long Lasting Diseases; a Review of the Evidence on Revealed Decisions and Choices. It's your Choice! A study of search and selection processes, and the use of performance indicators in different patient groups. 2008, Rotterdam: Erasmus Universiteit Rotterdam, 73-138.

Kerssens J, Bensing J, Andela M: Patient preference for genders of health professionals. Soc Sci Med. 1997, 44: 1531-1540. 10.1016/S0277-9536(96)00272-9.

Dawson D, Jacobs R, Martin S, Smith P: Is patient choice an effective mechanism to reduce waiting times?. Applied Health Econ Health Policy. 2004, 3: 195-203. 10.2165/00148365-200403040-00003.

Dawson D, Gravelle H, Jacobs R, Martin S, Smith PC: The effects of expanding patient choice of provider on waiting times: evidence from a policy experiment. Health Econ. 2007, 16: 113-128. 10.1002/hec.1146.

Siciliani L, Martin S: An empirical analysis of the impact of choice on waiting times. Health Econ. 2007, 16: 763-779. 10.1002/hec.1205.

Exworthy M, Peckham S: Access, choice and travel: implications for health policy. Social Policy & Administration. 2010, 40: 267-287.

Faber M, Bosch M, Wollersheim H, Leatherman S, Grol R: Public reporting in health care: how do consumers use quality-of-care information? A systematic review. Med care. 2009, 47: 1-8. 10.1097/MLR.0b013e3181808bb5.

Groenewoud AS: Building quality report cards for geriatric care in the Netherlands: using concept mapping to identify the appropriate 'Building blocks' from the consumer's perspective. It's your Choice! A study of search and selection processes, and the use of performance indicators in different patient groups. 2008, Rotterdam: Erasmus Universiteit Rotterdam, 245-270.

Kolstad JT, Chernew ME: Quality and consumer decision making in the market for health insurance and health care services. Med Care Res Rev. 2009, 66: 28S-52S. 10.1177/1077558708325887.

Lubalin JS, Harris-Kojetin LD: What do consumers want and need to know in making health care choices?. Med Care Res Rev. 1999, 56 (Suppl 1): 67-12.

Magee H, Davis LJ, Coulter A: Public views on healthcare performance indicators and patient choice. J R Soc Med. 2003, 96: 338-342. 10.1258/jrsm.96.7.338.

Hirth RA, Banaszak-Holl JC, Fries BE, Turenne MN: Does quality influence consumer choice of nursing homes? Evidence from nursing home to nursing home transfers. Inquiry. 2003, 40: 343-361. 10.5034/inquiryjrnl_40.4.343.

Orr D, Sidiki SS, McGhee CN: Factors that influence patient choice of an excimer laser treatment center. J Cataract Refract Surg. 1998, 24: 335-340.

Merle V, Germain JM, Tavolacci MP, Brocard C, Chefson C, Cyvoct C, et al: Influence of infection control report cards on patients' choice of hospital: pilot survey. J Hosp Infect. 2009, 71: 263-268. 10.1016/j.jhin.2008.11.025.

Arora R, Singer J, Arora A: Influence of key variables on the patients' choice of a physician. Qual Manag Health Care. 2004, 13: 166-173. 10.1136/qshc.2004.010504.

Cooper PF, Nichols LM, Taylor AK: Patient choice of physician: do health insurance and physician characteristics matter?. Inquiry. 1996, 33: 237-246.

Plunkett B, Kohli P, Milad M: The importance of physician gender in the selection of an obstetrician or a gynecologist. Am J Obstet Gynecol. 2002, 186: 926-928. 10.1067/mob.2002.123401.

Varadarajulu S, Petruff C, Ramsey W: Patient preferences for gender of endoscopists. Gastrointest Endosc. 2002, 56: 170-173.

Zuckerman M, Navizedeh N, Feldman J, Mc-Calla S, Minkoff H: Determinants of women's choice of obstetrician/gynecologist. J Womens Health (Larchmt). 2002, 11: 175-180.

Rosenthal M, Li Z, Milstein A: Do patients continue to see physicians who are removed from a PPO network?. Am J Manag Care. 2009, 15: 713-719.

Geraedts M, Schwartze D, Molzahn T: Hospital quality reports in Germany: patient and physician opinion on the reported quality indicators. BMC Health Serv Res. 2007, 7: 157-162. 10.1186/1472-6963-7-157.

Bornstein BH, Marcus D, Cassidy W: Choosing a doctor: an exploratory study of factors influencing patients' choice of a primary care doctor. J Eval Clin Pract. 2000, 6: 255-262.

McGlone TA, Butler ES, McGlone VL: Factors influencing consumers' selection of a primary care physician. Health Mark Q. 2002, 19: 21-37.

Howell E, Gardiner B, Concato J: Do women prefer female obstetricians?. Obstetrics & Gynecology. 2002, 99: 1031-1035. 10.1016/S0029-7844(02)01980-4.

Schnatz PF, Murphy JL, O'Sullivan DM, Sorosky JI: Patient choice: comparing criteria for selecting an obstetrician-gynecologist based on image, gender, and professional attributes. Am J Obstet Gynecol. 2007, 197: 548-154.

Lako CJ, Rosenau P: Demand-driven care and hospital choice. Dutch health policy toward demand-driven care: results from a survey into hospital choice. Health Care Anal. 2009, 17: 20-35. 10.1007/s10728-008-0093-9.

Rademakers J, Delnoij D, de Boer D: Structure, process or outcome: which contributes most to patiens' overall assessment of healthcare quality?. BMJ Quality and Safety. 2011, 20: 326-331. 10.1136/bmjqs.2010.042358.

Varkevisser M, van der Geest S, Schut F: Quality competition in regulated hospital markets: consumer information and patient choice for angioplasty. Patient choice, competition and antitrust enforcement in Dutch hospital markets. 2009, Rotterdam: Erasmus Universiteit Rotterdam, 117-149.

Burge P, Devlin N, Appleby J, Rohr C, Grant J: Do patients always prefer quicker treatment? A discrete choice analysis of patients' stated preferences in the London Patient Choice Project. Appl Health Econ Health Policy. 2004, 3: 183-194. 10.2165/00148365-200403040-00002.

Kiiskinen U, Suominen-Taipale AL, Cairns J: Think twice before you book? Modelling the choice of public vs private dentist in a choice experiment. Health Econ. 2010, 19: 670-682. 10.1002/hec.1504.

Anell A, Rosén P, Hjortsberg C: Choice and participation in health services: a survey of preference among Swedish residents. Health Policy. 1997, 40: 157-168. 10.1016/S0168-8510(96)00891-3.

Lambrew JM: "Choice" in health care: what do people really want?. Issue Brief (Commonw Fund). 2005, 853: 1-12.

Hoerger T, Howard L: Search behavior and choice of physician in the market for prenatal care. Med Care. 1995, 33: 332-349. 10.1097/00005650-199504000-00002.

Robertson R, Dixon A: Choice at the point of referral: early results of a patient survey. 2009, London: The king's fund

Kooreman P, Prast H: What does behavioral economics mean for policy? Challenges to savings and health policies in the Netherlands. The Economist. 2010, 158: 101-122. 10.1007/s10645-010-9141-6.

Hibbard J, Slovic P, Jewett J: Informing consumer decisions in health care: implications from decision making research. Milbank Q. 1997, 75: 395-414. 10.1111/1468-0009.00061.

Moser A, Korstjens I, van der Weijden T, Tange H: Themes affecting health-care consumers' choice of a hospital for elective surgery when receiving web-based comparative consumer information. Patient Educ Couns. 2010, 78: 365-371. 10.1016/j.pec.2009.10.027.

Foster MM, Earl PE, Haines TP, Mitchell GK: Unravelling the concept of consumer preference: implications for health policy and optimal planning in primary care. Health Policy. 2010, 97: 105-112. 10.1016/j.healthpol.2010.04.005.

Fasolo B, Reutskaja E, Dixon A, Boyce T: Helping patients choose: How to improve the design of comparative scorecards of hospital quality. Patient Educ Couns. 2010, 78: 344-349. 10.1016/j.pec.2010.01.009.

Hibbard JH, Peters E: Supporting informed consumer health care decisions: data presentation approaches that facilitate the use of information in choice. Annu Rev Public health. 2003, 24: 413-433. 10.1146/annurev.publhealth.24.100901.141005.

Redelmeier D, Shafir E: Medical decision making in situations that offer multiple alternatives. JAMA. 1995, 273: 302-305. 10.1001/jama.1995.03520280048038.

Peters E, Dieckmann NF, Västfjäll D, Mertz C, Slovic P, Hibbard J: Bringing meaning to numbers: the impact of evaluative categories on decisions. J Exp Psychol Appl. 2009, 15: 213-227.

Peters E, Dieckmann N, Dixon A, Hibbard J, Mertz C: Less is more in presenting quality information to consumers. Med Care Res Rev. 2007, 64: 169-190. 10.1177/10775587070640020301.

Reyna V, Nelson W, Han P, Dieckmann N: How numeracy influences risk comprehension and medical decision making. Psychol Bull. 2009, 135: 943-973.

Groenewoud AS: Patients' decision making processes in the search for and selection of their Health care provider: findings from a grounded theory study. It's your Choice! A study of search and selection processes, and the use of performance indicators in different patient groups. 2008, Rotterdam: Erasmus Universiteit Rotterdam, 139-186-PhD thesis

Groenewoud AS: Performance indicators for the choosing heath care consumer?. It's your Choice! A study of search and selection processes, and the use of performance indicators in different patient groups. 2008, Rotterdam: Erasmus Universiteit Rotterdam, 41-72.

Hibbard J, Greene J, Daniel D: What is quality anyway? Performance reports that clearly communicate to consumers the meaning of quality of care. Med Care Res Rev. 2010, 67: 275-293. 10.1177/1077558709356300.

Schauffler H, Mordavsky J: Consumer reports in health care: do they make a difference?. Annu Rev Public Health. 2001, 22: 69-89. 10.1146/annurev.publhealth.22.1.69.

Mukamel DB, Mushlin AI: The impact of quality report cards on choice of physicians, hospitals, and HMOs: a midcourse evaluation. The Joint Commission journal on quality improvement. 2001, 27: 20-27.

Bundorf M, Chun N, Goda G, Kessler D: Do markets respond to quality information? The case of fertility clinics. J Health Econ. 2009, 28: 718-727. 10.1016/j.jhealeco.2009.01.001.

Mukamel D, Mushlin A: Quality of care information makes a difference: an analysis of market share and price changes after publication of the New York state cardiac surgery mortality reports. Med Care. 1998, 36: 945-954. 10.1097/00005650-199807000-00002.

Fung CH, Lim Y, Mattke S, Damberg C, Shekelle PG: Systematic review: the evidence that publishing patient care performance date improves quality of care. Ann Intern Med. 2008, 148: 111-123.

Damman O, Hendriks M, Rademakers J, Spreeuwenberg P, Delnoij D, Groenewegen P: Consumers' interpretation and use of comparative information on the quality of healthcare: the effect of presentation approaches. Public reporting about healhtcare users' experiences. 2010, Utrecht: NIVEL, 109-128.

Gooding SK: The relative importance of information sources in consumers' choice of hospitals. Journal of ambulatory care marketing. 1995, 6: 99-108.

Sinaiko AD: How do quality information and cost affect patient choice of provider in a tiered network setting? Results from a survey. BMC Health Serv Res. 2011, 46: 437-456.

Boonen LHHM, Schut F, Donkers B, Koolman X: Which preferred providers are really preferred? Effectiveness of insurers' channeling incentives on pharmacy choice. Consumer channeling in health care: (im)possible? Consumentensturing in de zorg: (on)mogelijk?. 2009, Rotterdam: Erasmus Universiteit Rotterdam, 65-90.

Ahmad F, Gupta H, Rawlins J, Stewart D: Preferences for gender of family physician among Canadian European-descent and South-Asian immigrant women. BMC Fam Pract. 2002, 19: 146-153.

Tai W, Porell F, Adams E: Hospital choice of rural Medicare beneficiaries: patient, hospital attributes, and the patient-physician relationship. BMC Health Serv Res. 2004, 39: 1903-1922.

Shah J, Dickinson CL: Establishing which factors patients value when selecting urology outpatient care. British Journal of Medical and Surgical Urology. 2010, 3: 25-29. 10.1016/j.bjmsu.2009.10.003.

Groenewoud AS: What influences patients' decisions when choosing a health care provider? Measuring preferences of patients with Knee arthrosis, Chronic depression or Alzheimer's disease, using discrete choice experiments. It's your Choice! A study of search and selection processes, and the use of performance indicators in different patient groups. 2008, Rotterdam: Erasmus Universiteit Rotterdam, 217-244. PhD thesis

Boonen LHHM, Schut F, Donkers B: Consumer willingness to switch to preferred providers: are preferences stronger for GPs than for pharmacies?. Consumer channeling in health care: (im)possible? Consumentensturing in de zorg: (on)mogelijk?. 2009, Rotterdam: Erasmus Universiteit Rotterdam, 117-146.

Newton FJ, Ewing MT, Burney S, Vella-Brodrick D: Medical clinic facilities and doctor characteristics: what older rural men value. Aust J Rural Health. 2007, 15: 41-45. 10.1111/j.1440-1584.2007.00848.x.

Safran D, Montgomery J, Chang H, Murphy J, Rogers W: Switching doctors: predictors of voluntary disenrollment from a primary physician's practice. J Fam Pract. 2001, 50: 130-136.

Dealey C: The factors that influence patients' choice of hospital and treatment. Br J Nurs. 2005, 14: 576-579.

Varkevisser M, van der Geest S: Why do patients bypass the nearest hospital? An empirical analysis for orthopaedic care and neurosurgery in the Netherlands. Eur J Health Econ. 2007, 8: 287-295. 10.1007/s10198-006-0035-0.

Damman OC, Spreeuwenberg P, Rademakers J, Hendriks M: Creating compact comparative health care information: what are the key quality attributes to present for cataract and total hip or knee replacement surgery?. Med Decis Making. 2011, 32: 287-300.

Roh CY, Lee KH, Fottler MD: Determinants of hospital choice of rural hospital patients: the impact of networks, service scopes, and market competition. J Med Syst. 2008, 32: 343-353. 10.1007/s10916-008-9139-7.

Roh C, Moon M: Nearby, but not wanted? the bypassing of rural hospitals and policy implications for rural health care systems. The Policy Studies Journal. 2005, 33: 377-394. 10.1111/j.1541-0072.2005.00121.x.

Varkevisser M, van der Geest SA, Schut FT: Assessing hospital competition when prices don't matter to patients: the use of time-elasticities. Int J Health Care Finance Econ. 2010, 10: 43-60. 10.1007/s10754-009-9070-6.

Bouche G, Migeot V, Mathoulin-Pelissier S, Salamon R, Ingrand P: Breast cancer surgery: do all patients want to go to high-volume hospitals?. Surgery. 2008, 143: 699-705. 10.1016/j.surg.2008.03.013.

Vonberg R, Sander C, Gastmeier P: Consumer attitudes about health care acquired infections: a German survey on factors considered important in the choice of a hospital. Am J Med Qual. 2008, 23: 56-59. 10.1177/1062860607310915.

Boonen LHHM, Schut F, Donkers B: Channeling patients to preferred GPs: not a question of how, but of when!. Consumer channeling in health care: (im)possible? Consumentensturing in de zorg: (on)mogelijk?. 2009, Rotterdam: Erasmus Universiteit Rotterdam, 91-116.

Mavis B, Vasilenko P, Schnuth R, Marshall J, Jeffs MC: Female patients' preferences related to interpersonal communications, clinical competence, and gender when selecting a physician. Acad Med. 2005, 80: 1159-1165. 10.1097/00001888-200512000-00022.

Humphreys J, Mathews-Cowey S, Weinand H: Factors in accessibility of general practice in rural Australia. Med J Aust. 1997, 166: 577-580.

Robertson RH, Dixon A, Le Grand J: Patient choice in general practice: the implications of patient satisfaction surveys. J Health Serv Res Policy. 2008, 13: 67-72. 10.1258/jhsrp.2007.007055.

De Boer D, Delnoij D, Rademakers J: The importance of patient-centered care for various patient groups. Patient Educ Couns. 2011, Epub ahead of print

Ringard A, Hagen TP: Are waiting times for hospital admissions affected by patients' choices and mobility?. BMC Health Serv Res. 2011, 11: 170-178. 10.1186/1472-6963-11-170.

Tweede Kamer: 27807 nr. 25. 2004, Den Haag: Tweede Kamer

Fotaki M: Patient choice and empowerment-what does it take to make it real? A comparative study of choice in th UK and Sweden under the market-oriented reforms. Eurohealth. 2010, 11: 3-7.

Bekkers VJJM, De Kool D, Straten GFM: Educational Governance: Strategie, ontwikkeling en effecten. Themaproject 4: Ouderbetrokkenheid bij schoolbeleid. 2012, Rotterdam: Erasmus Universiteit Rotterdam, Netherlands Institute of Government

Schwartz JA, Chapman GB: Are more options always better? The attraction effect in physicians' decisions about medications. Med Decis Making. 1999, 19: 315-323. 10.1177/0272989X9901900310.

Kling JR, Mullainathan S, Shafir E, Vermeulen LC, Wrobel M: Comparison friction: experimental evidence from medicare drug plans. The Quarterly Journal of Economics. 2012, 127: 199-135. 10.1093/qje/qjr055.

