Avoid common mistakes on your manuscript.
Healthcare is under heavy pressure due to rising expenditures, an ageing population, high prices of new medical treatment options, and a substantial resource waste every year [1,2,3,4]. Accurate prediction of patients’ choice behaviour enables better decisions in healthcare by avoiding poor policy decisions, trial-and-error implementation and demand–supply imbalance [5, 6]. Choice modelling via discrete choice experiments (DCEs) has demonstrated significant potential to accurately predict patients’ choice behaviour [7,8,9]. A DCE is a stated preference method and is also referred to as a stated choice experiment, choice-based conjoint or simply a choice experiment. In a DCE, respondents are confronted with a series of carefully tailored hypothetical choice situations entailing two or more options, each characterised by a bundle of attributes with given attribute levels [10, 11]. DCEs have a solid foundation in random utility theory [12, 13] and include a Nobel prize-winning econometric approach commonly referred to as choice modelling [14]. The stated choices allow the ranking of all possible options (i.e. all combinations of attribute levels), including options not presented to respondents. Choice modelling has demonstrated its usefulness in various disciplines, including marketing, transport and environmental economics, where it is mainstream for transport planning and policy development, marketing product development and pricing, and resource management decision-making [15, 16]. It is therefore not surprising that choice modelling has become commonly used in health economics as well [17, 18].
To support policy development based on choice modelling, external validity of stated preferences is widely recognised as an important research question [17, 19,20,21]. External validity is here defined as the degree of consistency of stated choices with actual choice behaviour [so-called 'revealed (true) choices' or ‘actual utilization’]. Despite the strong basis for internal validity [8, 22, 23], some of the usual assumptions made in choice modelling via DCEs may limit external validity. Extant choice models assume that choice processes are independent of the influence of people other than the decision-maker (in healthcare, for example, the patient, the physician, the healthcare consumer) in question, which is the almost universal practice in choice modelling applications in health, transport, marketing and environment. However, most healthcare choices are not made in a social vacuum! Thus, current choice models in healthcare (and beyond) fall short in terms of realistic representation of decision-makers’ choice context [24]. Moving towards a social interdependent choice paradigm in a rigorous and theoretically sound manner is crucial for choice modelling to be useful for health policy development.
It is surprising that relatively little research has been focused on integrating social influences into healthcare choice models [25], especially given the importance of such choices in the overwhelming majority of health domains [22, 26]. Important questions include the following: Which key influencers (i.e. agents from the patient’s own social network) are involved in common healthcare decisions? What decision rules are used? How do we measure and model stated choices for socially interdependent decisions to improve prediction of choice behaviour in healthcare? When and to what extent does taking social influences into account lead to more accurate choice behaviour prediction at an aggregate as well as an individual level?
The answers to these questions require research at the interface of sociology, (behavioural) economic, econometric, psychological and health sciences. Therefore, combining a mixed-method approach with researchers from different disciplines is essential, allowing these urgent and relevant questions to be answered, and so pave the way towards accurate prediction of healthcare choices. INTERSOCIAL—a public research initiative—has recently been launched to tackle these challenges [27, 28].
1 INTERSOCIAL: Aims, Objectives and Deliverables
INTERSOCIAL is an acronym for the research project ‘Towards a Social Interdependent Choice Paradigm for Ex-Ante Evaluation of Healthcare Policies’ [27]. This initiative focuses on generating and validating a social-interdependent choice paradigm to integrate social influences into choice models in healthcare. INTERSOCIAL is a 5-year project funded by the Dutch Research Council (NWO). The consortium of INTERSOCIAL includes eight academic institutions from Australia, The Netherlands, UK and US, one National Health Institute, and one international patient organisation, all adding their experience and perspectives to the project.
