Patient preferences as human factors for health data recommender systems and shared decision making in orthopaedic practice.

Background: 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 treatment outcome preferences (TOP). 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 treatment outcome 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 TOPs 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 TOPs 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 TOP 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 TOPs 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 TOPs 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 TOPs 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.

Comparison of fluorodeoxyglucose positron emission tomography and (111)indium-white blood cell imaging in the diagnosis of periprosthetic infection of the hip.

We aimed to compare the accuracy of fluorodeoxyglucose positron emission tomography (FDG-PET) with technetium-99m sulfur colloid (111)indium-labeled white blood cell scintigraphy (TcSC-Ind BM/WBC) in diagnosis of periprosthetic infection. Eighty-nine patients with 92 painful hip prostheses were recruited prospectively and given the option of undergoing either combined FDG-PET and TcSC-Ind BM/WBC or FDG-PET only. FDG-PET correctly diagnosed 20 of the 21 infected cases (sensitivity, 95.2%) and ruled out infection in 66 of the 71 aseptic hips (specificity, 93%) corresponding to a positive predictive value of 80% (20/25) and a negative predictive value of 98.5% (66/67). TcSC-Ind BM/WBC correctly identified 5 of the 10 infected cases (sensitivity, 50%) and 39 of 41 aseptic cases (specificity, 95.1%) corresponding to a positive and negative predictive values of 41.7% (5/12 cases) and 88.6% (39/44 cases), respectively. Based on these preliminary results, FDG-PET appears to be a promising diagnostic tool for distinguishing septic from aseptic painful hip prostheses.