Watral, Alexandra

Abstract Background Provider burnout due to workload is a significant concern in primary care settings. Workload for primary care providers encompasses both scheduled visit care and non–visit care interactions. These interactions are highly influenced by patients' health conditions or acuity, which can be measured by the Adjusted Clinical Group (ACG) score. However, new patients typically have minimal health information beyond social determinants of health (SDOH) to determine ACG score. Objectives This study aims to assess new patient workload by first predicting the ACG score using SDOH, age, and gender and then using this information to estimate the number of appointments (scheduled visit care) and non–visit care interactions. Methods Two years of appointment data were collected for patients who had initial appointment requests in the first year and had the ACG score, appointment, and non–visit care counts in the subsequent year. State-of-art machine learning algorithms were employed to predict ACG scores and compared with current baseline estimation. Linear regression models were then used to predict appointments and non–visit care interactions, integrating demographic data, SDOH, and predicted ACG scores. Results The machine learning methods showed promising results in predicting ACG scores. Besides the decision tree, all other methods performed at least 9% better in accuracy than the baseline approach which had an accuracy of 78%. Incorporating SDOH and predicted ACG scores also significantly improved the prediction for both appointments and non–visit care interactions. The R 2 values increased by 95.2 and 93.8%, respectively. Furthermore, age, smoking tobacco, family history, gender, usage of injection birth control, and ACG were significant factors for determining appointments. SDOH factors such as tobacco usage, physical exercise, education level, and group activities were strongly correlated with non–visit care interactions. Conclusion The study highlights the importance of SDOH and predicted ACG scores in predicting provider workload in primary care settings.

Objective: In this study, we aim to develop and propose an evaluation method for analyzing the design of operating rooms (ORs) from the perspective of surgical teams’ reported experiences and stress levels. Background: Stress and burnout of surgical team members can lead to diminished performance and medical errors, which endangers the safety of both the patients and team members. The design and layout of the OR play a critical role in managing such stress. Methods: To understand surgical teams’ spatial needs related to their experiences and stress, we administered a survey and in-depth focus group discussions to three surgical teams from the same organization. The identified spatial needs were translated into functional scenarios and spatial metrics, essentially viewing the OR through the perspective of users. Results: Our analysis revealed four integral sections—patient flow, room organization, access to facilities/medical equipment/support staff/team members, and staff well-being—identified as critical design factors associated with the experiences and stress levels of the surgical teams in the ORs. Conclusions: We expect this method to serve as a tool for evaluating the effect of the design of OR layouts on stress, thereby supporting the well-being and resiliency of surgical teams.