Patient Phenotypes to Identify Resource Allocation and Usage in Primary Care

Resource allocation, including decisions about clinical and administrative staffing, language interpreter requirements, and billing procedures, is challenging in a complex medical system. In the setting of limited resources and high patient need, identification of patients who require a high amount of medical, nursing, and clinical services need to be identified for optimal care. The purpose of this paper is to identify the factors that predict patient phenotypes, a set of observable characteristics of an individual, that reflect their primary care resource usage. The data used in this study are de-identified, patient level data (n=34,957) between January 2019 to December 2021. We used k-means clustering to identify patient phenotypes based on the frequency of primary care and emergency department visits. Using multinomial regression, we then identified insurance type, comorbidity score, age, race, language, gender, hypertension, chronic opioid, obesity, prediabetes, tobacco usage, congestive heart failure, and chronic obstructive pulmonary disease as significant predictors for the primary care usage phenotypes. Having a more complete, holistic understanding of patient resource phenotypes can help leaders to make important decisions regarding optimal hospital resource allocations. Future work using our methods could be used to prospectively identify patients in high-need resource phenotypes compared to individuals with average annual usage.