The main aim of INTERSOCIAL is to develop and validate a new choice paradigm, called the Social Interdependent Choice Paradigm. It is hypothesised that this paradigm will lead to more accurate predictions of choice behaviour in healthcare than current extant paradigms permit, and so advance the state-of-the-art to a point where more accurate ex-ante evaluation of health policies is feasible. To reach this main aim, INTERSOCIAL is divided into three interrelated phases, each having its own general objective(s) involving frontier research at the interface between sociology, (behavioural) economic, econometric, psychological and medical sciences.
Phase I (‘Theory Development’) will develop a theory of socially interdependent decision-making, primarily to achieve accurate predictions of choice behaviour in healthcare. This phase aims to obtain in-depth insights and answer the following questions: How and to what extent are patients’ preferences influenced by their own social network in common healthcare decisions? How do we classify these influences and decisions? What kind of influence does each agent of that social network exercise? Which information and exogenous factors play a role? What decision rules are used? A three-step approach will be used. Step 1 conducts a systematic review to generate a variety of (non-)overlapping insights into socially interdependent decision-making, starting with the process (who, what, when) and how to measure stated preferences for such a decision. Step 2 conducts focus groups and semi-structured interviews among decision-makers (in this case, patients) and their influencers (e.g. partner, physician) to fill remaining gaps detected in step 1 and to unravel the underlying mechanisms differentiating socially interdependent processes that impact choice behaviour. Step 3 consolidates the findings and formulated hypotheses of steps 1–2 via the Delphi method among experts from different medical fields.
Phase II (‘Method Paradigm’) will translate the theory developed in phase I into a social interdependent choice method paradigm. This phase (1) generates conceptual models for common choice processes in healthcare; (2) determines data requirements for modelling choice behaviour prediction in healthcare; (3) develops a framework for collecting/measuring social interdependent decisions; (4) develops choice models reflecting social interdependence, including mechanisms of influence; and (5) determines calibration techniques to go from stated choices to revealed choices in a reliable way. Phase II of the project is an important distinguishing mark of the work, in that the measurement, statistical tools and methods will be directly suggested by and aligned with the theoretical propositions from phase I (i.e. theory driven).
Phase III (‘Proof-of-Principle’) will empirically validate the Social Interdependent Choice Paradigm developed in phase II and aim to determine when and to what extent revealed choice behaviour is predictable in healthcare. Specifically, (1) surveys among decision-makers (patients) will be conducted that contain socially embedded DCEs (SE-DCEs) that are designed according to state-of-the-art knowledge and insights from phases I–II. (2) Field data will be collected among the same respondents who filled out the survey of phase III, which is the only way to test to what extent revealed choice behaviour is consistent with SE-DCE–derived predictions at an individual level. This will show whether the Social Interdependent Choice Paradigm leads to more accurate choice behaviour prediction at an aggregate and individual level (i.e., hypothesis testing). (3) Telephone interviews, based on the I-Change model [29, 30], will take place with respondents that show discordance between stated choices and revealed choices despite the Social Interdependent Choice Paradigm, but also with a “successful” sample. The first group will permit us to figure out possible reasons for not predicting well. The second group permits us to figure out if the reasons for predicting well are due to the innovations developed by the INTERSOCIAL consortium. Based on phases I–III findings, a guideline will be provided about when and to what extent patient choice behaviour in healthcare is predictable, including improvements in DCE studies to reach more accurate ex-ante evaluation of healthcare policies.
