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.

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.

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

 
  • References

  • 1 Kannampallil T, Abraham J, Lou SS, Payne PRO. Conceptual considerations for using EHR-based activity logs to measure clinician burnout and its effects. J Am Med Inform Assoc 2021; 28 (05) 1032-1037
  • 2 McFarland DC, Hlubocky F, Susaimanickam B, O'Hanlon R, Riba M. Addressing depression, burnout, and suicide in oncology physicians. Am Soc Clin Oncol Educ Book 2019; 39: 590-598
  • 3 Dzau VJ, Kirch DG, Nasca TJ. To care is human - collectively confronting the clinician-burnout crisis. N Engl J Med 2018; 378 (04) 312-314
  • 4 Shanafelt TD, Balch CM, Bechamps GJ. et al. Burnout and career satisfaction among American surgeons. Ann Surg 2009; 250 (03) 463-471
  • 5 Willard-Grace R, Knox M, Huang B, Hammer H, Kivlahan C, Grumbach K. Burnout and health care workforce turnover. Ann Fam Med 2019; 17 (01) 36-41
  • 6 Linzer M, Konrad TR, Douglas J. et al. Managed care, time pressure, and physician job satisfaction: results from the physician worklife study. J Gen Intern Med 2000; 15 (07) 441-450
  • 7 Huang YL, Berg BP, Horn JL, Nagaraju D, Rushlow DR. Balancing clinician workload through strategic patient panel designs. Qual Manag Health Care 2023; 32 (03) 137-144
  • 8 Khurshid A, Hautala M, Oliveira E. et al. Social and health information platform: piloting a standards-based, digital platform linking social determinants of health data into clinical workflows for community-wide use. Appl Clin Inform 2023; 14 (05) 883-892
  • 9 Feldman SS, Davlyatov G, Hall AG. Toward understanding the value of missing social determinants of health data in care transition planning. Appl Clin Inform 2020; 11 (04) 556-563
  • 10 Chen M, Tan X, Padman R. Social determinants of health in electronic health records and their impact on analysis and risk prediction: a systematic review. J Am Med Inform Assoc 2020; 27 (11) 1764-1773
  • 11 Zhao Y, Wood EP, Mirin N, Cook SH, Chunara R. Social determinants in machine learning cardiovascular disease prediction models: a systematic review. Am J Prev Med 2021; 61 (04) 596-605
  • 12 Amrollahi F, Shashikumar SP, Meier A, Ohno-Machado L, Nemati S, Wardi G. Inclusion of social determinants of health improves sepsis readmission prediction models. J Am Med Inform Assoc 2022; 29 (07) 1263-1270
  • 13 Taylor LA, Tan AX, Coyle CE. et al. Leveraging the social determinants of health: what works?. PLoS One 2016; 11 (08) e0160217
  • 14 Sotudian S, Afran A, LeBedis CA, Rives AF, Paschalidis IC, Fishman MDC. Social determinants of health and the prediction of missed breast imaging appointments. BMC Health Serv Res 2022; 22 (01) 1454
  • 15 Langevin R, Berry ABL, Zhang J. et al. Implementation fidelity of chatbot screening for social needs: acceptability, feasibility, appropriateness. Appl Clin Inform 2023; 14 (02) 374-391
  • 16 Breiman L. Random forests. Mach Learn 2001; 45 (01) 5-32
  • 17 Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat 2001; 29 (05) 1189-1232
  • 18 Hosmer DW, Lemeshow S. Applied Logistic Regression. New York, NY: John Wiley & Sons; 2004
  • 19 Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995; 20 (03) 273-297
  • 20 von Winterfeldt D, Edwards W. Decision Trees. In: Decision Analysis and Behavioral Research. Cambridge: Cambridge University Press; 63-89
  • 21 Montgomery DC, Peck EA, Vining GG. Introduction to Linear Regression Analysis. New York, NY: John Wiley & Sons; 2021
  • 22 Department of Health Policy and Management at The Johns Hopkins University Bloomberg School of Public Health. The johns hopkins ACG® system version 12.0 user documentation. 2019
  • 23 Pedregosa F, Varoquaux G, Gramfort A. et al. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 2011; 12: 2825-2830
  • 24 Piazza JR, Charles ST, Almeida DM. Living with chronic health conditions: age differences in affective well-being. J Gerontol B Psychol Sci Soc Sci 2007; 62 (06) 313-321
  • 25 Bergström J, Preber H. Tobacco use as a risk factor. J Periodontol 1994; 65 (5, Suppl): 545-550
  • 26 Vlassoff C. Gender differences in determinants and consequences of health and illness. J Health Popul Nutr 2007; 25 (01) 47-61
  • 27 Scholes D, LaCroix AZ, Ichikawa LE, Barlow WE, Ott SM. Injectable hormone contraception and bone density: results from a prospective study. Epidemiology 2002; 13 (05) 581-587