Looking at Patients' Choices through the Lens of Expected Utility: A Critique and Research Agenda

Institute for Health, Health Care Policy, and Aging Research, and Department of Economics, Rutgers University, 112 Paterson St., New Brunswick, NJ 08901, USA.
Medical Decision Making (Impact Factor: 3.24). 06/2012; 32(4):527-31. DOI: 10.1177/0272989X12451339
Source: PubMed


The expected utility framework underlies much research in medical decision making. Because the framework requires decisions to be decomposed into probabilities of states and the values of those states, researchers have investigated the two components separately from each other and from patients' actual decisions. The authors propose that it would be productive to focus more research on the relationships among risk perceptions, outcome valuations, and choices in the same decision makers. They outline exploratory analyses based on two existing national surveys, the Medical Expenditure Panel Survey and the Joint Canada/United States Survey of Health.

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    • "While there are many perceived advantages to this prescriptive approach, there are also criticisms. First, ‘optimal’ options derived from decision analysis are reliant on assumptions, theories and inaccuracies in inputs which mean they may not actually prescribe the best course of action for each patient [34]. Second, the current approaches to decision analysis are typically ‘overt’ to be best course of action, and consequently have been argued to be an extension of paternalism, compromising patient autonomy [35]. "
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    ABSTRACT: Background Patient decision aids (PtDA) are developed to facilitate informed, value-based decisions about health. Research suggests that even when informed with necessary evidence and information, cognitive errors can prevent patients from choosing the option that is most congruent with their own values. We sought to utilize principles of behavioural economics to develop a computer application that presents information from conventional decision aids in a way that reduces these errors, subsequently promoting higher quality decisions. Method The Dynamic Computer Interactive Decision Application (DCIDA) was developed to target four common errors that can impede quality decision making with PtDAs: unstable values, order effects, overweighting of rare events, and information overload. Healthy volunteers were recruited to an interview to use three PtDAs converted to the DCIDA on a computer equipped with an eye tracker. Participants were first used a conventional PtDA, and then subsequently used the DCIDA version. User testing was assessed based on whether respondents found the software both usable: evaluated using a) eye-tracking, b) the system usability scale, and c) user verbal responses from a ‘think aloud’ protocol; and useful: evaluated using a) eye-tracking, b) whether preferences for options were changed, and c) and the decisional conflict scale. Results Of the 20 participants recruited to the study, 11 were male (55%), the mean age was 35, 18 had at least a high school education (90%), and 8 (40%) had a college or university degree. Eye-tracking results, alongside a mean system usability scale score of 73 (range 68–85), indicated a reasonable degree of usability for the DCIDA. The think aloud study suggested areas for further improvement. The DCIDA also appeared to be useful to participants wherein subjects focused more on the features of the decision that were most important to them (21% increase in time spent focusing on the most important feature). Seven subjects (25%) changed their preferred option when using DCIDA. Conclusion Preliminary results suggest that DCIDA has potential to improve the quality of patient decision-making. Next steps include larger studies to test individual components of DCIDA and feasibility testing with patients making real decisions.
    Full-text · Article · Aug 2014 · BMC Medical Informatics and Decision Making
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    ABSTRACT: To introduce a new online generic decision support system based on multicriteria decision analysis (MCDA), implemented in practical and user-friendly software (Annalisa©). All parties in health care lack a simple and generic way to picture and process the decisions to be made in pursuit of improved decision making and more informed choice within an overall philosophy of person- and patient-centred care. The MCDA-based system generates patient-specific clinical guidance in the form of an opinion as to the merits of the alternative options in a decision, which are all scored and ranked. The scores for each option combine, in a simple expected value calculation, the best estimates available now for the performance of those options on patient-determined criteria, with the individual patient's preferences, expressed as importance weightings for those criteria. The survey software within which the Annalisa file is embedded (Elicia©) customizes and personalizes the presentation and inputs. Principles relevant to the development of such decision-specific MCDA-based aids are noted and comparisons with alternative implementations presented. The necessity to trade-off practicality (including resource constraints) with normative rigour and empirical complexity, in both their development and delivery, is emphasized. The MCDA-/Annalisa-based decision support system represents a prescriptive addition to the portfolio of decision-aiding tools available online to individuals and clinicians interested in pursuing shared decision making and informed choice within a commitment to transparency in relation to both the evidence and preference bases of decisions. Some empirical data establishing its usability are provided.
    Full-text · Article · Aug 2013 · Health expectations: an international journal of public participation in health care and health policy
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    ABSTRACT: The quality and safety of health care are under increasing scrutiny. Recent studies suggest that medical errors, practice variability, and guideline noncompliance are common, and that cognitive error contributes significantly to delayed or incorrect diagnoses. These observations have increased interest in understanding decision-making psychology.Many nonrational (i.e., not purely based in statistics) cognitive factors influence medical decisions and may lead to error. The most well-studied include heuristics, preferences for certainty, overconfidence, affective (emotional) influences, memory distortions, bias, and social forces such as fairness or blame.Although the extent to which such cognitive processes play a role in anesthesia practice is unknown, anesthesia care frequently requires rapid, complex decisions that are most susceptible to decision errors. This review will examine current theories of human decision behavior, identify effects of nonrational cognitive processes on decision-making, describe characteristic anesthesia decisions in this context, and suggest strategies to improve decision-making.
    Full-text · Article · Nov 2013 · Anesthesiology
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