Patient-Preference Diagnostics: Adapting Stated-Preference Methods to Inform Effective Shared Decision Making

Juan Marcos Gonzalez Sepulveda, F. Reed Johnson, Shelby D. Reed, Charles Muiruri, Carolyn A. Hutyra and Richard C. Mather
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Juan Marcos Gonzalez Sepulveda: Department of Population Health Sciences, Duke School of Medicine, Durham, NC, USA
F. Reed Johnson: Department of Population Health Sciences, Duke School of Medicine, Durham, NC, USA
Shelby D. Reed: Department of Population Health Sciences, Duke School of Medicine, Durham, NC, USA
Charles Muiruri: Department of Population Health Sciences, Duke School of Medicine, Durham, NC, USA
Carolyn A. Hutyra: Optimal Care at Optum
Richard C. Mather: Department of Orthopaedic Surgery, Duke School Medicine, Durham, NC, USA

Medical Decision Making, 2023, vol. 43, issue 2, 214-226

Abstract: Background While clinical practice guidelines underscore the need to incorporate patient preferences in clinical decision making, incorporating meaningful assessment of patient preferences in clinical encounters is challenging. Structured approaches that combine quantitative patient preferences and clinical evidence could facilitate effective patient-provider communication and more patient-centric health care decisions. Adaptive conjoint or stated-preference approaches can identify individual preference parameters, but they can require a relatively large number of choice questions or simplifying assumptions about the error with which preferences are elicited. Method We propose an approach to efficiently diagnose preferences of patients for outcomes of treatment alternatives by leveraging prior information on patient preferences to generate adaptive choice questions to identify a patient’s proximity to known preference phenotypes. This information can be used for measuring sensitivity and specificity, much like any other diagnostic procedure. We simulated responses with varying levels of choice errors for hypothetical patients with specific preference profiles to measure sensitivity and specificity of a 2-question preference diagnostic. Results We identified 4 classes representing distinct preference profiles for patients who participated in a previous first-time anterior shoulder dislocation (FTASD) survey. Posterior probabilities of class membership at the end of a 2-question sequence ranged from 87% to 89%. We found that specificity and sensitivity of the 2-question sequences were robust to respondent errors. The questions appeared to have better specificity than sensitivity. Conclusions Our results suggest that this approach could help diagnose patient preferences for treatments for a condition such as FTASD with acceptable precision using as few as 2 choice questions. Such preference-diagnostic tools could be used to improve and document alignment of treatment choices and patient preferences. Highlights Approaches that combine patient preferences and clinical evidence can facilitate effective patient-provider communication and more patient-centric healthcare decisions. However, diagnosing individual-level preferences is challenging, and no formal diagnostic tools exist. We propose a structured approach to efficiently diagnose patient preferences based on prior information on the distribution of patient preferences in a population. We generated a 2-question test of preferences for the outcomes associated with the treatment of first-time anterior shoulder dislocation. The diagnosis of preferences can help physicians discuss relevant aspects of the treatment options and proactively address patient concerns during the clinical encounter.

Keywords: discrete-choice experiment; experimental design; preference diagnostic; shared decision making (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:43:y:2023:i:2:p:214-226

DOI: 10.1177/0272989X221115058

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