Appl Clin Inform 2024; 15(03): 511-527
DOI: 10.1055/s-0044-1787647
Research Article

Predicting Provider Workload Using Predicted Patient Risk Score and Social Determinants of Health in Primary Care Setting

Yiqun Jiang
1   Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, United States
Yu-Li Huang
1   Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, United States
Alexandra Watral
1   Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, United States
Renaldo C. Blocker
1   Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, United States
David R. Rushlow
2   Department of Family Medicine, Mayo Clinic, Rochester, Minnesota, United States
› Author Affiliations
Funding None.


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.

Protection of Human and Animal Subjects

The research study was not human subject research.

Publication History

Received: 20 December 2023

Accepted: 07 May 2024

Article published online:
03 July 2024

© 2024. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

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