2 INTERSOCIAL: Ambitions and Impact
INTERSOCIAL is designed to make a range of important contributions to the fields of choice modelling, choice behaviour prediction in healthcare, and health policy development. First, INTERSOCIAL is unique worldwide and highly innovative. The rigorous approach followed in INTERSOCIAL to include social influence in choice modelling will move substantially beyond earlier attempts [25, 31]. The project will take a variety of (non-)overlapping insights from different fields into account and integrate cross-domain sciences to fill important gaps in predicting choice behaviour in healthcare. It will advance theoretical aspects and provide a deeper understanding of the underlying mechanisms between derived preferences, social influences and choice behaviour, which has the potential to positively impact both decision-making and preference elicitation in healthcare (and beyond). Second, INTERSOCIAL is the first project ever that will empirically and thoroughly test whether and when choice behaviour can be predicted using SE-DCE in real-life healthcare settings. Third, INTERSOCIAL will lead to methodological advances that are needed to develop valuable choice behaviour prediction models and evolve current practice, both due to its broader scope and closer emulation of what happens in the real-world. More generally, the proposed research is ground-breaking as it develops tools that have the potential to reliably predict choice behaviour, and so advance the state-of-the-art to a point where robust ex-ante evaluation of health policies is feasible. A successful conclusion of this project will open new horizons, not only for decision-making in healthcare, but also for other fields that use choice modelling.
In summary, the ambition of INTERSOCIAL is to pave the way towards accurate prediction of healthcare choices through incorporation of social influences. The strongest demonstration of the value of INTERSOCIAL will come from acceptance of its tools for those who investigate choices (academics) and for those who make choices in healthcare at a macro or micro level (policy makers, patients, clinicians, care insurance companies and pharmaceutical companies).
References
OECD and European Union. Health at a Glance: Europe 2018; 2018.
Bloom DE, Chatterji S, Kowal P, Lloyd-Sherlock P, McKee M, Rechel B, Rosenberg L, Smith JP. Macroeconomic implications of population ageing and selected policy responses. Lancet. 2015;385(9968):649–57.
Watkins JB. Affordability of health care: a global crisis. Value Health. 2018;21(3):280–2.
West LM, Diack L, Cordina M, Stewart D. A systematic review of the literature on ‘medication wastage’: an exploration of causative factors and effect of interventions. Int J Clin Pharm. 2014;36(5):873–81.
Zucchelli E, Jones A, Rice N. The evaluation of health policies through microsimulation methods. Int J Microsim. 2012;5(1):2–20.
Terris-Prestholt F, Quaife M, Vickerman P. Parameterising user uptake in economic evaluations: the role of discrete choice experiments. Heal Econ (United Kingdom). 2016;25(Suppl 1):116–23.
de Bekker-Grob EW, Donkers B, Bliemer MCJ, Veldwijk J, Swait J. Can healthcare choice be predicted using stated preference data? Soc Sci Med. 2020;246:112736.
de Bekker-Grob EW, Swait J, Kassahun HT, Bliemer MCJ, Jonker MF, Veldwijk J, Cong K, Rose JM, Donkers B. Are healthcare choices predictable? The impact of discrete choice experiment designs and models. Value Health. 2019;22(9):1050–62.
Quaife M, Terris-Prestholt F, Luca Di Tanna G, Vickerman P. How well do discrete choice experiments predict health choices? A systematic review and meta-analysis of external validity. Eur J Health Econ. 2018;19:1053–66.
Hensher DA, Rose JM, Greene WH. Applied choice analysis: a primer. Cambridge: Cambridge University Press; 2005.
Louviere J, Hensher DA, Swait JD. Stated choice methods: analysis and application. Cambridge: Cambridge University Press; 2000.
Thurstone LL. A law of comparative judgment. Psychol Rev. 1927;34:273–86.
Manski CF. The structure of random utility models. Theor Decis. 1977;8:229–54.
Nobelprize.org. The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel; 2000. http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/2000/.
Mahieu P-A, Andersson H, Beaumais O, Crastes dit Sourd R, Hess S, Wolff F-C. Stated preferences: a unique database composed of 1657 recent published articles in journals related to agriculture, environment, or health. Rev Agric Food Environ Stud. 2017. https://doi.org/10.1007/s41130-017-0053-6.
Bliemer M, Rose J. Experimental design influences on stated choice outputs: an empirical study in air travel choice. Transp Res Part A Policy Pract. 2011;45:63–79.
Soekhai VR, de Bekker-Grob EW, Ellis AR, Vass CM. Discrete choice experiments in health economics: past, present and future. Pharmacoeconomics. 2019;37(2):201–26.
