Bonner, Timethia J

ABSTRACT Introduction Community health workers (CHWs) help patients navigate community resources. CHW programs can improve health outcomes and reduce healthcare utilization, but identifying eligible patients is challenging. We developed an electronic health record (EHR)‐based algorithm trained to predict referrals to a CHW program. Our objective was to evaluate whether an algorithm trained on historical referrals identifies a subgroup of patients that differs from the subgroup identified through questionnaire‐based health‐related social needs (HRSN) screening alone. Methods This analysis used data from Mayo Clinic primary care patients in Southeast Minnesota. We developed a gradient boosted time‐to‐event model that incorporated demographics, diagnoses, HRSN questionnaire responses, and HRSNs derived from clinical notes using natural language processing. We compared the characteristics of algorithm‐identified patients with those identified through questionnaires and assessed the variable importance of model features. Results The algorithm had AUC: 0.87 in the training and 0.92 in the hold out set. Patients flagged by the algorithm tended to use interpreter services more commonly (12.6% vs. 1.3%), have a non‐English preferred language (14.1% vs. 2.3%), and have more instances of health literacy documented (5.36 vs. 1.29) compared to patients identified by the HRSN questionnaire. Only 47.0% of patients flagged by the algorithm had responded to the HRSN questionnaire within 2 years. Variable importance was highest for health literacy and financial strain from unstructured notes. Conclusions The algorithm is a chart‐review prioritization tool for referral to the CHW program for patients that may overcome challenges to identifying patients with HRSNs, especially when questionnaire data is incomplete.

While studies have evaluated the utility of telehealth in replacing in-person clinical encounters, there is a dearth of literature examining the quality of patient-physician communication with telehealth encounters. Accordingly, this study assessed the feasibility of using virtual cardiology clinical encounters to examine patient-physician interaction, communication, and perceptions of the clinical encounter. Telemedicine cardiology clinical encounters were audio- and video-recorded following the encounter, patients, and cardiologists completed an electronic survey to assess perceptions of the encounter. Qualitative analysis of the communication and statistical analysis of the survey data was conducted, providing descriptive data. The study included 11 patient-physician dyads; all patients were non-Hispanic White. Cardiologists were more racially and ethnically diverse (63% Asian). Most patients agreed telemedicine was comparable to in-person encounters (85.7%), with all cardiologists reporting that patients appeared satisfied with the encounter. We utilized an assessment tool to examine patient-physician communication in the recorded virtual encounters. This study suggests examining patient-physician communication using virtual clinical encounters is feasible, although there are barriers that need addressing for larger studies.