Tiemeijer W: Hoe mensen keuzes maken. 2011, De psychologie van het beslissen. Amsterdam: Amsterdam University Press

Thaler RH, Sunstein CR: Nudge: Improving Decisions About Health, Wealth, and Happiness. 2009, USA: Penguin Group

Chalder M, Montgomery A, Hollinghurst S, Cooke M, Munro J, Lattimer V, Sharp D, Salisbury D: Comparing care at walk-in centres and at accident and emergency departments: an exploration of patient choice, preference and satisfaction. Emerg Med J. 2007, 24: 260-264. 10.1136/emj.2006.042499.

Hirth R, Banaszak-Holl J, McCarthy J: Nursing home to nursing home transfers: prevalence, time pattern and resident correlates. Med Care. 2000, 38: 660-669. 10.1097/00005650-200006000-00007.

Hodgkin D: Specialized service offerings and patients' choice of hospital: the case of cardiac catheterization. J Health Eon. 1996, 15: 305-332. 10.1016/0167-6296(96)00004-5.

Ketelaar NABM, Faber MJ, Flottorp S, Rygh LH, Deane KHO, Eccles MP: Public release of performance data in changing the behaviour of healthcare consumers, professionals or orgenisations (Review). Cochrane Database Syst Rev. 2011, 11: 1-40.

Moodie JJ, Masood I, Tint N, Rubinstein M, Vernon SA: Patients' attitudes towards trainee surgeons performing cataract surgery at a teaching hospital. Eye. 2008, 22: 1183-1186. 10.1038/sj.eye.6702872.

Petry JJ, Finkel R: Spirituality and choice of health care practitioner. J Altern Complement Med. 2004, 10: 939-945. 10.1089/acm.2004.10.939.

Propper C, Damiani M, Leckie G, Dixon J: Impact of patients' socioeconomic status on the distance travelled for hospital admission in the English National Health Service. J Health Serv Res Policy. 2007, 12: 153-159. 10.1258/135581907781543049.

Saha S, Taggart S, Komaromy M, Bindman A: Do patients choose physicians of their own race?. Health Aff (Millwood). 2000, 19: 76-83. 10.1377/hlthaff.19.4.76.

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Acknowledgements

We would like to thank the authors of all the studies we reviewed. Additionally, we would like to thank Linda Schoonmade and Patriek Mistiaen for helping us think the search strategy through. We would also like to thank Mike Wilkinson for copyediting the paper and Christiaan Lako, Pauline Rosenau and Mylene Lagarde for reviewing the paper. Finally, we would like to thank the Dutch Ministry of Education, Culture and Science, which provided funding for this review.

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AV participated in the design of the study, carried out the literature search and selection process, charted and modelled the data and drafted the paper. JR also participated in the design of the study, the literature selection process and the modelling of the data and helped to draft the paper. All the authors participated in modelling the data, drafting the paper and reading and approving the final text.

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Victoor, A., Delnoij, D.M., Friele, R.D. et al. Determinants of patient choice of healthcare providers: a scoping review. BMC Health Serv Res 12 , 272 (2012). https://doi.org/10.1186/1472-6963-12-272

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DOI : https://doi.org/10.1186/1472-6963-12-272

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Determinants of patient choice of medical provider: a case study in rural China

Affiliation.

• 1 Harvard School of Public Health, Boston, USA.
• PMID: 10187600
• DOI: 10.1093/heapol/13.3.311

This study examines the factors that influence patient choice of medical provider in the three-tier health care system in rural China: village health posts, township health centres, and county (and higher level) hospitals. The model is estimated using a multinomial logit approach applied to a sample of 1877 cases of outpatient treatment from a household survey in Shunyi county of Beijing in 1993. This represents the first effort to identify and quantify the impact of individual factors on patient choice of provider in China. The results show that relative to self-pay patients, Government and Labour Health Insurance beneficiaries are more likely to use county hospitals, while patients covered by the rural Cooperative Medical System (CMS) are more likely to use village-level facilities. In addition, high-income patients are more likely to visit county hospitals than low-income patients. The results also reveal that disease patterns have a significant impact on patient choice of provider, implying that the ongoing process of health transition will lead people to use the higher quality services offered at the county hospitals. We discuss the implications of the results for organizing health care finance and delivery in rural China to achieve efficiency and equity.

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• Utilization and out-of-pocket expenses of primary care among the multimorbid elderly in China: A two-part model with nationally representative data. Chen Y, Liu W. Chen Y, et al. Front Public Health. 2022 Nov 24;10:1057595. doi: 10.3389/fpubh.2022.1057595. eCollection 2022. Front Public Health. 2022. PMID: 36504938 Free PMC article.
• Determinants of Mental Healthcare-Seeking Behavior of Postpartum Women in Ibadan, Nigeria. Odufuwa OT, Olaniyan O, Okuonzi SA. Odufuwa OT, et al. Front Glob Womens Health. 2022 Jun 29;3:787263. doi: 10.3389/fgwh.2022.787263. eCollection 2022. Front Glob Womens Health. 2022. PMID: 35846560 Free PMC article.
• Association between resident status and patients' experiences of primary care: a cross-sectional study in the Greater Bay Area, China. Wu J, Liu R, Shi L, Zheng L, He N, Hu R. Wu J, et al. BMJ Open. 2022 Mar 25;12(3):e055166. doi: 10.1136/bmjopen-2021-055166. BMJ Open. 2022. PMID: 35338060 Free PMC article.
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Assessment of underlying cancer risk

This project is examining way of enhancing the implementation of patient selection (triage) for assessment of underlying cancer risk

20 June 2024

Common diagnostics can help assess underlying cancer risk, but many cancer patients do not receive them as part of their initial assessment. Faecal Immunochemical Testing (FIT) has been implemented at pace, but completion and return of the test, and subsequent referrals vary by sociodemographic group. Blood test abnormalities detected many months pre-diagnosis is seemingly missed or misattributed to other causes.  Test panels are recommended for triaging referrals, but which test abnormalities should trigger referral remains unclear. Further, there is a need to survey concordance with referral guidelines and related variation by patient group and over time. Risk stratification approaches using primary care records data have been developed and some have been externally validated. However, the potential for risk-based approaches to patient selection is not realised as these tools are rarely used. Prediction tools require sophisticated analytics but their implementation in real-time is not supported by available health records software (EMIS, SystemOne, Vision). Medical device regulation requirements further impede clinical translation of prediction tools. 

For patients with low-risk symptoms not justifying immediate urgent referral, immediate specialist referrals cannot be recommended. When cancer is present a vigilant approach post-initial consultation (‘safety-netting’) can shorten diagnostic delays, through appropriate re-consultation or proactive follow-up. Safety-netting consultation tools, and text messages post-consultation (‘text-netting’) can support appropriate re-consultation, or follow-up, but evidence of effectiveness is currently lacking. 

Aims and objectives

• To identify patient group inequalities in test use for triaging patients for suspected cancer referral and concordance with existing referral guidelines. 
• To enable risk stratification model development and clinical implementation.  
• To identify effective diagnostic strategies to prevent premature diagnostic closure. 

Policy Relevance & Dissemination  

We will identify key bottle necks across phases of the testing process (e.g., between test request – test performance – test result – and test response) and degree of guideline concordance to reveal implementation strategies to optimise the primary or secondary care assessment of patients with possible cancer symptoms.

We hope to remove barriers to optimising patient selection for cancer investigation through risk stratification using prediction tools, and identify and develop patient, GP, and system level solutions to optimise diagnostic reasoning and patient follow-up.

This is to prevent delays and late-stage diagnoses in patients with non-alarm symptoms, and will involve direct engagement with DHSC policy teams; NHS England Cancer Teams; Cancer Research UK Policy; CRUK Test Evidence Transition team; RCGP; SAPC; and MHRA. 

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The NIHR Policy Research Unit in Cancer Awareness, Screening and Early Diagnosis is part of the NIHR and hosted by UCL.

The NIHR Policy Research Unit in Cancer awareness, screening and early diagnosis

• Introduction
• Article Information

Prevalence in the randomly sampled dataset was 71.8%; prior studies have estimated that prevalence ranges from 64%-76% (as shown in the shaded horizontal).

eMethods. Rules-Based NLP Algorithm

eTable. Performance Characteristics of Existing NLP Classifier

eFigure. Study Design

Data Sharing Statement

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Liang AS , Vedak S , Dussaq A, et al. Using a Large Language Model to Identify Adolescent Patient Portal Account Access by Guardians. JAMA Netw Open. 2024;7(6):e2418454. doi:10.1001/jamanetworkopen.2024.18454

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Using a Large Language Model to Identify Adolescent Patient Portal Account Access by Guardians

• 1 Division of Clinical Informatics, Stanford University School of Medicine, Palo Alto, California
• 2 Department of Pediatrics, Stanford University School of Medicine, Palo Alto, California

The 21st Century Cures Act mandates electronic health record (EHR) access for patients and their legal representatives. In its balance, the Health Insurance Portability and Accountability Act (HIPAA) and state minor consent laws stipulate that adolescents can consent to specific health services and have certain privacy rights over related data. 1 , 2 To reconcile these legal requirements, patient portals offer differential access to the health record for adolescent vs parent and/or guardian proxy accounts. However, 64% to 76% of adolescent accounts are directly accessed by guardians, 3 jeopardizing confidentiality and potentially affecting adolescents’ willingness to engage with care. 4 Our institution developed a rules-based natural language processing (NLP) algorithm to detect direct guardian access of adolescents’ primary accounts through message content analysis 3 ; however, low sensitivity and manual workflow limited its utility. Large language models (LLMs) have excelled in natural language-based medical tasks, 5 and emerging EHR–LLM integrations provide opportunities for seamless workflow. In this study, a LLM’s ability to detect guardian authorship of messages originating from adolescent patient portals was tested.

This single-site diagnostic/prognostic study describes the GPT-4 (Open AI; model gpt-4-32k-0613) LLM’s performance at identifying parent- and/or guardian-authored portal messages. Messages from adolescent patient portal accounts at Stanford Children’s Health between June 1, 2014, and February 28, 2020, were sampled and manually reviewed for authorship as described in the study by Ip et al. 3 Two prompts were iteratively engineered on a stratified random subset of 20 messages until perfect performance (100% sensitivity and specificity) was achieved: one focused on authorship identification (single task, eMethods in Supplement 1 ) and another that generated a response to the message and identified authorship (multitask, eMethods in Supplement 1 ). Both prompts were tested on remaining messages using our institution’s personal health information–compliant LLM (eFigure in Supplement 1 ) with our NLP algorithm's performance as a benchmark (eMethods and eTable in Supplement 1 ). To account for correlated data, performance on 1 randomly selected message per patient was analyzed (eMethods in Supplement 1 ). Positive predictive values (PPV) and negative predictive values (NPV) were calculated from the tested sample, then mathematically modeled on varying prevalences (eMethods in Supplement 1 ). The 95% CIs were calculated using the Clopper-Pearson exact method. Statistical analysis was performed with JavaScript ECMAScript 2023 from December 2023 to April 2024.

Of the 2088 test messages, 1500 (71.8%) were labeled as parent- or guardian-authored and 588 (28.2%) as patient-authored. The single-task LLM achieved a sensitivity of 98.1% (95% CI, 97.3%-98.8%), and the multitask LLM achieved a sensitivity of 98.3% (95% CI, 97.5%-98.9%). The single-task LLM achieved a specificity of 88.4% (95% CI, 85.6%-90.9%); and the multitask LLM achieved a specificity of 88.9% (95% CI, 86.1%-91.4%) ( Table ). This corresponded to PPV and NPV greater than 95% for multitask LLM, and the classifiers’ PPV and NPV exceeded 90% on the previously reported prevalence range 3 ( Figure ). Single-task and multitask classifiers performed statistically identically, and removing correlated data did not significantly affect classifier performance ( Table ).

This study’s LLM-based classifiers accurately detected guardian authorship of messages sent from an adolescent patient portal, achieving PPV and NPV exceeding 95%. This LLM had significantly better sensitivity and NPV than our current NLP algorithm and could enhance adolescent confidentiality, identifying more instances of direct guardian access with a relatively small increase in false positives. Our head-to-head comparison of different prompts reassuringly showed no performance deterioration despite the added cognitive burden of drafting a response in the multi-task large language model classifier. Therefore, these results suggest that EHR integrations can perform both tasks in a single LLM interaction, presenting a scalable application for clinical use. Limitations included single-site data, exclusions of non-English messages, and small number of unique patients. Additionally, expert review may have misidentified the author. Challenges for implementation included the need for an HIPAA-compliant LLM instance, accounting for instances where patients permitted direct portal access by parents and/or guardians, and thoughtful communication around false-positive cases. Ultimately, reliable identification of nonpatient-authored messages has implications beyond adolescent medicine. Among adults, care partners commonly access patient portals using the patient’s credentials, 6 especially relevant for geriatric patients or individuals with developmental differences. Our results found that this study’s LLM has potential in improving safeguards for patient confidentiality.

Accepted for Publication: April 23, 2024.

Published: June 25, 2024. doi:10.1001/jamanetworkopen.2024.18454

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Liang AS et al. JAMA Network Open .

Corresponding Author: April S. Liang, MD, Division of Clinical Informatics, Stanford University School of Medicine, 453 Quarry Rd, MC 5660, Palo Alto, CA 94304 ( [email protected] ).

Author Contributions: Dr Liang had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: Liang, Vedak, Dussaq, Yao, Ip.

Drafting of the manuscript: Liang, Vedak, Yao, Pageler.

Critical review of the manuscript for important intellectual content: Vedak, Dussaq, Yao, Morse, Ip, Pageler.

Statistical analysis: Dussaq.

Administrative, technical, or material support: Liang, Vedak, Dussaq, Yao, Morse, Pageler.

Supervision: Ip, Pageler.

Conflict of Interest Disclosures: Dr Ip reported he is an employee of nference and has financial interest in the company. No other disclosures were reported.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: We thank Stanford Children’s Health data scientists Conner Brown, BSE, and William Haberkorn, BA, for executing the large language model application programming interface to generate the classifier outputs. We thank Stanford University School of Medicine clinical assistant professor of anesthesiology James Xie, MD, and former Stanford Children’s Health senior data scientist Austin Powell, MS, for their prior work developing the natural language processing algorithm. They were not compensated.

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The Woman Behind Freud’s First Case Study

The case of anna o. showed that psychoanalysis worked. did freud tamper with it.

There is perhaps no one more devoted to the cause than a convert, and there is no one more violent toward it than a person who has lost their faith. The faithful turned faithless take up the act of crusade, but in reverse: new atheists confronting the world with secular eyes, children who learn that their parents aren’t omnipotent. They have suffered the loss of an organizing principle, the very thing they built their life around. Now, they may seek revenge on the object that caused an earlier delusion. The commitment doesn’t end—it just takes on new guises.

Beyond the reactions of former lovers and former zealots, we see this in the history of psychoanalysis, perhaps because the practice attracts and demands those same qualities of immersion and devotion. Many have justly loved psychoanalysis, and many have justly despaired of it. This includes the very founders of rational emotive behavioral therapy and cognitive behavioral therapy, who brought about a sea change in mental health care, and the critics Frederick Crews, Jeffrey Masson, and Philip Rieff, who turned against Freud even after he had been unthroned as king of the twentieth century. This hatred can feel quasi-personal, aimed at the originator, their father figure, Sigmund Freud.

This loss of faith looms over Gabriel Brownstein’s book, The Secret Mind of Bertha Pappenheim: The Woman Who Invented Freud’s Talking Cure . On its face, the book is a study of the first analytic patient (although she didn’t exactly receive psychoanalytic treatment), Bertha Pappenheim. Pappenheim, who was treated by Freud’s mentor Josef Breuer in Vienna, was the subject of one of Breuer’s case studies and was much discussed by Freud throughout his own career. The book’s stated aim is to offer a full portrait of someone flattened and circulated as a specimen. For Pappenheim is best known by another name—Anna O.—and is best known not as her full person, who left a legacy of feminist and activist patronage, but as the world’s most famous hysteric.

But quietly, this is also a book about the birth and death of psychoanalysis—which is to say that the narrative of Freud’s ascendance and betrayal is the engine that drives the book. Brownstein argues, sometimes contradictorily, that Freud’s brilliance and his drive to make his way as a medical doctor propelled him to tamper with Bertha’s story.

Given that Pappenheim’s stunning cure is the origin story of psychoanalysis, Brownstein seeks to denigrate the whole endeavor on these grounds. If the Anna O. case was a fraud, so, too, would the cure be that she discovered.

Hysteria, much like psychoanalysis, has a storied past, one with a powerful crescendo followed by a caesura. Though the term “hysteric” is now assumed in common speech to be either a pejorative epithet, synonymous with performative hyper-emotionality ( he was hysterical ), or a historical diagnosis made up by misogynistic doctors (like, some argue, Breuer and Freud), the condition was once quite common. For the uninitiated, hysteria is an illness where the body speaks, where neurotic symptoms appear in and on it. It was treated by an array of cures, from gynecological massage (prescribed orgasm), hypnotism, rest, and drugging, to change of scenery, and, yes, for a very few patients, starting in the late nineteenth century, Breuer and Freud’s cathartic method. This eventually became psychoanalysis. This was, it must be said, a treatment that seems preferable to the other options.

Bertha Pappenheim was in many ways a typical hysterical patient, and an extraordinary woman. When she went to see Breuer in 1880, she presented with the typical hysterical complaints: partial paralysis, disturbances of appetite and language, pain. She couldn’t recall her native German and only spoke in English. She wouldn’t drink water. She had fallen ill while nursing her father, and her condition deteriorated upon his death. She was treated both in her home and in an asylum, often with high doses of drugs. What marks her case as special is that Pappenheim was the first person on Earth to be treated by the cathartic method, in large part because she invented it. Anytime you hear someone say “talking cure,” they’re using the very term Pappenheim ascribed to the yearslong experiment she undertook, morning and night, with her doctor. As she chattered on, as she engaged in the “chimney sweeping” of her mind—so the story goes—she felt better.

