title: Weighting Primary Care Patient Panel Size: A Novel Electronic Health Record-Derived Measure Using Machine Learning.
abstract: BACKGROUND
  Characterizing patient complexity using granular electronic health record (EHR) data regularly available to health systems is necessary to optimize primary care processes at scale.
  
  
  OBJECTIVE
  To characterize the utilization patterns of primary care patients and create weighted panel sizes for providers based on work required to care for patients with different patterns.
  
  
  METHODS
  We used EHR data over a 2-year period from patients empaneled to primary care clinicians in a single academic health system, including their in-person encounter history and virtual encounters such as telephonic visits, electronic messaging, and care coordination with specialists. Using a combination of decision rules and k-means clustering, we identified clusters of patients with similar health care system activity. Phenotypes with basic demographic information were used to predict future health care utilization using log-linear models. Phenotypes were also used to calculate weighted panel sizes.
  
  
  RESULTS
  We identified 7 primary care utilization phenotypes, which were characterized by various combinations of primary care and specialty usage and were deemed clinically distinct by primary care physicians. These phenotypes, combined with age-sex and primary payer variables, predicted future primary care utilization with R2of .394 and were used to create weighted panel sizes.
  
  
  CONCLUSIONS
  Individual patients' health care utilization may be useful for classifying patients by primary care work effort and for predicting future primary care usage.
authors.affiliation: Department of Medicine, University of California, San Francisco, San Francisco, CA, United States. alvin.rajkomar@ucsf.edu.
authors.forename: Alvin
authors.identifier: http://orcid.org/0000-0001-5750-5016
authors.initials: A
authors.lastname: Rajkomar
authors.forename: Joanne Wing Lan
authors.identifier: http://orcid.org/0000-0002-0627-0699
authors.initials: JW
authors.lastname: Yim
authors.forename: Kevin
authors.identifier: http://orcid.org/0000-0002-7809-214X
authors.initials: K
authors.lastname: Grumbach
authors.forename: Ami
authors.identifier: http://orcid.org/0000-0001-9885-8563
authors.initials: A
authors.lastname: Parekh
keywords: ambulatory care; health care economics and organizations; machine learning; medical informatics; patient acceptance of health care; primary health care; risk adjustment