Home Research Research Library Community Vital Signs: Taking the Pulse of the Community While Caring for Patients Community Vital Signs: Taking the Pulse of the Community While Caring for Patients 2016 Author(s) Hughes, Lauren S, Phillips, Robert L, DeVoe, Jennifer E, and Bazemore, Andrew W Topic(s) Achieving Health System Goals Keyword(s) Population Health Volume Journal of the American Board of Family Medicine Source Journal of the American Board of Family Medicine In 2014 both the Institute of Medicine and the National Quality Forum recommended the inclusion of social determinants of health data in electronic health records (EHRs). Both entities primarily focus on collecting socioeconomic and health behavior data directly from individual patients. The burden of reliably, accurately, and consistently collecting such information is substantial, and it may take several years before a primary care team has actionable data available in its EHR. A more reliable and less burdensome approach to integrating clinical and social determinant data exists and is technologically feasible now. Community vital signs-aggregated community-level information about the neighborhoods in which our patients live, learn, work, and play-convey contextual social deprivation and associated chronic disease risks based on where patients live. Given widespread access to “big data” and geospatial technologies, community vital signs can be created by linking aggregated population health data with patient addresses in EHRs. These linked data, once imported into EHRs, are a readily available resource to help primary care practices understand the context in which their patients reside and achieve important health goals at the patient, population, and policy levels. ABFM Research Read all 2019 Accountable Care Organizations Serving Deprived Communities Are Less Likely to Share in Savings Go to Accountable Care Organizations Serving Deprived Communities Are Less Likely to Share in Savings 2020 Quality Changes Among Primary Care Clinicians Participating in the Transforming Clinical Practice Initiative Go to Quality Changes Among Primary Care Clinicians Participating in the Transforming Clinical Practice Initiative 2025 Heterogeneity of diagnosis and documentation of post-COVID conditions in primary care: A machine learning analysis Go to Heterogeneity of diagnosis and documentation of post-COVID conditions in primary care: A machine learning analysis 2018 Slow Progress and Persistent Challenges for the Underrepresented Minority Family Physician Go to Slow Progress and Persistent Challenges for the Underrepresented Minority Family Physician
Author(s) Hughes, Lauren S, Phillips, Robert L, DeVoe, Jennifer E, and Bazemore, Andrew W Topic(s) Achieving Health System Goals Keyword(s) Population Health Volume Journal of the American Board of Family Medicine Source Journal of the American Board of Family Medicine
ABFM Research Read all 2019 Accountable Care Organizations Serving Deprived Communities Are Less Likely to Share in Savings Go to Accountable Care Organizations Serving Deprived Communities Are Less Likely to Share in Savings 2020 Quality Changes Among Primary Care Clinicians Participating in the Transforming Clinical Practice Initiative Go to Quality Changes Among Primary Care Clinicians Participating in the Transforming Clinical Practice Initiative 2025 Heterogeneity of diagnosis and documentation of post-COVID conditions in primary care: A machine learning analysis Go to Heterogeneity of diagnosis and documentation of post-COVID conditions in primary care: A machine learning analysis 2018 Slow Progress and Persistent Challenges for the Underrepresented Minority Family Physician Go to Slow Progress and Persistent Challenges for the Underrepresented Minority Family Physician
2019 Accountable Care Organizations Serving Deprived Communities Are Less Likely to Share in Savings Go to Accountable Care Organizations Serving Deprived Communities Are Less Likely to Share in Savings
2020 Quality Changes Among Primary Care Clinicians Participating in the Transforming Clinical Practice Initiative Go to Quality Changes Among Primary Care Clinicians Participating in the Transforming Clinical Practice Initiative
2025 Heterogeneity of diagnosis and documentation of post-COVID conditions in primary care: A machine learning analysis Go to Heterogeneity of diagnosis and documentation of post-COVID conditions in primary care: A machine learning analysis
2018 Slow Progress and Persistent Challenges for the Underrepresented Minority Family Physician Go to Slow Progress and Persistent Challenges for the Underrepresented Minority Family Physician