Freud and Breuer went on to co-write the groundbreaking Studies on Hysteria , published in 1895. The two doctors, one senior and one junior, open the book with a co-written introduction and end it with a pair of stand-alone essays (Freud’s undermining Breuer’s) in which the nascent theories of repression, defense, catharsis, and abreaction first appear. Each supplied case material of hysteric women treated by this nascent cathartic method. Freud wrote up four cases, and Breuer only contributed the case of Pappenheim, now disguised and named “Anna O.” The two detailed the symptoms of their patients and how each was aided, if not outright cured, by this new talking protocol.

In Breuer’s write-up of Anna O., which only runs about 25 pages, he elaborates on the case study, telling his readers how ill Anna was, when, and why. He then goes on to describe his therapeutic practice of sitting with her at night, and how, while Anna O. was under hypnosis, the two came to “develop a therapeutic technique” of linking each of her symptoms to the moment it appeared. The water she will not drink, for instance, is linked to a moment she saw her English ladies’ companion let a little dog drink from her glass. After the connection is revealed under hypnosis, Breuer tells us, Anna O. drinks water once more. The process repeated until there were no symptoms left, and Anna O.’s mental state presumably returned to normal.

The problem is—and basically all historians of psychoanalysis agree on this point—that even though Breuer and Freud reported a miracle cure, Anna O. didn’t get better. In fact, she got worse and was put in a sanatorium. The question is why. Brownstein, following the anti-Freud tradition, attributes this failure to the treatment. Freud, of course, attributed this failure to the person who offered the treatment—Breuer—not because he couldn’t cure her, but because he didn’t finish doing so.

Like all origin myths, the case has been subject to endless interpretation and reinterpretation. Even the original case study is retrospective: Breuer didn’t write up the Anna O. case at the time of treatment. He did so at Freud’s urging, so that the two might document this new technique of psychotherapy. Anna O. thus became the first patient of psychoanalysis only after the fact, and even though her treatment has just about nothing in common with psychoanalysis today, she is celebrated as such. Freud then revised the case multiple times across his life (in private letters, then in publications in 1910 and 1914), often to diminish Breuer’s role in the origin of psychoanalysis. This is in part due to what Freud thought of privately as Breuer’s failure: When Anna O. showed Breuer she had transferred onto him—by fantasizing about having his baby—Breuer ran away. Breuer could have invented psychoanalysis had he stayed in the room—but he didn’t dare. And thus Anna remained ill, but, in Freud’s understanding, psychoanalysis was not at fault.

Once Freud died, others revised the case in their own ways. Stacks of books can be called up in any research library by those who either defend or revile Freud—and nearly all of them, at one point, turn to Anna O. These studies often seek to collate and correlate Breuer’s flattened write-up of the case with historical reality, trying to reconstruct both Anna O.’s illness and her medical treatment. Some are feminist rereadings of the case, arguing that Anna O. was sick with patriarchy; others center squarely on Freud’s obsession with the case, excavating his letters about Anna O. to various ends.

What’s plain as day: Pappenheim has become the Rorschach test for the field. What we see in her case tends to be run through our feelings about psychoanalysis. The great historian of psychoanalysis John Forrester has argued that the baby that Anna O. spoke of wanting to have with Breuer was psychoanalysis—something she conceived with Breuer, even though he wouldn’t stick around and take responsibility for it. Anti-Freudian Mikkel Borch-Jacobsen sees Anna O.’s case as entirely fabricated, a young woman taken in by her handsome doctor and given huge quantities of drugs; if she invented psychoanalysis, she was the first to be duped by it. As the late Peter Gay observed, “There are contradictions and obscurities in successive versions of the case, but this much is more or less beyond dispute: In 1880, when Anna O. fell ill, she was twenty-one.”

But because very little besides Breuer’s documents is known of her life at the time of treatment, we project what we want onto her, and we can, for her history is a mere fragment. That we continue to do so makes exquisite sense: Psychoanalysis teaches us we must go back to our origins to go forward. And the treatment of Anna O. by Breuer is one way—a decent way—to conceptualize the start of Freud’s theory of mind.

Brownstein’s main critique of Freud’s use of Anna O. is this: that he took her case for his own material ends (though, by the same token, we might ask after Brownstein’s book advance). Freud was a broke young doctor; he needed to get married, and, to do so, he needed to press Breuer into writing Studies on Hysteria so that he could practice this new treatment with a kind of paternal authorization, styling himself as a doctor of “the cathartic method of J. Breuer.”

Brownstein agrees with anti-Freudians like Borch-Jacobsen and Crews that Anna O.’s treatment was a dismal failure. And even though that would make the lie—that Anna O. was cured—Breuer’s, Brownstein argues it was Freud who metaphorically had a gun to his mentor’s head and forced him to write it. More softly, Brownstein argues that Anna O. obscures Bertha Pappenheim, whom Brownstein now promises to deliver to us. Here’s the problem: Brownstein wants to make Freud the (very) bad guy of a story that had little to do with him, even if he had a great deal to do with the case becoming a story. So much so that Brownstein treats the possibility of Freud seeing Bertha Pappenheim at a party years after the treatment as corroborating evidence for some kind of misdeed.

Brownstein thus rewrites up the notorious case, with his chatty, negative asides and interpretations taking center stage. His first close reading from the book is, appropriately, from the first page. He argues that, though Studies purports to be “about the sex lives and sex drives of young bourgeois women,” it “begins by announcing that, for the purposes of propriety, any discussion of their actual intimate lives will be avoided.” Brownstein argues that this is a cover—that Breuer and Freud are maliciously withholding evidence for their theory because there isn’t any and because the doctors wanted to appear respectable. But if we read the first page of Studies , here’s what Breuer and Freud actually wrote: “It would be a grave breach of confidence to publish material of this kind, with the risk of patients being recognized and their acquaintances becoming informed of facts which were confided only to the physician.” There is a deep truth to what Freud and Breuer argue: They were working in a small coterie of largely wealthy Viennese Jewish patients. Everyone knew one another (hence, the great possibility of Freud running into Pappenheim). If you circulated reports of the ills of a young woman’s “marriage bed” or lack thereof, it would have meant no father would refer his daughter to Breuer or Freud, let alone the greater ethical considerations Brownstein says are gestured to half-heartedly.

Elsewhere, Brownstein accuses Freud of having a faulty memory and disguising the patient (despite the authors’ own opening warning to the reader not to go looking for biographical information of Pappenheim). To cover over the lack of details about her, Brownstein freely narrativizes the case, turning it into a historical fiction. At other times, Brownstein seems furious that Freud tends to write beautifully—Brownstein takes this as a sign of fudging the facts—while he then turns to close reading it like a literary critic.

By the end, we know from Brownstein that we’re supposed to find Breuer largely unobjectionable, but in the grips of a young Freud. The cardinal sin for Brownstein, though, is that Anna O. wasn’t made better. (Brownstein believes that she was in fact suffering from a functional neurological disorder, a contemporary diagnosis that overlaps with hysteria.) She was transported back to the asylum, so ill that Breuer reportedly told Freud his beloved patient might be better off dead, so that she might be free of suffering. Yet we might pause and say something did indeed happen in that treatment: Pappenheim was ultimately able to recover enough. By 1889, at 29 years of age, she was able not only to get out of bed, to talk, but to work in a soup kitchen. From this year on, she published—first anonymously and then pseudonymously, under the name Paul Berthold. Soon, Pappenheim was finally known not as Anna O., not as Berthold, but as herself. She also became famous as herself, a powerful, feminist leader, founding the Jewish Women’s Association and centralizing Jewish women’s organizing toward both employment and charity.

Why a book about Bertha Pappenheim now? One answer: With its claim that it will deliver readers Pappenheim in full, Brownstein’s book sits on that ever-expanding shelf of nonfiction books that seek to tell the stories of women who have been relegated to the margins of history, returning them to their larger, unobfuscated import. The book, too, in trying to bring Pappenheim’s story up to the present by rediagnosing her with functional neurological disorder, joins the book market for explorations of contested illness. Yet this book isn’t exactly proper to either of these subgenres. Instead, we might make sense of it as a work of backlash: Just as a range of analysts and writers have turned once more to Freud (as The New York Times proclaimed in an article not quite aptly titled “Not Your Daddy’s Freud”), so have others returned to maligning him. Brownstein has offered us, perhaps, the first book of the Freud Wars 2.0.

Brownstein, in fact, inherits the role of Freud skeptic from an earlier generation. His father, Dr. Shale Brownstein, was a prominent New York psychiatrist and psychoanalyst with a Rolodex of famous patients. Sometime in the 1980s, Dr. Brownstein became disillusioned with psychoanalysis and became an anti-Freudian—though we are never quite told why. One night, when Brownstein went to visit his father, he found him in his underwear, speaking wildly. The subject: Bertha Pappenheim. His father held a thick envelope filled with scientific and historic papers, newspaper clippings, reviews of books, and his own essay on the subject.

His father gave him the manila envelope. The younger Brownstein went home to Brooklyn, and the next day his father was dead. As if in a novel, Brownstein then becomes fixated on the envelope and its contents only to discover he has misplaced it. His own book is as much an attempt to decipher his father’s theory about Bertha Pappenheim as to understand his father’s turn against Freud. Brownstein makes clear that his father was a devoted doctor, and treated luminaries in downtown New York, including Peter Hujar and Richard Serra. Dr. Brownstein tended to babies with HIV in the 1980s who languished otherwise in their cots, when others wouldn’t dare go near. Dr. Brownstein gave everything to psychoanalysis, but then something changed. We don’t quite know what, but his father became so disillusioned that he burned all 24 volumes of Freud’s Standard Edition .

Was it the homophobia of mainstream psychoanalysis that rightfully made him repudiate his training? Was it indeed the legacy of Anna O.? I wish we knew what Brownstein felt as he wrestled with Freud via his father. As author and son, Brownstein is overwhelmed by the research subject he must now try to understand and, more importantly, terribly overwhelmed by the pain of being alive when life is most brutal. Shortly after his father’s death, his wife is diagnosed with terminal pancreatic cancer, and when the global pandemic arrives, Brownstein must weather it without them.

While Brownstein seemingly hates Freud, he, like many others, can’t escape him. Early in the book, he disparages two Freudian terms: “secondary gain,” which can be described as the unconscious advantage patients acquire through their illness (stereotyped here as attention), and “ la belle indifférence ,” a calm character in the face of crisis. But toward the book’s close, Brownstein suddenly tips his hand: He comes to a form of self-understanding through these concepts. In not getting treated for a heart problem, he says he has a case of la belle indifférence . In writing the book, he self-analyzes, he can be understood as having a case of secondary gain—after all, Brownstein was quite literally paid for producing it.

But Brownstein uses these concepts defensively—to show his reader he is in on the joke. The book itself, more movingly, is a testament to yet another set of Freudian concepts: the return of the repressed, as evidenced by his return to the use of Freud; working through (here, loss of his father, his wife); and, indeed, sublimation. Writing the book then might be an act of Freudian sublimation; it is also an act of devotion. This article has been updated.

Hannah Zeavin is an assistant professor of history at UC Berkeley. She is the author of The Distance Cure: A History of Teletherapy .

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Article Contents

Answer to part 1, answer to part 2, answer to part 3, answer to part 4, answer to part 5.

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Educational Case: A 57-year-old man with chest pain

Contributed equally.

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Nikhil Aggarwal, Subothini Selvendran, Vassilios Vassiliou, Educational Case: A 57-year-old man with chest pain, Oxford Medical Case Reports , Volume 2016, Issue 4, April 2016, Pages 62–65, https://doi.org/10.1093/omcr/omw008

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This is an educational case report including multiple choice questions and their answers. For the best educational experience we recommend the interactive web version of the exercise which is available via the following link: http://www.oxfordjournals.org/our_journals/omcr/ec01p1.html

A 57 year-old male lorry driver, presented to his local emergency department with a 20-minute episode of diaphoresis and chest pain. The chest pain was central, radiating to the left arm and crushing in nature. The pain settled promptly following 300 mg aspirin orally and 800 mcg glyceryl trinitrate (GTN) spray sublingually administered by paramedics in the community. He smoked 20 cigarettes daily (38 pack years) but was not aware of any other cardiovascular risk factors. On examination he appeared comfortable and was able to complete sentences fully. There were no heart murmurs present on cardiac auscultation. Blood pressure was 180/105 mmHg, heart rate was 83 bpm and regular, oxygen saturation was 97%.

What is the most likely diagnosis?

An ECG was requested and is shown in figure 1.

How would you manage the patient? (The patient has already received 300 mg aspirin).

30 minutes later the patient's chest pain returned with greater intensity whilst waiting in the emergency department. Now, he described the pain as though “an elephant is sitting on his chest”. The nurse has already done an ECG by the time you were called to see him. This is shown in figure 2.

ECG on admission.

ECG 30 minutes after admission.

What would be the optimal management for this patient?

He was taken to the catheterization lab where the left anterior descending coronary artery (LAD) was shown to be completely occluded. Following successful percutaneous intervention and one drug eluding stent implantation in the LAD normal flow is restored (Thrombosis in myocardial infarction, TIMI = 3). 72 hours later, he is ready to be discharged home. The patient is keen to return to work and asks when he could do so.

When would you advise him that he could return to work?

One week later, he receives a letter informing him that he is required to attend cardiac rehabilitation. The patient is confused as to what cardiac rehabilitation entails, although he does remember a nurse discussing this with him briefly before he was discharged. He phones the hospital in order to get some more information.

Which of the following can be addressed during cardiac rehabilitation?

A - Acute coronary syndrome

Although the presentation could be attributable to any of the above differential diagnoses, the most likely etiology given the clinical picture and risk factors is one of cardiac ischemia. Risk factors include gender, smoking status and age making the diagnosis of acute coronary syndrome the most likely one. The broad differential diagnosis in patients presenting with chest pain has been discussed extensively in the medical literature. An old but relevant review can be found freely available 1 as well as more recent reviews. 2 , 3

C - Atorvastatin 80 mg, Clopidogrel 300 mcg, GTN 500 mcg, Ramipril 2.5 mg,

In patients with ACS, medications can be tailored to the individual patient. Some medications have symptomatic benefit but some also have prognostic benefit. Aspirin 4 , Clopidogrel 5 , Atenolol 6 and Atorvastatin 7 have been found to improve prognosis significantly. ACE inhibitors have also been found to improve left ventricular modeling and function after an MI. 8 , 9 Furthermore, GTN 10 and morphine 11 have been found to be of only significant symptomatic benefit.

Oxygen should only to be used when saturations <95% and at the lowest concentration required to keep saturations >95%. 12

There is no evidence that diltiazem, a calcium channel blocker, is of benefit. 13

His ECG in figure 1 does not fulfil ST elevation myocardial infarction (STEMI) criteria and he should therefore be managed as a Non-STEMI. He would benefit prognostically from beta-blockade however his heart rate is only 42 bpm and therefore this is contraindicated. He should receive a loading dose of clopidogrel (300 mg) followed by daily maintenance dose (75 mg). 14 , 15 He might not require GTN if he is pain-free but out of the available answers 3 is the most correct.

D - Proceed to coronary angiography

The ECG shows ST elevation in leads V2-V6 and confirms an anterolateral STEMI, which suggests a completely occluded LAD. This ECG fulfils the criteria to initiate reperfusion therapy which traditionally require one of the three to be present: According to guidance, if the patient can undergo coronary angiography within 120 minutes from the onset of chest pain, then this represents the optimal management. If it is not possible to undergo coronary angiography and potentially percutaneous intervention within 2 hours, then thrombolysis is considered an acceptable alternative. 12 , 16

≥ 1 mm of ST change in at least two contiguous limb leads (II, III, AVF, I, AVL).

≥ 2 mm of ST change in at least two contiguous chest leads (V1-V6).

New left bundle branch block.

GTN and morphine administration can be considered in parallel but they do not have a prognostic benefit.

E - Not before an exercise test

This patient is a lorry driver and therefore has a professional heavy vehicle driving license. The regulation for driving initiation in a lorry driver following a NSTEMI/ STEMI may be different in various countries and therefore the local regulations should be followed.

In the UK, a lorry driver holds a category 2 driving license. He should therefore refrain from driving a lorry for at least 6 weeks and can only return to driving if he completes successfully an exercise evaluation. An exercise evaluation is performed on a bicycle or treadmill. Drivers should be able to complete 3 stages of the standard Bruce protocol 17 or equivalent (e.g. Myocardial perfusion scan) safely, having refrained from taking anti-anginal medication for 48 hours and should remain free from signs of cardiovascular dysfunction during the test, notably: angina pectoris, syncope, hypotension, sustained ventricular tachycardia, and/or electrocardiographic ST segment shift which is considered as being indicative of myocardial ischemia (usually >2 mm horizontal or down-sloping) during exercise or the recovery period. 18

For a standard car driving license (category 1), driving can resume one week after successful intervention providing that no other revascularization is planned within 4 weeks; left ventricular ejection fraction (LVEF) is at least 40% prior to hospital discharge and there is no other disqualifying condition.

Therefore if this patent was in the UK, he could restart driving a normal car one week later assuming an echocardiogram confirmed an EF > 40%. However, he could only continue lorry driving once he has passed the required tests. 18

E - All of the above

Cardiac rehabilitation bridges the gap between hospitals and patients' homes. The cardiac rehabilitation team consists of various healthcare professions and the programme is started during hospital admission or after diagnosis. Its aim is to educate patients about their cardiac condition in order to help them adopt a healthier lifestyle. This includes educating patients' about their diet, exercise, risk factors associated with their condition such as smoking and alcohol intake and finally, about the medication recommended. There is good evidence that adherence to cardiac rehabilitation programmes improves survival and leads to a reduction in future cardiovascular events.​ 19 , 20

Oille JA . Differential diagnosis of pain in the chest . Can Med Assoc J . 1937 ; 37 (3) : 209 – 216 . http://www.ncbi.nlm.nih.gov/pmc/articles/PMC536075/ .

