The increase in depression during the COVID-19 pandemic underscores the importance of systematic approaches to identify individuals with mental health concerns. Primary care is often underutilized for depression screening, and it is not clear how practices can successfully increase screening rates. This study describes a quality improvement initiative to increase depression screening in five Family Medicine clinics. The initiative included four Plan-Do-Study-Act cycles that resulted in implementing a standardized workflow for depression screening, collaborative efforts with health information technology to prompt providers to perform screening via the medical record, delivering educational materials for providers and clinic staff and conducting follow-up education. Between September 2020 and April 2021 there were 23,745 clinic encounters with adult patients that were analyzed to determine whether patients were up-to-date on depression screening following their visit. A multi-level logistic regression model was constructed to determine the changes in likelihood of a patient being up-to-date on screening over the study period, while controlling for patient demographics and comorbidities. The average proportion of up-to-date patients increased from 61.03% in September 2020 to 82.33% in April 2021. Patients aged 65+ and patients with comorbidities were more likely to be up-to-date on screening; patients with telemedicine visits had lower odds of being up-to-date on depression screening. Overall, this paper describes a feasible, effective intervention to increase depression screening in a primary care setting. Additionally, we discuss lessons learned and recommendations to inform the design of future interventions.
Background and Objectives: The patient panels of graduating residents must be reassigned by the end of residency. This process affects over 1 million patients annually within the specialty of family medicine. The purpose of this project was to implement a structured, year-end reassignment system in a family medicine residency program. Methods: Our structured reassignment process took place from December 2017 through June 2020. Panel lists of current, active patients were generated and residents were responsible for reassigning their own panels during a panel reassignment night. We created a tip sheet that addressed patient complexity and continuity, a risk stratification algorithm based on patients’ medical and social complexity, and a tool that tracked the number of patients assigned to each future provider. Outcome measures included a resident satisfaction survey administered in 2018-2020 and patient-provider continuity measured with a run chart from December 2016 through August 2020. Results: The resident survey response rate was 75%. Seventy-three percent felt the panel reassignment night was very helpful; 87% thought the reassignment timeline was extremely reasonable, and 87% indicated that they had the necessary information to reassign their patients. Residents also felt confident that their patients were reassigned appropriately (33% extremely confident, 67% somewhat confident). Patient continuity improved with a 13-point run above the median, indicating nonrandom variation. Patient continuity remained above the median until the impact of COVID-19 in April 2020. Conclusion: Our structured reassignment process was received positively by residents and resulted in improved patient continuity.
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