Patient preferences as human factors for health data recommender systems and shared decision making in orthopaedic practice - Singh, Akanksha; Schooley, Benjamin; Floyd, Sarah B.; Pill, Stephen G.; Brooks, John M. (Frontiers in Digital Health, 2023)
A core set of requirements for designing AI-based Health Recommender Systems (HRS) is a thorough understanding of human factors in a decision-making process. Patient preferences regarding treatment outcomes can be one important human factor. For orthopaedic medicine, limited communication may occur between a patient and a provider during the short duration of a clinical visit, limiting the opportunity for the patient to express outcome preferences. This may occur despite patient preferences having a significant impact on achieving patient satisfaction, shared decision making and treatment success. Inclusion of patient preferences during patient intake and/or during the early phases of patient contact and information gathering can lead to better treatment recommendations. Aim: We aim to explore patient preferences as significant human factors in treatment decision making in orthopedics. The goal of this research is to design, build, and test an app that collects baseline patient preferences across orthopaedic outcomes and reports this information to providers during a clinical visit. This data may also be used to inform the design of HRSs for orthopaedic treatment decision making. Methods: We created a mobile app to collect patient preferences using a direct weighting (DW) technique. We used a mixed methods approach to pilot test the app with 23 first-time orthopaedic visit patients presenting with joint pain and/or function deficiency by presenting the app for utilization and conducting qualitative interviews and quantitative surveys post utilization. Results: The study validated five core preference domains, with most users dividing their 100-point DW allocation across 1-3 domains. The tool received moderate to high usability scores. Thematic analysis of patient interviews provides insights into outcome preferences that are important to patients, how they can be communicated effectively, and incorporated into a clinical visit with meaningful patient-provider communication that leads to shared decision making. Conclusion: Patient preferences may be important human factors to consider in determining treatment options that may be helpful for automating patient treatment recommendations. We conclude that inclusion of patient preferences to inform the design of HRSs results in creating more robust patient treatment profiles in the EHR thus enhancing opportunities for treatment recommendations and future AI applications.