Google Scholar

Lee TH , Goldman L . Evaluation of the patient with acute chest pain . N Engl J Med . 2000 ; 342 (16) : 1187 – 1195 . http://www.nejm.org/doi/full/10.1056/NEJM200004203421607 .

Douglas PS , Ginsburg GS . The evaluation of chest pain in women . N Engl J Med . 1996 ; 334 (20) : 1311 – 1315 . http://www.nejm.org/doi/full/10.1056/NEJM199605163342007 .

Baigent C , Collins R , Appleby P , Parish S , Sleight P , Peto R . ISIS-2: 10 year survival among patients with suspected acute myocardial infarction in randomised comparison of intravenous streptokinase, oral aspirin, both, or neither. the ISIS-2 (second international study of infarct survival) collaborative group . BMJ . 1998 ; 316 (7141) : 1337 – 1343 . http://www.ncbi.nlm.nih.gov/pmc/articles/PMC28530/ .

Yusuf S , Zhao F , Mehta S , Chrolavicius S , Tognoni G , Fox K . Clopidogrel in unstable angina to prevent recurrent events trail investigators . effects of clopidogrel in addition to aspirin in patients with acute coronary syndromes without ST-segment elevation . N Engl J Med . 2001 ; 345 (7) : 494 – 502 . http://www.nejm.org/doi/full/10.1056/NEJMoa010746#t=articleTop .

Yusuf S , Peto R , Lewis J , Collins R , Sleight P . Beta blockade during and after myocardial infarction: An overview of the randomized trials . Prog Cardiovasc Dis . 1985 ; 27 (5) : 335 – 371 . http://www.sciencedirect.com/science/article/pii/S0033062085800037 .

Schwartz GG , Olsson AG , Ezekowitz MD et al.  . Effects of atorvastatin on early recurrent ischemic events in acute coronary syndromes: The MIRACL study: A randomized controlled trial . JAMA . 2001 ; 285 (13) : 1711 – 1718 . http://jama.jamanetwork.com/article.aspx?articleid=193709 .

Pfeffer MA , Lamas GA , Vaughan DE , Parisi AF , Braunwald E . Effect of captopril on progressive ventricular dilatation after anterior myocardial infarction . N Engl J Med . 1988 ; 319 (2) : 80 – 86 . http://content.onlinejacc.org/article.aspx?articleid=1118054 .

Sharpe N , Smith H , Murphy J , Hannan S . Treatment of patients with symptomless left ventricular dysfunction after myocardial infarction . The Lancet . 1988 ; 331 (8580) : 255 – 259 . http://www.sciencedirect.com/science/article/pii/S0140673688903479 .

Ferreira JC , Mochly-Rosen D . Nitroglycerin use in myocardial infarction patients . Circ J . 2012 ; 76 (1) : 15 – 21 . http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3527093/ .

Herlitz J , Hjalmarson A , Waagstein F . Treatment of pain in acute myocardial infarction . Br Heart J . 1989 ; 61 (1) : 9 – 13 . http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1216614/ .

Task Force on the management of ST-segment elevation acute myocardial infarction of the European Society of Cardiology (ESC), Steg PG, James SK, et al . ESC guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation . Eur Heart J . 2012 ; 33 (20) : 2569 – 2619 . http://eurheartj.oxfordjournals.org/content/33/20/2569 .

The effect of diltiazem on mortality and reinfarction after myocardial infarction . the multicenter diltiazem postinfarction trial research group . N Engl J Med . 1988 ; 319 (7) : 385 – 392 . http://www.nejm.org/doi/full/10.1056/NEJM198808183190701 .

Jneid H , Anderson JL , Wright RS et al.  . 2012 ACCF/AHA focused update of the guideline for the management of patients with unstable angina/Non–ST-elevation myocardial infarction (updating the 2007 guideline and replacing the 2011 focused update) A report of the american college of cardiology foundation/american heart association task force on practice guidelines . J Am Coll Cardiol . 2012 ; 60 (7) : 645 – 681 . http://circ.ahajournals.org/content/123/18/2022.full .

Hamm CW , Bassand JP , Agewall S et al.  . ESC guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation: The task force for the management of acute coronary syndromes (ACS) in patients presenting without persistent ST-segment elevation of the european society of cardiology (ESC) . Eur Heart J . 2011 ; 32 (23) : 2999 – 3054 . http://eurheartj.oxfordjournals.org/content/32/23/2999.long .

O'Gara PT , Kushner FG , Ascheim DD et al.  . 2013 ACCF/AHA guideline for the management of ST-elevation myocardial infarction: Executive summary: A report of the american college of cardiology foundation/american heart association task force on practice guidelines . J Am Coll Cardiol . 2013 ; 61 (4) : 485 – 510 . http://content.onlinejacc.org/article.aspx?articleid=1486115 .

BRUCE RA , LOVEJOY FW Jr . Normal respiratory and circulatory pathways of adaptation in exercise . J Clin Invest . 1949 ; 28 (6 Pt 2) : 1423 – 1430 . http://www.ncbi.nlm.nih.gov/pmc/articles/PMC439698/ .

DVLA . Https://Www.gov.uk/current-medical-guidelines-dvla-guidance-for-professionals-cardiovascular-chapter-appendix .

British Heart Foundation . Http://Www.bhf.org.uk/heart-health/living-with-heart-disease/cardiac-rehabilitation.aspx .

Kwan G , Balady GJ . Cardiac rehabilitation 2012: Advancing the field through emerging science . Circulation . 2012 ; 125 (7) : e369–73. http://circ.ahajournals.org/content/125/7/e369.full .

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Human Subjects Office

Medical terms in lay language.

Please use these descriptions in place of medical jargon in consent documents, recruitment materials and other study documents. Note: These terms are not the only acceptable plain language alternatives for these vocabulary words.

This glossary of terms is derived from a list copyrighted by the University of Kentucky, Office of Research Integrity (1990).

For clinical research-specific definitions, see also the Clinical Research Glossary developed by the Multi-Regional Clinical Trials (MRCT) Center of Brigham and Women’s Hospital and Harvard  and the Clinical Data Interchange Standards Consortium (CDISC) .

Alternative Lay Language for Medical Terms for use in Informed Consent Documents

A   B   C   D   E   F   G   H   I  J  K   L   M   N   O   P   Q   R   S   T   U   V   W  X  Y  Z

ABDOMEN/ABDOMINAL body cavity below diaphragm that contains stomach, intestines, liver and other organs ABSORB take up fluids, take in ACIDOSIS condition when blood contains more acid than normal ACUITY clearness, keenness, esp. of vision and airways ACUTE new, recent, sudden, urgent ADENOPATHY swollen lymph nodes (glands) ADJUVANT helpful, assisting, aiding, supportive ADJUVANT TREATMENT added treatment (usually to a standard treatment) ANTIBIOTIC drug that kills bacteria and other germs ANTIMICROBIAL drug that kills bacteria and other germs ANTIRETROVIRAL drug that works against the growth of certain viruses ADVERSE EFFECT side effect, bad reaction, unwanted response ALLERGIC REACTION rash, hives, swelling, trouble breathing AMBULATE/AMBULATION/AMBULATORY walk, able to walk ANAPHYLAXIS serious, potentially life-threatening allergic reaction ANEMIA decreased red blood cells; low red cell blood count ANESTHETIC a drug or agent used to decrease the feeling of pain, or eliminate the feeling of pain by putting you to sleep ANGINA pain resulting from not enough blood flowing to the heart ANGINA PECTORIS pain resulting from not enough blood flowing to the heart ANOREXIA disorder in which person will not eat; lack of appetite ANTECUBITAL related to the inner side of the forearm ANTIBODY protein made in the body in response to foreign substance ANTICONVULSANT drug used to prevent seizures ANTILIPEMIC a drug that lowers fat levels in the blood ANTITUSSIVE a drug used to relieve coughing ARRHYTHMIA abnormal heartbeat; any change from the normal heartbeat ASPIRATION fluid entering the lungs, such as after vomiting ASSAY lab test ASSESS to learn about, measure, evaluate, look at ASTHMA lung disease associated with tightening of air passages, making breathing difficult ASYMPTOMATIC without symptoms AXILLA armpit

BENIGN not malignant, without serious consequences BID twice a day BINDING/BOUND carried by, to make stick together, transported BIOAVAILABILITY the extent to which a drug or other substance becomes available to the body BLOOD PROFILE series of blood tests BOLUS a large amount given all at once BONE MASS the amount of calcium and other minerals in a given amount of bone BRADYARRHYTHMIAS slow, irregular heartbeats BRADYCARDIA slow heartbeat BRONCHOSPASM breathing distress caused by narrowing of the airways

CARCINOGENIC cancer-causing CARCINOMA type of cancer CARDIAC related to the heart CARDIOVERSION return to normal heartbeat by electric shock CATHETER a tube for withdrawing or giving fluids CATHETER a tube placed near the spinal cord and used for anesthesia (indwelling epidural) during surgery CENTRAL NERVOUS SYSTEM (CNS) brain and spinal cord CEREBRAL TRAUMA damage to the brain CESSATION stopping CHD coronary heart disease CHEMOTHERAPY treatment of disease, usually cancer, by chemical agents CHRONIC continuing for a long time, ongoing CLINICAL pertaining to medical care CLINICAL TRIAL an experiment involving human subjects COMA unconscious state COMPLETE RESPONSE total disappearance of disease CONGENITAL present before birth CONJUNCTIVITIS redness and irritation of the thin membrane that covers the eye CONSOLIDATION PHASE treatment phase intended to make a remission permanent (follows induction phase) CONTROLLED TRIAL research study in which the experimental treatment or procedure is compared to a standard (control) treatment or procedure COOPERATIVE GROUP association of multiple institutions to perform clinical trials CORONARY related to the blood vessels that supply the heart, or to the heart itself CT SCAN (CAT) computerized series of x-rays (computerized tomography) CULTURE test for infection, or for organisms that could cause infection CUMULATIVE added together from the beginning CUTANEOUS relating to the skin CVA stroke (cerebrovascular accident)

DERMATOLOGIC pertaining to the skin DIASTOLIC lower number in a blood pressure reading DISTAL toward the end, away from the center of the body DIURETIC "water pill" or drug that causes increase in urination DOPPLER device using sound waves to diagnose or test DOUBLE BLIND study in which neither investigators nor subjects know what drug or treatment the subject is receiving DYSFUNCTION state of improper function DYSPLASIA abnormal cells

ECHOCARDIOGRAM sound wave test of the heart EDEMA excess fluid collecting in tissue EEG electric brain wave tracing (electroencephalogram) EFFICACY effectiveness ELECTROCARDIOGRAM electrical tracing of the heartbeat (ECG or EKG) ELECTROLYTE IMBALANCE an imbalance of minerals in the blood EMESIS vomiting EMPIRIC based on experience ENDOSCOPIC EXAMINATION viewing an  internal part of the body with a lighted tube  ENTERAL by way of the intestines EPIDURAL outside the spinal cord ERADICATE get rid of (such as disease) Page 2 of 7 EVALUATED, ASSESSED examined for a medical condition EXPEDITED REVIEW rapid review of a protocol by the IRB Chair without full committee approval, permitted with certain low-risk research studies EXTERNAL outside the body EXTRAVASATE to leak outside of a planned area, such as out of a blood vessel

FDA U.S. Food and Drug Administration, the branch of federal government that approves new drugs FIBROUS having many fibers, such as scar tissue FIBRILLATION irregular beat of the heart or other muscle

GENERAL ANESTHESIA pain prevention by giving drugs to cause loss of consciousness, as during surgery GESTATIONAL pertaining to pregnancy

HEMATOCRIT amount of red blood cells in the blood HEMATOMA a bruise, a black and blue mark HEMODYNAMIC MEASURING blood flow HEMOLYSIS breakdown in red blood cells HEPARIN LOCK needle placed in the arm with blood thinner to keep the blood from clotting HEPATOMA cancer or tumor of the liver HERITABLE DISEASE can be transmitted to one’s offspring, resulting in damage to future children HISTOPATHOLOGIC pertaining to the disease status of body tissues or cells HOLTER MONITOR a portable machine for recording heart beats HYPERCALCEMIA high blood calcium level HYPERKALEMIA high blood potassium level HYPERNATREMIA high blood sodium level HYPERTENSION high blood pressure HYPOCALCEMIA low blood calcium level HYPOKALEMIA low blood potassium level HYPONATREMIA low blood sodium level HYPOTENSION low blood pressure HYPOXEMIA a decrease of oxygen in the blood HYPOXIA a decrease of oxygen reaching body tissues HYSTERECTOMY surgical removal of the uterus, ovaries (female sex glands), or both uterus and ovaries

IATROGENIC caused by a physician or by treatment IDE investigational device exemption, the license to test an unapproved new medical device IDIOPATHIC of unknown cause IMMUNITY defense against, protection from IMMUNOGLOBIN a protein that makes antibodies IMMUNOSUPPRESSIVE drug which works against the body's immune (protective) response, often used in transplantation and diseases caused by immune system malfunction IMMUNOTHERAPY giving of drugs to help the body's immune (protective) system; usually used to destroy cancer cells IMPAIRED FUNCTION abnormal function IMPLANTED placed in the body IND investigational new drug, the license to test an unapproved new drug INDUCTION PHASE beginning phase or stage of a treatment INDURATION hardening INDWELLING remaining in a given location, such as a catheter INFARCT death of tissue due to lack of blood supply INFECTIOUS DISEASE transmitted from one person to the next INFLAMMATION swelling that is generally painful, red, and warm INFUSION slow injection of a substance into the body, usually into the blood by means of a catheter INGESTION eating; taking by mouth INTERFERON drug which acts against viruses; antiviral agent INTERMITTENT occurring (regularly or irregularly) between two time points; repeatedly stopping, then starting again INTERNAL within the body INTERIOR inside of the body INTRAMUSCULAR into the muscle; within the muscle INTRAPERITONEAL into the abdominal cavity INTRATHECAL into the spinal fluid INTRAVENOUS (IV) through the vein INTRAVESICAL in the bladder INTUBATE the placement of a tube into the airway INVASIVE PROCEDURE puncturing, opening, or cutting the skin INVESTIGATIONAL NEW DRUG (IND) a new drug that has not been approved by the FDA INVESTIGATIONAL METHOD a treatment method which has not been proven to be beneficial or has not been accepted as standard care ISCHEMIA decreased oxygen in a tissue (usually because of decreased blood flow)

LAPAROTOMY surgical procedure in which an incision is made in the abdominal wall to enable a doctor to look at the organs inside LESION wound or injury; a diseased patch of skin LETHARGY sleepiness, tiredness LEUKOPENIA low white blood cell count LIPID fat LIPID CONTENT fat content in the blood LIPID PROFILE (PANEL) fat and cholesterol levels in the blood LOCAL ANESTHESIA creation of insensitivity to pain in a small, local area of the body, usually by injection of numbing drugs LOCALIZED restricted to one area, limited to one area LUMEN the cavity of an organ or tube (e.g., blood vessel) LYMPHANGIOGRAPHY an x-ray of the lymph nodes or tissues after injecting dye into lymph vessels (e.g., in feet) LYMPHOCYTE a type of white blood cell important in immunity (protection) against infection LYMPHOMA a cancer of the lymph nodes (or tissues)

MALAISE a vague feeling of bodily discomfort, feeling badly MALFUNCTION condition in which something is not functioning properly MALIGNANCY cancer or other progressively enlarging and spreading tumor, usually fatal if not successfully treated MEDULLABLASTOMA a type of brain tumor MEGALOBLASTOSIS change in red blood cells METABOLIZE process of breaking down substances in the cells to obtain energy METASTASIS spread of cancer cells from one part of the body to another METRONIDAZOLE drug used to treat infections caused by parasites (invading organisms that take up living in the body) or other causes of anaerobic infection (not requiring oxygen to survive) MI myocardial infarction, heart attack MINIMAL slight MINIMIZE reduce as much as possible Page 4 of 7 MONITOR check on; keep track of; watch carefully MOBILITY ease of movement MORBIDITY undesired result or complication MORTALITY death MOTILITY the ability to move MRI magnetic resonance imaging, diagnostic pictures of the inside of the body, created using magnetic rather than x-ray energy MUCOSA, MUCOUS MEMBRANE moist lining of digestive, respiratory, reproductive, and urinary tracts MYALGIA muscle aches MYOCARDIAL pertaining to the heart muscle MYOCARDIAL INFARCTION heart attack

NASOGASTRIC TUBE placed in the nose, reaching to the stomach NCI the National Cancer Institute NECROSIS death of tissue NEOPLASIA/NEOPLASM tumor, may be benign or malignant NEUROBLASTOMA a cancer of nerve tissue NEUROLOGICAL pertaining to the nervous system NEUTROPENIA decrease in the main part of the white blood cells NIH the National Institutes of Health NONINVASIVE not breaking, cutting, or entering the skin NOSOCOMIAL acquired in the hospital

OCCLUSION closing; blockage; obstruction ONCOLOGY the study of tumors or cancer OPHTHALMIC pertaining to the eye OPTIMAL best, most favorable or desirable ORAL ADMINISTRATION by mouth ORTHOPEDIC pertaining to the bones OSTEOPETROSIS rare bone disorder characterized by dense bone OSTEOPOROSIS softening of the bones OVARIES female sex glands

