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Weighting Primary Care Patient Panel Size: A Novel Electronic Health Record-Derived Measure Using Machine Learning
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 size...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5086026/ https://www.ncbi.nlm.nih.gov/pubmed/27742603 http://dx.doi.org/10.2196/medinform.6530 |
Sumario: | 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 R(2) of .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. |
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