ArticleJMIR medical informatics2016
Weighting Primary Care Patient Panel Size: A Novel Electronic Health Record-Derived Measure Using Machine Learning.
Article in JMIR medical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- Analysis of Variation in Organizational Definitions of Primary Care Panels: A Systematic Review.JAMA network open · 2022Pooled it
- Article
- Application of a machine learning model to predict the estimated primary care patient time consumption.npj health systems · 2026Article
- Transforming endoscopic retrograde cholangiopancreatography: the role of artificial intelligence in pre-operative planning, intraoperative navigation, and post-operative risk prediction.Frontiers in medicine · 2026Review
- Toward a universal definition of provider-patient attachment in primary care.Canadian family physician Medecin de famille canadien · 2024Review
- Implications of conscious AI in primary healthcare.Family medicine and community health · 2024Review
- How the Gender Wage Gap for Primary Care Physicians Differs by Compensation Approach : A Microsimulation Study.Annals of internal medicine · 2022Article
- Artificial intelligence and machine learning in precision and genomic medicine.Medical oncology (Northwood, London, England) · 2022Review
- Assessment of staffing needs for registered nurses and licensed practical nurses at primary care units in Brazil using Workload Indicators of Staffing Need (WISN) method.Human resources for health · 2022Article
- Physician Work Hours and the Gender Pay Gap - Evidence from Primary Care.The New England journal of medicine · 2020Article
- Electronic health records and burnout: Time spent on the electronic health record after hours and message volume associated with exhaustion but not with cynicism among primary care clinicians.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine.Database : the journal of biological databases and curation · 2020Article
- Ten Ways Artificial Intelligence Will Transform Primary Care.Journal of general internal medicine · 2019Article
- Empanelment in a Resident Teaching Practice: A Cornerstone to Improving Resident Outpatient Education and Patient Care.Journal of graduate medical education · 2019Article
- Determining Patient Panel Size in Primary Care: A Meta-Narrative Review.Journal of primary care & community healthReview
- A Simple Framework for Weighting Panels Across Primary Care Disciplines: Findings From a Large US Multidisciplinary Group Practice.Quality management in health careObservational
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCharacterizing patient complexity using granular electronic health record (EHR) data regularly available to health systems is necessary to optimize primary care processes at scale.
objectiveTo 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.
methodsWe 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.
resultsWe 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
conclusionsIndividual 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.