PARENTERAL given by injection PATENCY condition of being open PATHOGENESIS development of a disease or unhealthy condition PERCUTANEOUS through the skin PERIPHERAL not central PER OS (PO) by mouth PHARMACOKINETICS the study of the way the body absorbs, distributes, and gets rid of a drug PHASE I first phase of study of a new drug in humans to determine action, safety, and proper dosing PHASE II second phase of study of a new drug in humans, intended to gather information about safety and effectiveness of the drug for certain uses PHASE III large-scale studies to confirm and expand information on safety and effectiveness of new drug for certain uses, and to study common side effects PHASE IV studies done after the drug is approved by the FDA, especially to compare it to standard care or to try it for new uses PHLEBITIS irritation or inflammation of the vein PLACEBO an inactive substance; a pill/liquid that contains no medicine PLACEBO EFFECT improvement seen with giving subjects a placebo, though it contains no active drug/treatment PLATELETS small particles in the blood that help with clotting POTENTIAL possible POTENTIATE increase or multiply the effect of a drug or toxin (poison) by giving another drug or toxin at the same time (sometimes an unintentional result) POTENTIATOR an agent that helps another agent work better PRENATAL before birth PROPHYLAXIS a drug given to prevent disease or infection PER OS (PO) by mouth PRN as needed PROGNOSIS outlook, probable outcomes PRONE lying on the stomach PROSPECTIVE STUDY following patients forward in time PROSTHESIS artificial part, most often limbs, such as arms or legs PROTOCOL plan of study PROXIMAL closer to the center of the body, away from the end PULMONARY pertaining to the lungs

QD every day; daily QID four times a day

RADIATION THERAPY x-ray or cobalt treatment RANDOM by chance (like the flip of a coin) RANDOMIZATION chance selection RBC red blood cell RECOMBINANT formation of new combinations of genes RECONSTITUTION putting back together the original parts or elements RECUR happen again REFRACTORY not responding to treatment REGENERATION re-growth of a structure or of lost tissue REGIMEN pattern of giving treatment RELAPSE the return of a disease REMISSION disappearance of evidence of cancer or other disease RENAL pertaining to the kidneys REPLICABLE possible to duplicate RESECT remove or cut out surgically RETROSPECTIVE STUDY looking back over past experience

SARCOMA a type of cancer SEDATIVE a drug to calm or make less anxious SEMINOMA a type of testicular cancer (found in the male sex glands) SEQUENTIALLY in a row, in order SOMNOLENCE sleepiness SPIROMETER an instrument to measure the amount of air taken into and exhaled from the lungs STAGING an evaluation of the extent of the disease STANDARD OF CARE a treatment plan that the majority of the medical community would accept as appropriate STENOSIS narrowing of a duct, tube, or one of the blood vessels in the heart STOMATITIS mouth sores, inflammation of the mouth STRATIFY arrange in groups for analysis of results (e.g., stratify by age, sex, etc.) STUPOR stunned state in which it is difficult to get a response or the attention of the subject SUBCLAVIAN under the collarbone SUBCUTANEOUS under the skin SUPINE lying on the back SUPPORTIVE CARE general medical care aimed at symptoms, not intended to improve or cure underlying disease SYMPTOMATIC having symptoms SYNDROME a condition characterized by a set of symptoms SYSTOLIC top number in blood pressure; pressure during active contraction of the heart

TERATOGENIC capable of causing malformations in a fetus (developing baby still inside the mother’s body) TESTES/TESTICLES male sex glands THROMBOSIS clotting THROMBUS blood clot TID three times a day TITRATION a method for deciding on the strength of a drug or solution; gradually increasing the dose T-LYMPHOCYTES type of white blood cells TOPICAL on the surface TOPICAL ANESTHETIC applied to a certain area of the skin and reducing pain only in the area to which applied TOXICITY side effects or undesirable effects of a drug or treatment TRANSDERMAL through the skin TRANSIENTLY temporarily TRAUMA injury; wound TREADMILL walking machine used to test heart function

UPTAKE absorbing and taking in of a substance by living tissue

VALVULOPLASTY plastic repair of a valve, especially a heart valve VARICES enlarged veins VASOSPASM narrowing of the blood vessels VECTOR a carrier that can transmit disease-causing microorganisms (germs and viruses) VENIPUNCTURE needle stick, blood draw, entering the skin with a needle VERTICAL TRANSMISSION spread of disease

WBC white blood cell

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• Aiden Haghikia 1 ,
• Tobias Hegelmaier 1 ,
• Denise Wolleschak 2 ,
• Martin Böttcher 2 , 3 ,
• Vaia Pappa 1 ,
• Jeremias Motte 4 ,
• Dominic Borie 5 ,
• Ralf Gold 4 ,
• http://orcid.org/0000-0002-9872-5282 Eugen Feist 6 ,
• http://orcid.org/0000-0001-8740-9615 Georg Schett 7 , 8 ,
• http://orcid.org/0000-0002-2817-6660 Dimitrios Mougiakakos 2 , 3
• 1 Department of Neurology , Otto-von-Guericke-University Magdeburg , Magdeburg , Germany
• 2 Department of Haematology, Oncology, and Cell Therapy , Otto-von-Guericke-University Magdeburg , Magdeburg , Germany
• 3 Health Campus Immunology, Infectiology, and Inflammation , Otto-von-Guericke-University Magdeburg , Magdeburg , Germany
• 4 Department of Neurology , Ruhr University Bochum , Bochum , Germany
• 5 Kyverna Therapeutics , Emeryville , California , USA
• 6 Experimental Rheumatology , Otto-von-Guericke-University Magdeburg , Magdeburg , Germany
• 7 Department of Internal Medicine 3, Rheumatology and Immunology , Friedrich-Alexander-University Erlangen , Erlangen , Germany
• 8 Deutsches Zentrum für Immuntherapie (DZI) , Friedrich-Alexander-University Erlangen , Erlangen , Germany
• Correspondence to Professor Dimitrios Mougiakakos, Department of Hematology, Oncology, and Cell Therapy, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany; dimitrios.mougiakakos{at}med.ovgu.de ; Professor Aiden Haghikia, Department of Neurology, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany; aiden.haghikia{at}med.ovgu.de

https://doi.org/10.1136/ard-2024-226017

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• Anti-Citrullinated Protein Antibodies
• Arthritis, Rheumatoid
• Autoimmune Diseases
• Autoimmunity
• B-Lymphocytes

Myasthenia gravis (MG) is a B cell-driven autoimmune disease (AID) that can coincide with rheumatoid arthritis (RA). 1 Here, we report on a 37-year-old woman who was diagnosed with generalised, acetylcholine receptor (AChR)-antibody positive MG in 2013 and developed anticitrullinated protein antibody (ACPA) positive RA in 2020. Arthritis affected multiple hand and ankle joints that showed synovial swelling. Treatment was focused on controlling her severe MG, including thymectomy, acetylcholinesterase inhibitors, glucocorticoids, azathioprine, intravenous immunoglobulins, rituximab, and eculizumab. As the treatment of MG (ie, glucocorticoids, rituximab, and eculizumab) also affected RA activity, no additional RA-specific treatment had been initiated in the patient, considering the potential negative safety aspects of such combined interventions. The last treatment regimen before CD19-CAR T cell therapy was a combination of glucocorticoids (5 mg/day), eculizumab (1200 mg/biweekly), and pyridostigmine (120 mg short-acting and 90 mg long-acting/day). However, despite all of these treatments, both MG (quantitative myasthenia gravis (QMG) score: 18 (0–39); MG Activities of Daily Living (MG-ADL) score: 7 (0–24)) and RA (Disease Activity Score-28 based on erythrocyte sedimentation rate (DAS-28-ESR): 6 (0–9.4); Disease Activity Score-28 based on C reactive protein (DAS-28-CRP) 4 (0–8.6) and Clinical Disease Activity Index (CDAI): 24 (0–76)) remained active, impairing patient’s quality of life and leading to recurrent infections.

Based on previous experience in AIDs, 2 we decided to proceed with a named patient approach using autologous CD19-CAR T cells engineered with a fully human second-generation anti-CD19 CAR, KYV-101 (Kyverna Therapeutics, Emeryville, California, USA). KYV-101 has been used successfully in patients with neurological AIDs such as MG and multiple sclerosis. 3 4 After lymphodepletion with fludarabine (30 mg/m 2 from day −5 to −3) and cyclophosphamide (300 mg/m 2 from day −5 to −3), 1×10 8  CAR T cells were infused. The dominance of CD4+T cells among CAR T cells …

Handling editor Josef S Smolen

X @Martin_MetabIO

AH, TH, GS and DM contributed equally.

Contributors AH, TH, DW, DB and DM designed the study. TH, DW, MB, JM, EF and VP acquired the data. AH, TH, MB, JM, DB, EF, RG, GS and DM interpreted the data. AH, TH, GS and DM prepared the manuscript. AH, TH, DW, MB and DM had access to and verified the data. AH and DM, as guarantors, accept full responsibility for the work and the conduct of the study, had access to the data and control the decision to publish.

Funding AH is supported by the Deutsche Zentrum für Neurodegenerative Erkrankungen (DZNE) and the Deutsche Forschungsgemeinschaft (DFG) through the CRC Transregio 128 (Multiple Sklerose). The work of GS is supported by the DFG through the Leibniz Award and the research groups PANDORA FOR2886, CRC1483 (EmpkinS) and CRC1181. GS received further funding from the Bundesministerium für Bildung und Forschung (BMBF) through the Mascara project, the European Union through the ERC Synergy grant 4D Nanoscope and the IMI-funded project RTCure. DM is supported by the DFG through the research groups PANDORA FOR2886, CRC/TRR305 (A03), CRC/TRR221 (A06), RTG2408 (P13) and the grant 536993790.

Competing interests AH has served on scientific advisory boards for Galapagos, Novartis and Merck Serono; received speaker honoraria from Biogen Idec, Merck Serono and Novartis; and received limited research grants from Merck Serono. DB is an employee and shareholder of Kyverna Therapeutics. EF has received speaker honoraria from AbbVie, BMS, Galapagos, Janssen, Lilly, Novartis, Roche, Sanofi and Pfizer. GS has received speaker honoraria from BMS, Cabaletta, Janssen, Kyverna, Miltenyi and Novartis. DM has received speaker honoraria and consulting fees from Abbvie, BMS, Beigene, Celgene, Galapagos, Gilead, Janssen, Miltenyi and Novartis.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

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• Study protocol
• Open access
• Published: 02 July 2024

Effect of norepinephrine and phenylephrine on prothrombotic response in patients undergoing cesarean section under spinal anesthesia: protocol for a randomized, double-blind, controlled study

• Wenhui Tao 1 , 2   na1 ,
• Yufang Xie 3   na1 ,
• Wei Ding 1 , 2   na1 ,
• Jinfeng Bao 3 ,
• Ye Zhang 1 , 2 &
• Xianwen Hu 1 , 2  

Trials volume  25 , Article number:  432 ( 2024 ) Cite this article

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Metrics details

Norepinephrine and phenylephrine are commonly used vasoactive drugs to treat hypotension during the perioperative period. The increased release of endogenous norepinephrine elicits prothrombotic changes, while parturients are generally in a hypercoagulable state. Therefore, this trial aims to investigate whether there is a disparity between equivalent doses of prophylactic norepinephrine infusion and phenylephrine infusion on prothrombotic response in patients undergoing cesarean section under spinal anesthesia.

Sixty-six eligible parturients will be recruited for this trial and randomly assigned to the norepinephrine or phenylephrine group. The “study drug” will be administered at a rate of 15 ml/h starting from the intrathecal injection. The primary outcome are plasma coagulation factor VIII activity (FVIII: C), fibrinogen, and D-dimer levels. The secondary outcomes include hemodynamic variables and umbilical artery blood pH value.

Our study is the first trial comparing the effect of norepinephrine and phenylephrine on prothrombotic response in patients undergoing cesarean section under spinal anesthesia. Positive or negative results will all help us better understand the impact of vasoactive drugs on patients. If there are any differences, this trial will provide new evidence for maternal choice of vasoactive medications in the perioperative period.

Trial registration

Chinese Clinical Trial Registry ChiCTR2300077164. Registered on 1 November 2023. https://www.chictr.org.cn/ .

Peer Review reports

Introduction

Background and rationale {6a}.

Spinal anesthesia promotes systemic vasodilation in patients, which inevitably leads to a redistribution of blood between the core and periphery, and resulting in hypotension. Norepinephrine and phenylephrine are commonly used vasoactive drugs in patients undergoing cesarean section [ 1 , 2 , 3 ]. Numerous studies have compared the regulatory effects of norepinephrine and phenylephrine on perioperative hypotension in patients undergoing cesarean section [ 3 , 4 ], as well as fetal outcomes [ 5 , 6 ]. As a type of α-agonist, phenylephrine is a powerful and rapidly acting vasopressor that can better maintain the acid–base status of the fetus and has replaced ephedrine as the preferred vasopressor for hypotension during cesarean section [ 7 ]. However, phenylephrine is more prone to reflex bradycardia and decrease cardiac output [ 8 ]. Norepinephrine acts as an α-adrenergic agonist and also has a weak β-agonist effect, which has been the frequently used drug for treating hypotension undergoing cesarean section [ 9 , 10 , 11 ].

The coagulation system undergoes physiological changes to reach a hypercoagulable state to prevent severe bleeding during delivery [ 12 ]. Most of the pregnant patients who experienced acute coronary syndrome (ACS) had no coronary artery disease before, and the pathological mechanism involved was mainly non-atherosclerosis [ 13 ]. Meanwhile, the formation of blood clots plays an extremely important role in ACS [ 14 ]. Research has confirmed that the release of norepinephrine promotes blood clotting, and the resulting prethrombotic state may be a vital mechanism for triggering acute coronary artery disease [ 15 ]. However, whether norepinephrine and phenylephrine trigger coagulation changes measured in a laboratory setting and predict the risk of cardiovascular diseases (CVD) has not previously been investigated. The main coagulation molecules selected in this trial include FVIII: C, fibrinogen, and D-dimer, which have been shown to be closely associated with the risk of CVD [ 16 , 17 , 18 ]. The aim of this trial is to compare the prothrombin response of norepinephrine and phenylephrine.

Objectives {7}

The aim of this trial is to compare the prothrombin response of prophylactic infusion of the equivalent dose of norepinephrine and phenylephrine in patients undergoing cesarean section under spinal anesthesia. This study will provide a new theoretical basis for exploring the administration of vasoactive drugs in patients with hypercoagulation state during the perioperative period.

Trial design {8}

This study is a two-arm randomized controlled trial (RCT) with a 1:1 allocation ratio and explores the superiority of two interventions on patients’ prothrombotic response. The intervention will be implemented according to the protocol, with independent researchers conducting and monitoring randomization. Figure  1 shows the Consolidated Standard flow chart for reporting trials.

Consolidated Standards of Reporting Trials (CONSORT) flow diagram

Methods: participants, interventions, and outcomes

Study setting {9}.

This trial plans to recruit patients scheduled to undergo cesarean section, with an age range of 18–40 years. Eligible subjects will be recruited at the Second People’s Hospital of Hefei, a large tertiary hospital in China that performs thousands of cesarean sections each year. Blood samples will be sent to the Key Laboratory of Anesthesiology and Perioperative Medicine of Anhui Higher Education Institutions, Anhui Medical University, for coagulation function testing. Figure  2 is a Standard Protocol Items [ 19 ].

SPIRIT figure-schedule of enrolment, interventions, and assessments. SPIRIT, Standard Protocol Items: Recommendations for Interventional Trials

Eligibility criteria {10}

Inclusion criteria.

Aged from 18 to 40 years

American Society of Anesthesiologists class II or below

Singleton and full-term pregnancy

Voluntarily participate and receive intraoperative intervention

Exclusion criteria

Unable to implement informed consent

Allergy to study drugs

Known fetal abnormality

Mesenteric or peripheral vascular thrombosis

Suffering from severe vital organ diseases

Hypertensive disorders

With any contraindication for spinal anesthesia

Other inappropriate situations considered by the anesthesiologist

Withdrawal or dropout criteria

The patient requests to withdraw.

The patient fails to complete data collection.

Adverse events occur and require treatment.

The subject’s pathological and physiological changes require withdrawal.

The researcher believes that the patient is not suitable to continue.

Information consent {26a}

Elective cesarean sections are performed by an anesthetist who visits the patients the day before the operation to recruit the patient and obtain informed consent. For emergency cesarean sections, informed consent can be signed in the operating theater, prior to anesthesia. Patients can withdraw at any time. Not participating in this trial will not affect the right of parturient to receive routine anesthesia and surgery.

Additional consent provisions for collection and use of participant data and biological specimens {26b}

When signing the informed consent form, participants will be asked if they are willing to donate blood and fetal umbilical artery blood samples, and only patients who agree will be enrolled. Blood samples left over from testing will be destroyed.

Interventions

Explanation for the choice of comparators {6b}.

Norepinephrine group: Norepinephrine will be pumped intraoperatively. Phenylephrine group: Phenylephrine will be pumped during the operation. Norepinephrine and phenylephrine are both vasoactive drugs commonly used in clinical practice, and their use will be beneficial in maintaining the stability of maternal intraoperative circulation.

Intervention description {11a}

Patients who met the inclusion criteria will be recruited until 66 cases. Phenylephrine (100 µg/ml) and norepinephrine (8 µg/ml) will be prepared by assistants who are not involved in this trial. Patients undergo routine vital sign monitoring after entering the operating room. Intravenous access will be secured using an 18G intravenous catheter. Within 15 min before surgery, a rapid intravenous infusion of 5 ml/kg lactate Ringer solution will be administered, and then continue the infusion at a rate of 6 ml/kg/h. The ambient temperature in the operating room is maintained at 22–24 °C. Spinal anesthesia will be implemented using 0.75% ropivacaine hydrochloride 12 mg at the L 2 -L 3 or L 3 -L 4 intervertebral space in a lateral decubitus. The parturient maintains a supine position on the left side at 15 degrees to displace the uterus to the left. The “study drug” will be administered at a speed of 15 ml/h starting with the intrathecal injection.

