Paul, Margaret M

Introduction/Objectives The rise in screening for social drivers of health (SDoH) in primary care settings has led to questions about effective intervention design. A large primary care practice at Mayo Clinic developed a model centered on a full-time Health Equity Coordinator (HEC) to facilitate clinic-community partnerships in 2022. The goal of this analysis was to assess the model on process outcomes, resolution of SDoH needs, and changes in healthcare utilization. Methods Using a cohort study design, we identified primary care patients with unmet SDoH needs at the intervention clinic and two comparator clinics from March 2022 to December 2023. We assessed process outcomes descriptively and resolution of SDoH needs and healthcare utilization using multivariable regression. Results The HEC contacted 348 patients and conducted 748 meetings with community-based organization staff. While there were decreases for SDoH needs and missed or late cancelled appointments in intervention group, there were no significant difference in resolution of SDoH and healthcare utilization relative to comparator groups. Conclusions The HEC model for multi-stakeholder coordination showed promise in referring patients to services and engaging key stakeholders in a primary care setting. Longer term follow up may be needed to identify changes in SDoH needs and healthcare utilization.

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.

Introduction Video visits in primary care have decreased significantly since their surge early in the COVID-19 pandemic. Methods Clinicians including physicians, nurse practitioners, and physician assistants in the Mayo Clinic Department of Family Medicine were invited to dedicate one to two half-days per month exclusively to video visits. Schedules for these half-day sessions remained restricted to video appointments until the day prior. If at least 80% of slots were not filled by then, remaining openings were converted to in-person visits. If the half-day sessions were at least 80% full with video visits, clinicians had the option of performing these sessions remotely. Results Of 155 eligible clinicians, 34 (22%) participated voluntarily in this scheduling intervention. For the participating clinicians, the average video visit rate increased from 6.3% per month preintervention to 11.5% postintervention ( p < 0.001). On a departmental level, the trend line for the percent of outpatient visits via video visit was decreasing by −0.1% per month preintervention. The postintervention slope of the trend line was 0.3% per month ( p < 0.001). The majority (65%) of the time the half-day video visit schedules filled at least 80% and did not require conversion to allow in-person visits allowing clinicians to work remotely if they chose. Conclusion Allowing family medicine clinicians to perform 1–2 half-day schedules dedicated to only video visits increases the percentage of video visit performed per month both for individual clinicians and on a departmental level.

Qualitative interviews and focus groups are commonly used methods to elicit participants’ voices in program evaluations. However, the use of these data-gathering methods can fall short of the goal; even with open-ended questions, the protocols guiding and shaping interviews and focus groups heavily reflect the evaluators’ understanding and experience of the program or intervention under consideration and its target population(s). In this paper, we describe three cases that employed a method of inquiry that is underutilized in evaluation but one that we believe holds great potential as a method to enhance interpersonal reflexivity, namely participant-generated photo-elicitation interviewing (PEI). Across three diverse settings and interventions, we readily added participant-generated PEI to our mixed methods evaluations. We found participant-generated PEI to add great value to the evaluations by complementing our other data collection methods, allowing for greater participant voice, challenging evaluators’ assumptions, enhancing dissemination efforts, and fostering our reflexivity.

Abstract On June 1, 2020, NYC Health + Hospitals, in partnership with the NYC Department of Health and Mental Hygiene, other city agencies, and a large network of community partners, launched the New York City Test & Trace (T2) COVID-19 response program to identify and isolate cases, reduce transmission through contact tracing, and provide support to residents during isolation or quarantine periods. In this paper, we describe lessons learned with respect to planning and implemention of case notification and contact tracing. Our findings are based on extensive document review and analysis of 74 key informant interviews with T2 leadership and frontline staff, cases, and contacts conducted between January and September 2022. Interviews elicited respondent background, history of program development, program leadership and structure, goals of the program, program evolution, staffing, data systems, elements of community engagement, trust with community, program reach, timeliness, equity, general barriers and challenges, general facilitators and best practices, and recommendations/improvement for the program. Facilitators and barriers revealed in the interviews primarily revolved around hiring and managing staff, data and technology, and quality of interactions with the public. Based on these facilitators and barriers, we identify suggestions to support effective planning and response for future case notification and contact tracing programs, including recommendations for planning during latent periods, case management and data systems, and processes for outreach to cases and contacts.