scholarly journals Population Health Initiatives for Primary Care at Duke University School of Medicine

2014 ◽  
Vol 75 (1) ◽  
pp. 33-35
Author(s):  
Barbara Sheline ◽  
Mina Silberberg ◽  
Melinda Blazar ◽  
Brian Halstater ◽  
Lloyd Michener
2014 ◽  
Vol 89 (10) ◽  
pp. 1370-1374 ◽  
Author(s):  
Barbara Sheline ◽  
Anh N. Tran ◽  
Joseph Jackson ◽  
Bruce Peyser ◽  
Susan Rogers ◽  
...  

2000 ◽  
Vol 75 (Supplement) ◽  
pp. S265-S267
Author(s):  
RUSSEL E. KAUFMAN ◽  
EMIL R. PETRUSA

2021 ◽  
Vol 141 (2) ◽  
pp. 79-80
Author(s):  
Jonathan Pearson-Stuttard ◽  
Nick Harding

Author(s):  
Elham Hatef ◽  
Hadi Kharrazi ◽  
Ed VanBaak ◽  
Marc Falcone ◽  
Lindsey Ferris ◽  
...  

Maryland Department of Health (MDH) has been preparing for alignment of its population health initiatives with Maryland’s unique All-Payer hospital global budget program. In order to operationalize population health initiatives, it is required to identify a starter set of measures addressing community level health interventions and to collect interoperable data for those measures. The broad adoption of electronic health records (EHRs) with ongoing data collection on almost all patients in the state, combined with hospital participation in health information exchange (HIE) initiatives, provides an unprecedented opportunity for near real-time assessment of the health of the communities. MDH’s EHR-based monitoring complements, and perhaps replaces, ad-hoc assessments based on limited surveys, billing, and other administrative data. This article explores the potential expansion of health IT capacity as a method to improve population health across Maryland.First, we propose a progression plan for four selected community-wide population health measures: body mass index, blood pressure, smoking status, and falls-related injuries. We then present an assessment of the current and near real-time availability of digital data in Maryland including the geographic granularity on which each measure can be assessed statewide. Finally, we provide general recommendations to improve interoperable data collection for selected measures over time via the Maryland HIE. This paper is intended to serve as a high- level guiding framework for communities across the US that are undergoing healthcare transformation toward integrated models of care using universal interoperable EHRs.


Author(s):  
Kelly L. Strutz ◽  
Zhehui Luo ◽  
Jennifer E. Raffo ◽  
Cristian I. Meghea ◽  
Peggy Vander Meulen ◽  
...  

AbstractObjectivesEvaluating population health initiatives at the community level necessitates valid counterfactual communities, which includes having similar complexity with respect to population composition, healthcare access, and health determinants. Estimating appropriate county counterfactuals is challenging in states with large inter-county variation. We present and discuss an application of K-means cluster analysis for determining county-level counterfactuals in an evaluation of a county perinatal system of care for Medicaid-insured pregnant women.Materials and MethodsCounties were described using indicators from the American Community Survey, Area Health Resources Files, University of Wisconsin Population Health Institute County Health Rankings, and vital records for Michigan Medicaid-insured births for the year intervention began (or the closest available year). We ran analyses of 1,000 iterations with random starting cluster values for each of a range of number of clusters from 3 to 10 and used standard variability and reliability measures to identify the optimal number of clusters.ResultsOne county was grouped with the intervention county in all solutions for all iterations and thus considered most valid for 1:1 population county comparisons. Two additional counties were frequently grouped with the intervention county. However, no county was ideal for all subpopulation analyses.Practice ImplicationsAlthough the K-means method was successful at identifying a comparison county, concerning intervention-comparison differences remained. This limitation of the method may be specific to this county and the constraints of a within-state study. This method could potentially be more useful when applied to other counties in and outside of Michigan.


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