Administration of the “study drug” ends at the beginning of suturing the skin. The plane of midline sensory blockade will be check by pin prick with blunt tipped needle, and the maximum sensory plane is generally reached within 20 min after spinal blockade.

Circulatory parameters should be conducted every 3 min within the first 15 min after anesthesia. Ephedrine 6 mg should be used for treatment hypotension when the systolic blood pressure is below 90 mmHg. Hypertension is an increase in mean arterial pressure (MAP) > 20% from baseline. Once hypertension occurs, medication infusion should be stopped immediately until MAP returns to below the hypertension. Atropine 0.5 mg can be used for bradycardia (heart rate < 60 beats/min) treatment.

Criteria for discontinuing or modifying allocated interventions {11b}

Interventions will likely be interrupted or modified in the event of adverse events, serious procedural errors, or voluntary patient withdrawal.

Strategies to improve adherence to interventions {11c}

During the informed consent process, patients will be informed of the importance, potential benefits, and possible risks of participating in this study. There will be no additional financial burden associated with the study, and abnormalities in coagulation parameters will be promptly communicated and treated.

Relevant concomitant care permitted or prohibited during the trial {11d}

No concomitant care is prohibited during the trial.

Provisions for post-trial care {30}

Patients will be informed if they have a postoperative coagulation abnormality, and will be reviewed and treated.

Outcomes {12}

Primary outcome.

Plasma coagulation factor VIII activity (FVIII: C)

Secondary outcomes

Blood pressure

Umbilical artery blood pH value

If there are significant abnormalities in the perioperative data of patients, repeated measurements and analysis should be carried out immediately. The obtained data will be verified by two people and input into the computer.

Participant timeline {13}

Blood samples will be taken at two time points, entering the operating theater and suturing the skin, to determine levels of FVIII: C, fibrinogen, and D-dimer.

Sample size {14}

Pre-experimental screening of 20 patients was randomized into two groups and the results showed that the difference in postoperative and preoperative between groups were − 48.82 ± 29.66 (PHE, FVIII: C) vs. − 22.45 ± 40.96 (NE, FVIII: C), − 0.45 ± 0.19 (PHE, fibrinogen) vs. − 0.32 ± 0.14 (NE, fibrinogen), and − 1.220 ± 0.39 (PHE, D-dimer) vs. − 0.93 ± 0.33 (NE, D-dimer), respectively. The significance level ( α ) was set at 0.05 and the power ( β ) at 0.20. Accounting for 10% dropout rate, the corresponding sample size is calculated as 33, 33, and 30 cases in each group, selecting the maximum sample size of 66 in total for this trial. Gpower software version 3.1 (USA) was used to estimate the sample size in this trial.

Recruitment {15}

Sixty-six patients with confirmed full-term pregnancies in the Department of Obstetrics of the Second People’s Hospital of Hefei City will be recruited into one of the groups if they are eligible and the medical staff will inform them of the benefits and risks of the study, and the patients will voluntarily take part in this trial after being fully informed. The Second People’s Hospital of Hefei is a large tertiary hospital in China, where thousands of cesarean sections are performed annually. Recruitment of the first participant took place in October 2023 and is expected to be completed by the end of 2024.

Assignment of interventions: allocation

Sequence generation {16a}.

Subjects will be randomly assigned to receive a phenylephrine or norepinephrine infusion in a 1:1 ratio by a statistical expert using SPSS V.16.0 software.

Concealment mechanism {16b}

Sheets of paper will be labeled with grouping information and placed in sequentially numbered envelopes.

Implementation {16c}

Patient recruitment will be carried out by the anesthetist and drugs will be dispensed by a nurse not involved in this study.

Assignment of interventions: blinding

Who will be blinded {17a}.

Neither the patient nor the anesthetist will be aware of the grouping.

Procedure for unblinding if needed {17b}

Patients will be allowed to be unblinded if they have a serious adverse reaction or the trial ends.

Data collection and management

Plans for assessment and collection of outcomes {18a}.

Preoperative visitors screen enrolled patients and collect informed consent forms. Anesthesiologists will record the primary and secondary outcomes in the case report form (CRF). All CRF will be stored in a locked drawer. All information will be stored on a computer with a password. This trial will also be monitored by two clinical doctors for safety.

Plans to promote participant retention and complete follow-up {18b}

Our team has completed several clinical studies of these patients in advance and is experienced in dealing with recruitment and dislodgement. Considering that thousands of cesarean sections are performed each year, this means that data collection will be completed within 2024.

Data management {19}

Patient data collected by the researcher will be stored in a locked cabinet and electronic files will be kept in a computer with a password.

Confidentiality {27}

Information collected during the course of the research will be in a re-identifiable form and no information generated by this project may be used for any other purpose. Only study investigators will have access to study information.

Plans for collection, laboratory evaluation, and storage of biological specimens for genetic or molecular analysis in this trial/future use {33}

The patient’s venous blood will be drawn into a sodium citrate tube containing 3.8% phosphate. The sample will be centrifuged at room temperature (2000 g, 20 min) to obtain plasma, which will be transferred to polypropylene Eppendorf tubes and stored at − 80 °C until measurement in the Laboratory of Anesthesiology and Perioperative Medicine of Anhui Higher Education Institutes, Anhui Medical University. FVIII: C (pg/ml), fibrinogen (g/l), and D-dimer levels (mg/l) are measured using the enzyme-linked immunosorbent assay.

Statistical methods

Statistical methods for primary and secondary outcomes {20a}.

Our data will be analyzed using SPSS 14 software (USA). The Kolmogorov–Smirnov test will be used to test the normality of the data distribution, and all data will be evaluated for linearity using scatter plots. Normally distributed data will be expressed as the mean with a 95% confidence interval (95% CI) and analyzed by parametric testing (paired t -test). Non-normal distribution parameters will be expressed as median and range. A non-parametric test (Wilcoxon test) was used for analysis. Discrete variables will be analyzed using unpaired Student t -test or Mann–Whitney U test for parametric or non-parametric data. An independent t -test will compare the two groups’ coagulation molecule values. The condition for the significant difference is a P value < 0.05.

Interim analyses {21b}

After the first 30 participants have completed data collection, an interim analysis will be performed and the trial will be terminated if patients in the norepinephrine group have lower coagulation indices than those in the phenylephrine group.

Methods for additional analyses (e.g., subgroup analyses) {20b}

Due to the small sample size and homogeneous gender, no additional analyses beyond the primary and secondary outcome were considered for this study.

Methods in analysis to handle protocol non-adherence and any statistical methods to handle missing data {20c}

Missing data from participants who failed to complete the entire study will not be included in the statistics.

Plans to give access to the full protocol, participant-level data, and statistical code {31c}

This study protocol and anonymized participant datasets are available to other researchers on request.

Oversight and monitoring

Composition of the coordinating center and trial steering committee {5d}.

The clinical study was arranged in the Second People’s Hospital of Hefei, and all coagulation indices were monitored in the laboratory of the Department of Anesthesiology of the Second Affiliated Hospital of Anhui Medical University.

Composition of the data monitoring committee, its role, and reporting structure {21a}

As this randomized controlled exercise trial is not a large, complex, or high-risk clinical trial, we do not consider the DMC setting to be necessary.

Adverse event reporting and harms {22}

Theoretically, extravasation of norepinephrine can cause local ischemia, and the concentrations in this study have been shown to be safe. In the event of an adverse event, it will be recorded and reported to the hospital ethics committee.

Frequency and plans for auditing trial conduct {23}

The trial will be audited by investigators from the Clinical Trial Office of the Second Affiliated Hospital of Anhui Medical University and the Second People’s Hospital of Hefei. They will conduct on-site or remote monitoring in accordance with Good Clinical Practice (GCP) and national regulations.

Plans for communicating important protocol amendments to relevant parties (e.g., trial participants, ethical committees) {25}

This trial will be conducted strictly in accordance with the protocol (version 1.0). Once modifications occur, the revised protocol will be formally submitted to the relevant ethics trial registration authorities. It will also be resubmitted with amendments to the study protocol to Trials . All participants must provide informed written consent before they can take part in the study.

Dissemination plans {31a}

The results of this trial will be submitted and disseminated in the form of a manuscript to the journal for review and publication. Patients with coagulation abnormalities will be informed and given further investigations, assessment, and treatment.

The peripheral vasoconstriction caused by the administration of vasoactive drugs is characterized by a decrease in the redistribution of blood volume from the core to the periphery, thereby maintaining stable blood pressure [ 20 ]. Combined with liquid therapy, phenylephrine has been widely used in the prevention and treatment of hypotension in patients undergoing cesarean section [ 21 , 22 ]. Even in cesarean section surgery for severe preeclampsia, phenylephrine can be safely used in reduced doses [ 23 ]. Norepinephrine is equally effective in treating hypotension during spinal anesthesia as fixed rate infusion and can avoid bradycardia caused by phenylephrine and favorable acid–base distribution in newborns due to the norepinephrine’s β effectively maintaining placental blood flow [ 24 ]. In a word, norepinephrine can safely and effectively maintain maternal hemodynamics without causing adverse events to pregnant women or fetuses [ 25 , 26 ].

CVD is common among women of childbearing age, which has become the main reason for the increased morbidity and mortality of pregnant women [ 27 , 28 ]. The prothrombotic state is a critical cause of acute coronary artery disease, and blood hypercoagulable is believed to be associated with increased release of endogenous norepinephrine [ 15 , 29 ]. In the study, we will investigate the prothrombotic response, which may be differences due to infusion of norepinephrine and phenylephrine.

In this study, unlike previous single doses of norepinephrine and phenylephrine, we used a continuous measured infusion, which allowed tighter control of blood pressure, reduced hemodynamic fluctuations, and minimized anesthetist intervention [ 27 ]. The design of this study was to administer vasoactive drugs through a peripheral vein, in contrast to the prevailing view that vasoconstrictors must be administered via a central venous catheter. A retrospective cohort study evaluated the risk of adverse reactions [ 28 ], including skin necrosis, associated with receiving a peripheral intravenous infusion (20 µg/ml) of norepinephrine through a database. The results found that of 14,385 patients receiving peripheral continuous infusion of norepinephrine, only 5 patients experienced extravasation of the drug and did not require surgical or medical intervention, concluding that there was no significant association between peripheral intravenous norepinephrine infusion and adverse events. Peripheral administration of norepinephrine is recommended [ 29 ]: through a large proximal vein or anterior elbow fossa vein at a concentration not exceeding 32 μg/ml, for a duration not exceeding 12 h, with the infusion site observed every 2 h and an emergency plan in place. In this study, we will configure 8 µg/ml of norepinephrine and the whole procedure will take about 1 h. For safety reasons, we will observe the infusion location once in half an hour and have phentolamine or nitroglycerin on hand to deal with injuries caused by drug extravasation.

Considering that the central norepinephrine projection system is central to fear and anxiety [ 30 ] the patient’s preoperative anxiety and fear may have an impact on the release of norepinephrine. Although we will not assess the patient’s level of anxiety due to personnel limitations, we will give the patients a comprehensive understanding of the entire surgical process before surgery and meet their requirements as much as possible to minimize the interference of the patient’s possible anxiety on the indicators.

Trial status

The trial is ongoing and recruiting. The protocol (version 1.0) was approved on November 1, 2023. Patient recruitment begins in November 2023 and is expected to be completed before December 2024.

Availability of data and materials {29}

Upon completion of the trial, only researchers or teams with ethical approval will have access to the final datasets. The datasets analyzed will be available from the corresponding author upon reasonable request.

Abbreviations

• Phenylephrine
• Norepinephrine
• Cesarean section
• Spinal anesthesia

Cardiovascular disease

Plasma coagulation factor VIII activity

Sheng ZM, Sun HQ, Mao JQ, Liu J, Liang G, Mei Z. Comparative dose-response study on the infusion of norepinephrine combined with intravenous ondansetron versus placebo for preventing hypotension during spinal anesthesia for cesarean section: a randomised controlled trial. Int J Surg (London, England). 2023;110:832–8.

Ikeda Y, Sugiyama T, Shiko Y, Nagai A, Noguchi S, Kawasaki Y, Mazda Y. Association between maternal cardiac output and fetal acidaemia in caesarean delivery under spinal anaesthesia with norepinephrine infusion: a retrospective cohort study. Br J Anaesth. 2023;130(1):e4–7.

Article   PubMed   Google Scholar  

Guo L, Xu X, Qin R, Shi Y, Xue W, He L, Ma S, Chen Y. Prophylactic norepinephrine and phenylephrine boluses to prevent postspinal anesthesia hypotension during cesarean section: a randomized sequential allocation dose-finding study. Drug Des Dev Ther. 2023;17:1547–55.

Article   CAS   Google Scholar  

Guo L, Qin R, Ren X, Han C, Xue W, He L, Ma L, Pan H, Ma S, Chen Y, et al. Prophylactic norepinephrine or phenylephrine infusion for bradycardia and post-spinal anaesthesia hypotension in patients with preeclampsia during caesarean delivery: a randomised controlled trial. Br J Anaesth. 2022;128(5):e305–7.

Article   CAS   PubMed   Google Scholar  

Liu T, Cheng Z, Zou S, Xu C, Pan S, Zeng H, Shan Y, Feng Y, Zhang H. Effect of weight-adjusted phenylephrine, norepinephrine, and metaraminol for elective cesarean delivery on neonatal acid-base status: a randomized controlled trial. Drug Des Dev Ther. 2022;16:3215–23.

Singh A, Jain K, Goel N, Arora A, Kumar P. Neonatal outcomes following prophylactic administration of phenylephrine or noradrenaline in women undergoing scheduled caesarean delivery: a randomised clinical trial. Eur J Anaesthesiol. 2022;39(3):269–76.

Singh PM, Singh NP, Reschke M, Ngan Kee WD, Palanisamy A, Monks DT. Vasopressor drugs for the prevention and treatment of hypotension during neuraxial anaesthesia for caesarean delivery: a Bayesian network meta-analysis of fetal and maternal outcomes. Br J Anaesth. 2020;124(3):e95–107.

Stewart A, Fernando R, McDonald S, Hignett R, Jones T, Columb M. The dose-dependent effects of phenylephrine for elective cesarean delivery under spinal anesthesia. Anesth Analg. 2010;111(5):1230–7.

Sheng ZM, Shen YP, Pan ZB, Zhu M, Sun HT, Liu JP, Qian XW. Comparative study on the manually-controlled variable-rate versus fixed-rate infusion of norepinephrine for preventing hypotension during spinal anesthesia for cesarean delivery. J Clin Anesth. 2022;82:110944.

Chen Y, Xu X, Qin R, Guo L, Ni X. Comparison of crystalloid and colloid co-load combined with norepinephrine prophylaxis on post-spinal anesthesia hypotension during cesarean delivery: a randomized sequential allocation dose-finding study. Front Med. 2023;10:1214598.

Article   Google Scholar  

Mohta M, R L, Chilkoti GT, Agarwal R, Malhotra RK. A randomised double-blind comparison of phenylephrine and norepinephrine for the management of postspinal hypotension in pre-eclamptic patients undergoing caesarean section. Eur J Anaesthesiol. 2021;38(10):1077–84.

Wasserloos A, Thomassen M, Costa SD, Zenclussen A, Tchaikovski V, Hackeng TM, Stickeler E, Tchaikovski SN. Effect of blood loss during caesarean section on coagulation parameters. Thromb Res. 2021;202:84–9.

Samuel R, Alfadhel M, McAlister C, Nestelberger T, Saw J. Coronary events in the pregnant patient: who is at risk and how best to manage? Can J Cardiol. 2021;37(12):2026–34.

Crea F. The key role of thrombosis: focus on acute coronary syndrome, venous thrombo-embolism, and atrial fibrillation. Eur Heart J. 2024;45(1):1–4.

von Känel R, Heimgartner N, Stutz M, Zuccarella-Hackl C, Hänsel A, Ehlert U, Wirtz PH. Prothrombotic response to norepinephrine infusion, mimicking norepinephrine stress-reactivity effects, is partly mediated by α-adrenergic mechanisms. Psychoneuroendocrinology. 2019;105:44–50.

Danesh J, Lewington S, Thompson SG, Lowe GD, Collins R, Kostis JB, Wilson AC, Folsom AR, Wu K, Benderly M, et al. Plasma fibrinogen level and the risk of major cardiovascular diseases and nonvascular mortality: an individual participant meta-analysis. JAMA. 2005;294(14):1799–809.

CAS   PubMed   Google Scholar  

Lowe G, Rumley A. The relevance of coagulation in cardiovascular disease: what do the biomarkers tell us? Thromb Haemost. 2014;112(5):860–7.

PubMed   Google Scholar  

Willeit P, Thompson A, Aspelund T, Rumley A, Eiriksdottir G, Lowe G, Gudnason V, Di Angelantonio E. Hemostatic factors and risk of coronary heart disease in general populations: new prospective study and updated meta-analyses. PLoS ONE. 2013;8(2):e55175.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Chan AW, Tetzlaff JM, Gøtzsche PC, Altman DG, Mann H, Berlin JA, Dickersin K, Hróbjartsson A, Schulz KF, Parulekar WR, et al. SPIRIT 2013 explanation and elaboration: guidance for protocols of clinical trials. BMJ. 2013;346:e7586.

Article   PubMed   PubMed Central   Google Scholar  

Wieruszewski PM, Khanna AK. Vasopressor choice and timing in vasodilatory shock. Crit Care. 2022;26(1):76.

