Diplomate Spotlight Opening Doors with Board Certification: A Conversation with Long Standing Diplomate Joseph Cook Read Opening Doors with Board Certification: A Conversation with Long Standing Diplomate Joseph Cook
Phoenix Newsletter - July 2025 Available Now: 2026 5-Year Cycle Registration Read Available Now: 2026 5-Year Cycle Registration
Home Research Research Library Precision Ecologic Medicine: Tailoring Care to Mitigate Impacts of Climate Change Precision Ecologic Medicine: Tailoring Care to Mitigate Impacts of Climate Change 2023 Author(s) DeVoe, Jennifer E, Huguet, Nathalie, Likumahuwa-Ackman, Sonja, Bazemore, Andrew W, Gold, Rachel, and Werner, Leah Topic(s) Role of Primary Care Keyword(s) Health Information Technology (HIT) Volume Journal of Primary Care & Community Health Source Journal of Primary Care & Community Health As recent extreme weather events demonstrate, climate change presents unprecedented and increasing health risks, disproportionately so for disadvantaged communities in the U.S. already experiencing health disparities. As patients in these frontline communities live through extreme weather events, socioeconomic and health stressors are compounded; thus, their healthcare teams will need tools to provide precision ecologic medicine approaches to their care. Many primary care teams are taking actionable steps to bring community-level socioeconomic data (“community vital signs”) into electronic medical records, to facilitate tailoring care based on a given patient’s circumstances. This work can be extended to include environmental risk data, thus equipping healthcare teams with an awareness of clinical and community vital signs and making them better positioned to mitigate climate impacts on health. For example, if healthcare teams can easily identify patients who have multiple chronic conditions and live in an urban heat island, they can proactively arrange to “prescribe” an air conditioner, heat pump, and/or air purifier. Or, when a severe storm/heat event/poor air quality event is predicted, they can take preemptive steps to get help to patients at high medical and socioeconomic risk, rather than waiting for them to arrive in the emergency department. Advances in health information technologies now make it technically feasible to integrate a wealth of publicly-available community-level data into EMRs. Efforts to bring this contextual data into clinical settings must be accelerated to equip healthcare teams to provide precision ecologic medicine interventions to their patients. 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 2019 Payment Structures That Support Social Care Integration With Clinical Care: Social Deprivation Indices and Novel Payment Models Go to Payment Structures That Support Social Care Integration With Clinical Care: Social Deprivation Indices and Novel Payment Models 2020 The Impact of Social and Clinical Complexity on Diabetes Control Measures Go to The Impact of Social and Clinical Complexity on Diabetes Control Measures 2016 “Community vital signs”: incorporating geocoded social determinants into electronic records to promote patient and population health Go to “Community vital signs”: incorporating geocoded social determinants into electronic records to promote patient and population health
Author(s) DeVoe, Jennifer E, Huguet, Nathalie, Likumahuwa-Ackman, Sonja, Bazemore, Andrew W, Gold, Rachel, and Werner, Leah Topic(s) Role of Primary Care Keyword(s) Health Information Technology (HIT) Volume Journal of Primary Care & Community Health Source Journal of Primary Care & Community Health
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 2019 Payment Structures That Support Social Care Integration With Clinical Care: Social Deprivation Indices and Novel Payment Models Go to Payment Structures That Support Social Care Integration With Clinical Care: Social Deprivation Indices and Novel Payment Models 2020 The Impact of Social and Clinical Complexity on Diabetes Control Measures Go to The Impact of Social and Clinical Complexity on Diabetes Control Measures 2016 “Community vital signs”: incorporating geocoded social determinants into electronic records to promote patient and population health Go to “Community vital signs”: incorporating geocoded social determinants into electronic records to promote patient and population health
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
2019 Payment Structures That Support Social Care Integration With Clinical Care: Social Deprivation Indices and Novel Payment Models Go to Payment Structures That Support Social Care Integration With Clinical Care: Social Deprivation Indices and Novel Payment Models
2020 The Impact of Social and Clinical Complexity on Diabetes Control Measures Go to The Impact of Social and Clinical Complexity on Diabetes Control Measures
2016 “Community vital signs”: incorporating geocoded social determinants into electronic records to promote patient and population health Go to “Community vital signs”: incorporating geocoded social determinants into electronic records to promote patient and population health