Haghani M, Bliemer MCJ, Hensher DA. The landscape of econometric discrete choice modelling research. J Choice Model. 2021;40:100303.
de Bekker-Grob EW, Ryan M, Gerard K. Discrete choice experiments in health economics: a review of the literature. Health Econ. 2012;21(2):145–72.
Haghani M, Bliemer MCJ, Rose JM, Oppewal J, Lancsar E. Hypothetical bias in stated choice experiments: Part I. Macro-scale analysis of literature and integrative synthesis of empirical evidence from applied economics, experimental psychology and neuroimaging. J Choice Model. 2021;41:100309.
Haghani M, Bliemer MCJ, Rose JM, Oppewal J, Lancsar E. Hypothetical bias in stated choice experiments: Part II. Conceptualisation of external validity, sources and explanations of bias and effectiveness of mitigation methods. J Choice Model. 2021;41:100322.
Determann D, Korfage IJ, Fagerlin A, Steyerberg EW, Bliemer MCJ, Voeten HA,Richardus JH, Lambooij MS, de Bekker-Grob EW. Public preferences for vaccination programmes during pandemics caused by pathogens transmitted through respiratory droplets—a discrete choice experiments in four European countries. Eurosurveillance. 2016;22(21).
Luce RD, Tukey JW. Simultaneous conjoint measurement: a new type of fundamental measurement. J Math Psychol. 1964;1(1):1–27.
Lancsar E, Swait J. Reconceptualising the external validity of discrete choice experiments. Pharmacoeconomics. 2014;32(10):951–65.
Adamowicz W, Hanemann M, Swait JD, Johnson FR, Layton D, Regenwetter M, Reimer T, Sorkin R. Decision strategy and structure in households: a “groups” perspective. Mark Lett. 2005;16(3–4):387–99.
Munro S, Spooner L, Milbers K, Hudson M, Koehn C, Harrison M. Perspectives of patients, first-degree relatives and rheumatologists on preventive treatments for rheumatoid arthritis: a qualitative analysis. BMC Rheumatol. 2018;2:18.
Erasmus Choice Modelling Centre (ECMC). https://www.erim.eur.nl/choice-modelling/activities/research/projects/intersocial/.
Dutch Research Council. https://www.nwo.nl/en/onderzoeksprogrammas/nwo-talent-programme/projects-vidi/2019.
de Vries H, Mesters I, van de Steeg H, Honing C. The general public’s information needs and perceptions regarding hereditary cancer: an application of the Integrated Change Model. Patient Educ Couns. 2005;56(2):154–65.
de Vries H. An integrated approach for understanding health behavior; the I-change model as an example. Psychol Behav Sci Int J. 2017;2(2):555–85.
Eppstein MJ, Grover DK, Marshall JS, Rizzo DM. An agent-based model to study market penetration of plug-in hybrid electric vehicles. Energy Policy. 2011;39(6):3789–802.
Acknowledgements
Grant support for the Towards a Social Interdependent Choice Paradigm for Ex-Ante Evaluation of Healthcare Policies (INTERSOCIAL) project is from the Dutch Research Council (NWO-Talent-Scheme-Vidi-Grant No. 09150171910002). Joanna Coast is supported by the Wellcome Trust [205384/Z/16/Z].
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Funding
Not applicable.
Conflict of interest
The authors declare that there is no conflict of interest.
Ethical approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Data availability
Not applicable.
Code availability
Not applicable.
Disclaimer
This text and its contents reflects the INTERSOCIAL project's view and not the view of the Dutch Research Council or the authors’ respective organisations.
Rights and permissions
About this article
Cite this article
de Bekker-Grob, E.W., Donkers, B., Bliemer, M. et al. Towards Accurate Prediction of Healthcare Choices: The INTERSOCIAL Project. Patient 15, 509–512 (2022). https://doi.org/10.1007/s40271-022-00593-9
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40271-022-00593-9