Buthelezi AS, Bishop DG, Rodseth RN, Dyer RA. Prophylactic phenylephrine and fluid co-administration to reduce spinal hypotension during elective caesarean section in a resource-limited setting: a prospective alternating intervention study. Anaesthesia. 2020;75(4):487–92.

Khan E, Wong M, Ibrahim M, Babazade R, Simon M, Mendonca R, Vadhera R. Effect of phenylephrine infusion and spinal anesthesia on cardiac output during cesarean section by point of care echocardiogram: a case series. J Clin Anesth. 2021;75:110474.

Hu LJ, Mei Z, Shen YP, Sun HT, Sheng ZM, Chen XZ, Qian XW. Comparative dose-response study of phenylephrine bolus for the treatment of the first episode of spinal anesthesia-induced hypotension for cesarean delivery in severe preeclamptic versus normotensive parturients. Drug Des Dev Ther. 2022;16:2189–98.

Theodoraki K, Hadzilia S, Valsamidis D, Stamatakis E. Prevention of hypotension during elective cesarean section with a fixed-rate norepinephrine infusion versus a fixed-rate phenylephrine infusion. Α double-blinded randomized controlled trial. Int J Surg. 2020;84:41–9.

de Queiroz DV, Velarde LGC, Alves RL, Verçosa N, Cavalcanti IL. Incidence of bradycardia during noradrenaline or phenylephrine bolus treatment of postspinal hypotension in cesarean delivery: a randomized double-blinded controlled trial. Acta Anaesthesiol Scand. 2023;67(6):797–803.

Liu P, He H, Zhang SS, Liang Y, Gao ZJ, Yuan H, Dong BH. Comparative efficacy and safety of prophylactic norepinephrine and phenylephrine in spinal anesthesia for cesarean section: a systematic review and meta-analysis with trial sequential analysis. Front Pharmacol. 2022;13:1015325.

Siddik-Sayyid SM, Taha SK, Kanazi GE, Aouad MT. A randomized controlled trial of variable rate phenylephrine infusion with rescue phenylephrine boluses versus rescue boluses alone on physician interventions during spinal anesthesia for elective cesarean delivery. Anesth Analg. 2014;118(3):611–8.

Pancaro C, Shah N, Pasma W, Saager L, Cassidy R, van Klei W, Kooij F, Vittali D, Hollmann MW, Kheterpal S, et al. Risk of major complications after perioperative norepinephrine infusion through peripheral intravenous lines in a multicenter study. Anesth Analg. 2020;131(4):1060–5.

French WB, Rothstein WB, Scott MJ. Time to use peripheral norepinephrine in the operating room. Anesth Analg. 2021;133(1):284–8.

White TL, Depue RA. Differential association of traits of fear and anxiety with norepinephrine- and dark-induced pupil reactivity. J Pers Soc Psychol. 1999;77(4):863–77.

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Acknowledgements

The authors are deeply grateful for the support and contributions of patients, surgeons, and nursing staff to this trial.

This trial was funded by Anhui Province University Scientific Research Project (No. 2023AH010081 and No. 2023AH053181), National Natural Science Foundation Incubation Program of The Second Affiliated Hospital of Anhui Medical University (2023GMFY03), and The Scientific Training Program of Clinical Students in 5 + 3 Years Educational System (2023-ZQKY-017).

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Wenhui Tao, Yufang Xie, and Wei Ding are joint first authors and contributed equally.

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Department of Anesthesiology, The Second Affiliated Hospital of Anhui Medical University, Hefei City, Anhui Province, China

Wenhui Tao, Wei Ding, Ye Zhang & Xianwen Hu

Key Laboratory of Anesthesiology and Perioperative Medicine of Anhui Higher Education Institutes, Anhui Medical University, Hefei City, Anhui Province, China

Department of Anesthesiology, The Second People’s Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University, Hefei, Anhui, 230011, China

Yufang Xie & Jinfeng Bao

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WT and YX developed the research questions, hypotheses, conducted the assessment of the latest research progress, conceptualized the methods and analysis, and drafted the manuscript. YX calculated the sample size; JB performed the preoperative interview. WD conceived the method and helped prepare the manuscript. YZ assisted with research methods and improved the manuscript. XH assisted with review. The research initiators XH and YZ designate personnel to collect, manage, analyze, and interpret data, write reports, make the decision to submit reports for publication, and have the ultimate authority over these activities.

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Correspondence to Ye Zhang or Xianwen Hu .

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Ethics approval and consent to participate {24}.

The protocol (version number: V.1.0/20230814) has been submitted for approval by the Institutional Ethics Committee of The Second People’s Hospital of Hefei (ID: 2023–093). The ethics committee of our institution will regularly inspect and supervise the process and data management of this trial. All participants will provide informed written consent prior to their entry into the study.

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Tao, W., Xie, Y., Ding, W. et al. Effect of norepinephrine and phenylephrine on prothrombotic response in patients undergoing cesarean section under spinal anesthesia: protocol for a randomized, double-blind, controlled study. Trials 25 , 432 (2024). https://doi.org/10.1186/s13063-024-08255-x

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Comparing Discrete Choice Experiment with Swing Weighting to Estimate Attribute Relative Importance: A Case Study in Lung Cancer Patient Preferences

J. veldwijk.

Erasmus School of Health Policy & Management, Erasmus University, Rotterdam, the Netherlands

Erasmus Choice Modelling Centre, Erasmus University, Rotterdam, the Netherlands

Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Julius Centrum, Utrecht, the Netherlands

I. P. Smith

Applied Research Division for Cognitive and Psychological Science, IEO, European Institute of Oncology IRCCS, Milan, Italy

S. Petrocchi

M. y. smith.

Alexion AstraZeneca Rare Disease, Boston, MA, USA

Department of Regulatory and Quality Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, USA

R. Janssens

Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium

G. A. de Wit

Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam & Amsterdam Public Health Research Institute, Amsterdam, the Netherlands

C. G. M Groothuis-Oudshoorn

Health Technology and Services Research (HTSR), Faculty of Behavioural Management and Social Sciences, University of Twente, Enschede, the Netherlands

Associated Data

Supplemental material, sj-docx-1-mdm-10.1177_0272989X231222421 for Comparing Discrete Choice Experiment with Swing Weighting to Estimate Attribute Relative Importance: A Case Study in Lung Cancer Patient Preferences by J. Veldwijk, I. P. Smith, S. Oliveri, S. Petrocchi, M. Y. Smith, L. Lanzoni, R. Janssens, I. Huys, G. A. de Wit and C. G. M Groothuis-Oudshoorn in Medical Decision Making

Supplemental material, sj-docx-2-mdm-10.1177_0272989X231222421 for Comparing Discrete Choice Experiment with Swing Weighting to Estimate Attribute Relative Importance: A Case Study in Lung Cancer Patient Preferences by J. Veldwijk, I. P. Smith, S. Oliveri, S. Petrocchi, M. Y. Smith, L. Lanzoni, R. Janssens, I. Huys, G. A. de Wit and C. G. M Groothuis-Oudshoorn in Medical Decision Making

Introduction

Discrete choice experiments (DCE) are commonly used to elicit patient preferences and to determine the relative importance of attributes but can be complex and costly to administer. Simpler methods that measure relative importance exist, such as swing weighting with direct rating (SW-DR), but there is little empirical evidence comparing the two. This study aimed to directly compare attribute relative importance rankings and weights elicited using a DCE and SW-DR.

A total of 307 patients with non–small-cell lung cancer in Italy and Belgium completed an online survey assessing preferences for cancer treatment using DCE and SW-DR. The relative importance of the attributes was determined using a random parameter logit model for the DCE and rank order centroid method (ROC) for SW-DR. Differences in relative importance ranking and weights between the methods were assessed using Cohen’s weighted kappa and Dirichlet regression. Feedback on ease of understanding and answering the 2 tasks was also collected.

Most respondents (>65%) found both tasks (very) easy to understand and answer. The same attribute, survival, was ranked most important irrespective of the methods applied. The overall ranking of the attributes on an aggregate level differed significantly between DCE and SW-ROC ( P  < 0.01). Greater differences in attribute weights between attributes were reported in DCE compared with SW-DR ( P  < 0.01). Agreement between the individual-level attribute ranking across methods was moderate (weighted Kappa 0.53–0.55).

Significant differences in attribute importance between DCE and SW-DR were found. Respondents reported both methods being relatively easy to understand and answer. Further studies confirming these findings are warranted. Such studies will help to provide accurate guidance for methods selection when studying relative attribute importance across a wide array of preference-relevant decisions.

• Both DCEs and SW tasks can be used to determine attribute relative importance rankings and weights; however, little evidence exists empirically comparing these methods in terms of outcomes or respondent usability.
• Most respondents found the DCE and SW tasks very easy or easy to understand and answer.
• A direct comparison of DCE and SW found significant differences in attribute importance rankings and weights as well as a greater spread in the DCE-derived attribute relative importance weights.

As health care systems evolve toward more patient-centered care, there has been an increased interest in using patient preferences to support decision making to optimize care from product and process development to authorization and reimbursement. 1 – 3 Patient preference assessments measure what patients value in their health care and can be used to compare different aspects of care and tradeoffs patients find acceptable. 4 , 5 Patient preferences can be elicited using a variety of methods. 6 , 7

One frequently used method to elicit and quantify patient preferences is a discrete choice experiment (DCE). 6 DCEs are based on the random utility theory and require respondents to answer several choice tasks in which they are presented with multiple alternatives representing different health care options. The alternatives are described using a set of attributes with varying levels. 4 , 8 , 9 From these alternatives, respondents choose the option with the highest personal utility. 9 – 12 Based on the choices respondents make, the impact each attribute has on the utility is estimated, and the relative importance of the included attributes can be inferred from these estimates. 9 , 13 , 14 DCEs can thus be used to prioritize attributes of different care paths, calculate the relative importance of these attributes, and identify potential tradeoffs that patients are willing to make between these different attributes. The validity of DCE findings is well supported. 4 , 15 However, DCEs have been criticized for being complex for both researchers and respondents. First, DCEs require expert knowledge for generating formal experimental designs 16 and running the required complex statistical modelling techniques. 9 Second, DCEs are generally considered to be cognitively burdensome to respondents, making them less than ideal for participants who have cognitive impairments. 17 , 18 In addition, they require relatively large sample sizes, 19 making them inappropriate for administration in, for example, rare disease populations. The combination of expert input and large sample size renders relatively high study costs and long study duration. 18 , 19

Researchers as well as stakeholders who use preference information (i.e., representatives from the pharmaceutical industry and regulatory and reimbursement bodies) have expressed the need to compare DCEs to other, simpler methods. 22 This will help guide method selection for use in patient preference studies that are budget and or time sensitive, conducted in rare disease areas, and for which Marginal Rate of Substitution (MRS) or predicted uptake are not among the required outcome measures (e.g., prioritization of unmet medical needs or endpoint selection for clinical trials).

Swing weighting (SW) has been identified as a “simpler” preference elicitation method and was identified by researchers and other stakeholders as a promising method 23 to be applied when attribute importance is an outcome of interest to inform decision making. 24 In SW tasks, respondents are presented with a list of attributes used to define a health care treatment option. Each attribute on the list shows the “swing” from the attribute’s worst level to its best level (worst and best levels are determined a priori). The participant ranks these swings based on how important improving that attribute is to them. SW tasks are followed by a point allocation (PA) or direct rating (DR) task. In such a task, respondents state the value of each swing either by allocating a fixed number of points (usually out of 100 points) between the “swings” or by directly rating each swing on a standard point scale, with the top-ranked swing automatically receiving the maximum possible number of points (usually 100 points). 25 The results of an SW task can then be used to identify attribute priorities, and the relative importance weights of each ranked swing can be calculated using the proportion of points given to each swing. 26 , 27 This type of rating scale is an often-used way to measure the relative importance, and thus utility, of attributes. While some consider SW as simply a ranking method, 27 others argue that given the application of multiattribute value functions, SW (like DCE) is based on the concepts and axioms described by von Neumann and Morgenstern 28 and is embedded in multiattribute utility theory. 29 , 30 The key difference is that SW does not include a “random” component as choices in SW are deterministic in nature. 31 This enables researchers to directly capture relative attribute weights at an individual level (whereas for a DCE, the relative importance weights are estimated as a secondary outcome available only after applying econometric modeling) and can be done with smaller sample sizes and a greater number of attributes compared with DCE studies. 26 , 31 SW also does not require a formal experimental design, making them easier to develop, and they are believed to be cognitively easier to complete than a DCE task. 26 , 31

While both DCE and SW have been implemented in health care preference research, empirical evidence directly comparing DCE and SW outcomes in terms of attribute relative importance and ease of comprehension and completion is largely lacking. 31 , 32 Where some studies compared DCE to other methods in different clinical settings (e.g., DCE versus ordered categorical, 33 DCE versus best-worst-scaling, 34 – 36 or DCE versus thresholding, 37 , 38 this study aimed to address this gap in knowledge by empirically comparing DCE and swing weighting with direct rating (SW-DR)–derived attribute relative importance rankings and weights. Since both methods claim they can be used to determine attribute relative importance rankings and weights, applying them to a similar research question should result in comparable estimates.

Study Context and Ethics

The outcomes of a study assessing the preferences of Italian and Belgian patients with non–small-cell lung cancer (NSCLC) for treatment was used for this comparative analysis. Details on the study design have been published elsewhere. 39 , 40 This case study was identified as suitable for the comparison of DCE and SW-DR due to the potentially fragile physical state or diminished cognitive status of the patients. 41 – 44 The current study included DCE and SW exercises in the ways these methods typically would be applied to answer a particular clinical research question. The study was approved by the Ethical Committee of the European Institute of Oncology IRCCS (IEO, Milan, Italy; reference R1142/20-IEO 1206) and the Ethische Commissie Onderzoek UZ/KU Leuven (Belgium; reference S63007).

Respondents and Recruitment

Patients with NSCLC were recruited through clinical partners in Italy and Belgium. Respondents were selected and referred to the PREFER research team by the treating oncologists at cancer treatment centers in Belgium and in Italy. 40 To be eligible, patients had to understand Italian or Dutch, be 18 y or older, and have a histologic or cytologic diagnosis of NSCLC as evaluated by clinicians. Patients were not eligible if they (as evaluated by the clinician): 1) had cognitive impairments rendering the participant incapable of informed consent or 2) were unable to understand the study materials.

Attribute and Level Selection

Attributes and levels were identified and refined according to best practices and guidelines. 9 , 45 – 47 This included a literature review, 6 nominal group technique–based focus groups in Italy and Belgium with NSCLC patients, 48 , 49 and a multistakeholder discussion with clinicians and preference experts. 50 Five attributes with 3 levels each were identified as relevant for the study (see Table 1 ).

Attributes and Levels Included in the Discrete Choice Experiment and the Swings Used in the Swing Weighting

DCE Experimental Design

A Bayesian D-efficient design consisting of 2-unlabeled alternative-forced-choice tasks was constructed for the DCE using Ngene (ChoiceMetrics, Sydney, Australia). 16 , 51 A total of 36 unique choice tasks were generated, which were divided over three 12-choice task blocks. Respondents were randomly assigned to complete 1 of those blocks. Attribute prior information for DCE design optimization was generated using previously published literature and best guesses. The survey was pilot tested among respondents in Italy ( N  = 50), with the outcomes of a conditional logit model used to inform the final experimental design. Interactions between the attributes “5-y survival” and, respectively, “Risk of long-lasting skin problems,”“Risk of extreme tiredness,” and “Mode of administration” were accounted for in this design. An example of a DCE choice task can be found in Figure 1 .

Illustration of survey elements. (A) Swing weighting (SW) ranking task in which respondents sort the swings in attributes from worst level to best level by priority for improvement in a treatment. (B) SW direct rating task in which patients rate the swings relative on a scale from 0 (not at all important) to 100 (as important as the most important improvement). (C) Discrete choice experiment choice task in which respondents choose their most preferred treatment (pop-up shown to explain risk attribute).

An SW-DR task was developed using the same attributes and levels used in the DCE. In the SW section, respondents were first asked to choose the attribute they preferred to swing from the lowest (worst) to the highest (best) level. Respondents were asked to rank all other swings subsequently from most to least preferred (see Figure 1A ). The order in which the swings were presented was randomized in this section. In the DR section, respondents were asked to rate each of the swings relative to the others by giving it between 0 and 100 points, except for the highest ranked swing, which automatically received 100 points 25 (see Figure 1B ). This reflects the relative valuation of the importance of the different swings. Respondents were instructed on what this relative rating means as follows: “If you give 50 points to improve a feature, it means that you think improving it is half as important as improving the top ranked attribute because you gave it half as many points.” This unrestricted valuation is assumed to be simpler for respondents than PA from a fixed pool and has been found to be more reliable than restricted PA methods, 52 – 55 making it more suitable for this study population, who may have more fragile physical states or diminished cognitive status. 41 – 44

Both the DCE and SW-DR tasks were included as parts of a one-time online survey with respondents able to pause and return to the survey. The survey was programmed in Sawtooth software (lighthouse studio 9.13) and consisted of 6 parts. First, respondents were informed about the study and provided consent for data collection prior to answering sociodemographic and medical history–related questions. Second, respondents watched 2 different educational videos consisting of text and animations with voiceovers giving 1) an introduction with information on lung cancer and detailed descriptions of the attributes and levels included and (2) instructions on how to complete the first-choice task. Third, respondents were randomly assigned to receive either the DCE or SW task first to avoid any ordering effects. Fourth, respondents completed quality of life–related questions (EQ-5D). 56 , 57 Fifth, respondents watched a video with instructions on how to complete the second choice task. Finally, respondents were asked to complete psychosocial measures including measures of health literacy. 58 , 59

After each choice task, respondents were given 2 feedback questions about ease of understanding and answering the choice tasks on a 5-point Likert-type scale ranging from very easy to very difficult. The survey was pretested with 5 lung cancers patients in think-aloud interviews.

