Home Research Research Library Integrating Community and Clinical Data to Assess Patient Risks with A Population Health Assessment Engine (PHATE) Integrating Community and Clinical Data to Assess Patient Risks with A Population Health Assessment Engine (PHATE) 2020 Author(s) Bambekova, Pavela G, Liaw, Winston R, Phillips, Robert L, and Bazemore, Andrew W Topic(s) Education & Training, Role of Primary Care, and Achieving Health System Goals Keyword(s) Graduate Medical Education, Payment, Population Health, Prime, Quality Of Care, and Shortage Areas Volume Journal of the American Board of Family Medicine Source Journal of the American Board of Family Medicine Clinicians are concerned about their patients’ social determinants of health (SDH); yet, they are unsure how to effectively gather patient-level SDH data and intervene without adding to current administrative burdens. Designed properly, clinical registries offer solutions to integrate neighborhood SDH data with clinical data from electronic health records, enabling the understanding of community factors to guide patient care. Federal and state interest in adjusting reimbursements based on SDH further underscores the need for strategies that integrate SDH and clinical data. The Population Health Assessment Engine (PHATE) exemplifies a registry-based SDH data integration solution that adjusts payments, contributes to public health surveillance, organizes care around hot spots (gaps in quality or uncontrolled disease), assesses patient risk, and connects with community organizations. PHATE also permits residency training to meet community health competency milestones by incorporating the PHATE curriculum. These functions enhance value, and their utility in education and care delivery would benefit from further investigation. ABFM Research Read all 2020 Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments Go to Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments 2025 The Impact of Length of Training on Clinical Preparedness Among New Graduates: A Report From the Length of Training Pilot Study in Family Medicine Go to The Impact of Length of Training on Clinical Preparedness Among New Graduates: A Report From the Length of Training Pilot Study in Family Medicine 2017 Fellowship or Further Training for Family Medicine Residents? Go to Fellowship or Further Training for Family Medicine Residents? 2019 Research gaps in the organisation of primary healthcare in low-income and middle-income countries and ways to address them: a mixed-methods approach Go to Research gaps in the organisation of primary healthcare in low-income and middle-income countries and ways to address them: a mixed-methods approach
Author(s) Bambekova, Pavela G, Liaw, Winston R, Phillips, Robert L, and Bazemore, Andrew W Topic(s) Education & Training, Role of Primary Care, and Achieving Health System Goals Keyword(s) Graduate Medical Education, Payment, Population Health, Prime, Quality Of Care, and Shortage Areas Volume Journal of the American Board of Family Medicine Source Journal of the American Board of Family Medicine
ABFM Research Read all 2020 Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments Go to Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments 2025 The Impact of Length of Training on Clinical Preparedness Among New Graduates: A Report From the Length of Training Pilot Study in Family Medicine Go to The Impact of Length of Training on Clinical Preparedness Among New Graduates: A Report From the Length of Training Pilot Study in Family Medicine 2017 Fellowship or Further Training for Family Medicine Residents? Go to Fellowship or Further Training for Family Medicine Residents? 2019 Research gaps in the organisation of primary healthcare in low-income and middle-income countries and ways to address them: a mixed-methods approach Go to Research gaps in the organisation of primary healthcare in low-income and middle-income countries and ways to address them: a mixed-methods approach
2020 Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments Go to Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments
2025 The Impact of Length of Training on Clinical Preparedness Among New Graduates: A Report From the Length of Training Pilot Study in Family Medicine Go to The Impact of Length of Training on Clinical Preparedness Among New Graduates: A Report From the Length of Training Pilot Study in Family Medicine
2017 Fellowship or Further Training for Family Medicine Residents? Go to Fellowship or Further Training for Family Medicine Residents?
2019 Research gaps in the organisation of primary healthcare in low-income and middle-income countries and ways to address them: a mixed-methods approach Go to Research gaps in the organisation of primary healthcare in low-income and middle-income countries and ways to address them: a mixed-methods approach