Defense Date


Document Type


Degree Name

Doctor of Philosophy



First Advisor

Roy Sabo

Second Advisor

Leroy Thacker

Third Advisor

Ekaterina Smirnova

Fourth Advisor

Alison Huffstetler

Fifth Advisor

Yalda Jabbarpour


Primary Care is on the frontlines of healthcare, thus they see the most diverse set of patients. In order to achieve high functioning primary care, a practice must establish empanelment, the pairing of patients to providers. Enumeration of empanelment, or estimating panel sizes, helps ensure that the demands of the patients demand the supply of providers and optimize the balance of primary care resources to improve quality of care. Further we can adjust panel sizes by using patient-level data on healthcare utilization and complexity extracted from the electronic medial record to determine the amount of care or burden of work that a patient poses to a provider. With this adjustment we can have a more informed estimation of panel sizes that can differentiate the amount of care provided to individuals instead of assuming work for each patient is the same. This dissertation attempts to evaluate different methods of estimating adjusted panel sizes and understand the best practices for extracting data from the EMR to build decision making tools. In our analysis we compare the current best panel size estimation method introduced by Rajkomar et al 2016 with processes that change the clustering method (*k*-means vs gaussian mixture model), provide a direct estimation of a burden score, incorporate demographics and complexity data into the clustering (*KAMILA* and gaussian multinomial mixture model) and assess how to conduct panel size estimation with a larger set of features beyond utilization counts.


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