Statistical Analysis

Contrary to common practice in applied preference research, in this study, only surveys of respondents who completed both DCE and SW choice tasks were included in the analysis to facilitate within-person comparisons. One respondent was excluded from the data set due to flatlining behavior (defined as always choosing A or always choosing B). Statistical analysis was performed with Nlogit version 6 and R version 4.0.4. A significance level of P  < 0.05 was used for all analyses. All analyses were performed separately for data from Italy and Belgium to ensure most accurate methods comparison measures and avoid conflating potential scale heterogeneity between countries. 60

Respondent background characteristics

Respondent background characteristics (including general demographic and medical history information) were categorized and are presented as counts with percentages.

DCE analysis

Random parameter logit models (RPLs) were used to analyze the DCE data. Such models adjust for the fact that panel data were collected and adjusted for the multilevel structure of the data. 9 , 13 In addition, these models allow to include attribute (levels) as random parameters to adjust for the effect of preference heterogeneity. 9 , 13 All risk and benefit attributes were assumed to be linear, and the categorical attributes were dummy coded. The significance level of the standard deviation of the attributes was used to test which attributes should be included in the final model as random parameters (assuming normal distributions) to account for preference heterogeneity. The utility equation below formulates the outcomes of these procedures and displays the final utility model tested in the analysis. The systematic utility component (V) describes the measurable utility of a specific treatment based on the attributes included in the DCE. The β 1 –β 7 coefficients represent the attribute-level estimates indicating the relative importance of each attribute level for individual i . A constant term was included in model exploration (i.e., to test for reading order bias), but it was found to be insignificant and removed from the final model.

A choice task-order variable was included in the model as an interaction term with the attribute levels to test whether the task order (i.e., DCE first or SW first) influenced the outcomes, which turned out insignificant. Prespecified interaction terms that significantly contributed to model fit (as assessed using a log-likelihood [LL] ratio test) were included in the model. Individual specific conditional parameter estimates were estimated for each respondent using the final model. Individual attribute weights and rankings were calculated with these parameter estimates (by calculating the total impact of each attribute on utility and standardizing to a total of 100, where the highest weight represents highest rank) and averaged to estimate the mean population weights and rankings.

SW analysis

The SW analysis was performed by analyzing the patients’ rankings of the attributes and the points allocated to the different attributes. The individual attribute relative importance weights were calculated using both the rank-ordered centroid (ROC) weight method and the DR weight method per patient. The ROC weight method calculates a relative weight representing the distance between adjacent ranks on an ordinal or normalized scale. 61

The ROC weight for an attribute with rank i equals (in case of 5 attributes):

The DR method is used to generate individual proportional weights for an attribute with rank i and allocated points p i and equals (in case of 5 attributes):

These individual weights were averaged over all patients per country to obtain the average weights, which are the equivalent of the attribute relative importance weights resulting from DCEs.

Comparison between methods

Respondent feedback.

Frequencies and chi-square tests were conducted to compare the feedback of respondents regarding their perceived difficulty in understanding and answering the DCE and SW questions.

Comparing attribute importance ranking

Based on the outcomes of the RPL of the DCE and the SW-ROC, attribute ranking was compared. Ranking agreement (based on individual-level estimates from the DCE and SW-ROC) was evaluated with Cohen’s weighted kappa, which measures interrater reliability while accounting for chance similarities in rating. 62 , 63 Differences in the ranking based on DCE and SW-ROC were analyzed and tested with an ordered logit model. 64

Comparing attribute importance weighting

Based on the outcomes of the RPL of the DCE and the SW-DR, attribute weighting was compared. Differences in the weighting based on DCE and SW-DR were analyzed and tested using Dirichlet regression models. 65 Dirichlet regression models can be regarded as a generalization of beta regression models for proportions and percentages and are particularly suited for the analysis of compositional data (i.e., for weights that add up to 1). 66 In a Dirichlet regression model, the aggregate attribute weights are assumed to be distributed with a Dirichlet distribution with parameters µ i , i  = 1,…, 5, mean attribute weights that add up to 1, and a precision parameter ϕ (according to the so-called alternative parametrization). 67 The mean attribute weights are modeled with a logit link function similar to logistic regression:

Here, the logit of μ for individual i is equal to the linear predictor η and is modeled with an intercept β 0 , i , representing the DCE, and with a dummy variable D SW for the method as covariate. We defined the attribute 5-y survival as the base category, with β 0 , survival = β 1 , survival = 0 . In this way, the corresponding values of µ i equal

The precision parameter is modelled with a log link function with method as covariate:

The parameter estimates β 1,i can be interpreted as odds ratios after exponentiation relative to survival as base category. 66 Maximum likelihood estimation is used for obtaining the parameter estimates. 68 Finally, covariates were added to the models to correct for possible effects of method, for educational level, health literacy, gender, age, cancer stage, and treatment history.

Demographics

A sample of n  = 307 NSCLC patients was obtained from N  = 560 requests to patients ( n  = 159 declined invite; n  = 94 withdrew consent). No significant differences were found between the countries in respondents’ gender, age, cancer stage, or family history of cancer. Respondents in both countries differed significantly in family and relationship status, χ 2 (3) = 8.1, P  = 0.045; education level, χ 2 (2) = 7.248, P = 0.027; and health literacy, t (305) = −6.591, P  < 0.001. Patient demographic information can be found in Table 2 .

Demographic Characteristics of the Sample

Respondent Feedback

Most respondents found the DCE and SW tasks very easy or easy to understand and answer (74.6% and 64.5% for DCE and 73.0% and 69.7% for SW, respectively, in Italy and Belgium). The ease of understanding and answering the DCE and understanding the SW task was associated with educational level, with those who had higher levels of education reporting greater ease ( P  < 0.001).

Comparing Attribute Importance Ranking

Table 3 shows attribute ranks for the 2 methods separately per country ( Appendix Tables A1 and A2 show the original attribute-level estimates of the DCE and the ROC and DR estimates of the SW that were used for these calculations). Five-year survival was the most important attribute for most of the respondents, irrespective of the method. Agreement between the ranking of the DCE and SW-ROC was moderate with weighted Kappa correlation coefficients varying between 0.53 and 0.55. Despite the similar ranking of the 5-y survival and tiredness attributes, the overall ranking of the attributes differed significantly between DCE and SW-ROC tasks for both countries (χ 2  = 2042.9, 4 df, P  < .0001 for Italy; χ 2  = 1932.5, 4 df, P  < .0001 for Belgium; Table 4 ). For the Italian respondents, the attributes of mode and hair swapped their rank (third or fifth) depending on the method. For the Belgian respondents, the attributes of mode, skin problems, and hair changed ranking between being third, fourth, or fifth most important.

Attribute Rank and Weight (95% Confidence Interval) Based on DCE and SW-DR and Rank and Weight ( s ) for SW-ROC Separately for Italy and Belgium

CI, confidence interval; DCE, discrete choice experiement; s , standard deviation; SW-DR, swing weighting with direct rating; SW-ROC, swing weighting with rank order centroid method.

Rank-Ordered Logit Model a Beta Parameters (Mean and SE) Comparing SW-ROC and DCE Including Likelihood Ratio Test and Dirichlet Regression Odds Ratios (SW-DR Compared with DCE and 95% CI) Including the Dispersion Parameter (Ln Phi)

Comparing Attribute Importance Weighting

The weights of all the attributes differed substantially between DCE and SW-DR ( Table 3 and Figure 2 ). The largest difference was found for the weight of “5-y survival,” which was much greater for the DCE (59%–63% of total weight) than for the SW-DR methods (31%–33%). The differences in the weights are evidenced in their 95% confidence intervals, which minimally overlap between methods ( Table 3 ). The less important attributes had different weights but were more comparable across methods.

Relative attribute ranking and weights for Belgium (A) and Italy (B) calculated based on the discrete choice experiment (DCE) data and the swing weighting (SW) data, using both direct ranking (DR) and rank-ordered centroid (ROC).

The outcomes of the Dirichlet regression models are shown in Table 4 . The odds ratio refers to the attribute weights of all attributes relative to 5-y survival of the SW-DR (with the DCE being considered the base case). The aggregate attribute weights of the DCE and SW-DR were significantly different (LL ratio = 466.4 for Italy, P  < 0.0001; LL ratio = 435.0 for Belgium, P  < 0.0001). Weights of the SW-DR were more equally divided over the included attributes as compared with the DCE (in the DCE, most of the weight was allocated to the 5-y survival attribute; Figure 3 ). Relative to survival, the attribute importance weights calculated from the SW-DR for skin problems, mode of administration, tiredness, and hair problems were significantly larger compared wit the DCE weights ( P  < 0.001). Moreover, for Italy, the weights based on the SW-DR were significantly less dispersed (i.e., weighted more equally) compared with the DCE (ϕ = 0.75, CI: 0.64–0.87; P  < 0.001). These differences remained highly significant even after correcting for educational level, health literacy, gender, age, cancer stage, and treatment experience.

Comparison of the rankings derived from the ROC and DCE in Italy (A) and Belgium (B), and the attribute weighting from the point task and the DCE in Italy (C) and Belgium (D).

This study used empirical evidence to compare the relative importance of NSCLC treatment profile attribute ranking and weighting when assessed using a DCE or SW-DR task. Significant differences were found in the relative ranking and weights of the attributes between the SW-DR and the DCE. Similar results were found in the 2 countries included in this study, supporting the theoretical validity of these study outcomes. In addition, respondents generally indicated that both DCE and SW-DR tasks were easy or very easy to understand and answer.

The difference in relative attribute weights and ranking is likely in part due to the differences in how the 2 methods assess patient preferences and how respondents engage with the tasks. In an optimally designed DCE, respondents are forced to weigh all attributes when choosing and cannot directly state their individual attribute valuations. In contrast to this multiattribute nature of a DCE, in which the total utility of all attributes guides choices, the SW-DR method is unrestricted, allowing respondents to assign any number of points to attributes (excluding the most important attribute, which automatically receives 100 points). 25 This may have induced an equivalence bias, leading to a relative undervaluing of the more important attributes and overvaluing of the less important attributes. 53 The potential presence and impact of equivalence bias in SW experiments should be tested in future research, as the current study was not powered to test conclusions in this regard. Nevertheless, a small explorative post hoc add-on study was conducted (see Appendix B ) to explore whether a restricted PA task (forcing respondents to consider all attributes in assigning points) results in more equivalent relative importance weights to the DCE than the unrestricted DR task. In this study, 14 (randomly selected) Italian patients who previously completed the full survey were asked to complete the SW-DR task from the original survey as well as an additional restricted PA task. Respondents were asked to divide a total of 100 points over 5 attributes rather than simply rate each swing on a 100-point scale, thus forcing respondents to account for all attributes when allocating points. 26 While small and underpowered due to the explorative nature of this study, the results indicate that weights based on this restricted PA task more closely resemble the DCE study outcomes than those from the unrestricted DR task, which replicate previous findings 53 (see Appendix B ). Further studies are needed to confirm if findings from this exploratory analysis hold with larger samples, different sample composition, and different choice contexts to see whether differences remain and compare the outcomes with DCE outcomes.

Surprisingly, respondents did not report the SW-DR method being easier to understand and answer compared with the DCE. While, on one hand, this supports the use of SW-DR in future research on treatment preferences in similar patient populations, it does not favor this method over the DCE. Contrary, one could question whether DCE choice tasks really are as difficult as previously has been assumed. Respondents might be perfectly capable to accurately complete such choice tasks, which would “call for a partial change in perspective toward this method as being (too) complex and time consuming to complete.” 37 In part, this might be affected by the steep increase in the use of DCEs to elicit preferences, 6 which has undoubtfully led to increased familiarity among researchers with accurate design and conduct of DCE studies. Given that the SW method is relatively unexplored, this calls for further investigation into how best to design such studies, with specific attention for the validity and reliability of this method in studies aiming to measure attribute relative importance ranking and weights. While awaiting this evidence, the current study outcomes support the use of DCE over SW-DR in preference assessment.

A primary strength of this study is that the empirical evidence used to compare the 2 methods was generated in a 1-time survey of NSCLC patients who completed both methods, allowing for direct comparison of results. The within-subjects design reduced the chance of confounding factors playing a role in different preference outcomes. This survey was developed after an extensive qualitative study in close collaboration with a multidisciplinary team of clinicians, patients, and researchers. The tasks were explained using informational videos designed for the study, and the online setting allowing respondents to pause the educational material or the survey and return to it at a later time in. The online setting also allowed for multicountry, location-independent data collection and access for those with more serious disease complications or fatigue to participate, increasing the generalizability of the findings to other NCSLC populations and reducing the chance of bias.

However, this study also had some limitations. First, SW tasks were originally designed to be conducted in person via a trained facilitator. 26 , 31 The current study was administered online, with respondents completing the survey on their own. While online surveys are less costly and time-consuming than interviewer-led studies and SW surveys have previously been done online, the presence of an interviewer allows for assistance and clarification of questions or issues that could arise while the participant is completing the choice task. 69 This can be especially helpful when attributes are complex or the target population experiences cognitive impairments. 31 The patient feedback questions indicated that the online setting was not a problem for this study. Second, the sample was composed of relatively old and “fragile” NSCLC patients, reducing generalizability to younger or less fragile patient populations. Generalizability is also limited by the fact that the digital format of the survey may have discouraged those patients with lower digital literacy from participating as well as those who lack access to computer equipment or to the internet. 70 Third, the current study focused on medical decision making along the medical product life cycle, which did not include clinical or shared decision making. Because other outcome measures and potential methodological considerations might be important when selecting a preference method, the current findings might have limited generalizability toward those situations. Finally, it is unclear whether patients received support from relatives while completing the survey. If this occurred, those supporting the patient in completing the survey could have influenced the outcomes of the survey such that the values measured did not solely reflect the true values of the patient.

In conclusion, this study found significant differences in attribute importance between DCE and SW-DR as well as a greater spread in the DCE-derived relative importance of the attributes. Respondents reported both methods being relatively easy to understand and answer. Further studies confirming these findings as well as SW studies with restricted PA tasks are warranted to enable the provision of accurate guidance for methods selection when studying relative attribute importance across a wide array of preference-relevant decisions. Such studies will contribute to the knowledge base around the validity and reliability of SW in health preference assessment, support guidance for good research practices when using this method, and help researchers decide which method to use when assessing attribute relative importance ranking and weights. While awaiting this evidence, the current study outcomes support the use of DCE over SW-DR in preference assessment.

Supplemental Material

Acknowledgments.

The authors of this article would like to acknowledge the following people for their contributions to the study: the patients for participating and sharing their valuable insights in the interviews and survey; Luca Bailo for his work in developing the study and writing the protocol; Gabriella Pravettoni for her aid in recruiting patients; Reinhard Arnou for his assistance in conducting and analyzing the patient discussions; Elise Schoefs for her help with the development of the survey and the recruitment of patients; our clinical partners in the Department of Respiratory Oncology, (KU Leuven), Department of Thoracic Surgery (KU Leuven), Thoracic Surgery Division (IEO, Milan), Department of Medical Oncology (Fondazione IRCCS Istituto Nazionale dei Tumori, Milan), and the Department of Medicine Section Hematology/Oncology (University of Chicago) for their contributions throughout the survey development and patient recruitment; and the PREFER consortium for their advice and guidance throughout the study.

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: J. Veldwijk, I. P. Smith, S. Oliveri, S. Petrocchi, L. Lanzoni, Isabelle Huys, Rosanne Janssens, Ardine de Wit, and C. G. M. Groothuis-Oudshoorn declare no conflict of interest. M. Y. Smith is a full-time employee of Alexion AstraZeneca Rare Disease and is a shareholder in the company. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study formed part of the PREFER project. The Patient Preferences in Benefit-Risk Assessments during the Drug Life Cycle (PREFER) project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 115966. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation program and EFPIA. The PREFER project aims to strengthen patient-centric decision-making through evidence-based recommendations guiding stakeholders on how and when patient preference studies should inform medical product development and evaluation. Financial support for this study was provided entirely by a grant from Innovative Medicines Initiative 2 Joint Undertaking under grant No. 11966. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report.

Disclaimer: This article and its contents reflect the view of the presenter and not the view of PREFER, IMI, the European Union or EFPIA.

Contributor Information

J. Veldwijk, Erasmus School of Health Policy & Management, Erasmus University, Rotterdam, the Netherlands. Erasmus Choice Modelling Centre, Erasmus University, Rotterdam, the Netherlands. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Julius Centrum, Utrecht, the Netherlands.

I. P. Smith, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Julius Centrum, Utrecht, the Netherlands.

S. Oliveri, Applied Research Division for Cognitive and Psychological Science, IEO, European Institute of Oncology IRCCS, Milan, Italy.

S. Petrocchi, Applied Research Division for Cognitive and Psychological Science, IEO, European Institute of Oncology IRCCS, Milan, Italy.

M. Y. Smith, Alexion AstraZeneca Rare Disease, Boston, MA, USA. Department of Regulatory and Quality Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, USA.

L. Lanzoni, Applied Research Division for Cognitive and Psychological Science, IEO, European Institute of Oncology IRCCS, Milan, Italy.

R. Janssens, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium.

I. Huys, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium.

G. A. de Wit, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Julius Centrum, Utrecht, the Netherlands. Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam & Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.

C. G. M Groothuis-Oudshoorn, Health Technology and Services Research (HTSR), Faculty of Behavioural Management and Social Sciences, University of Twente, Enschede, the Netherlands.