Polis Center Works

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Browsing Polis Center Works by Author "Comer, Karen F."

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    Electronic Health Record (EHR)-Based Community Health Measures: An Exploratory Assessment of Perceived Usefulness by Local Health Departments
    (BMC, 2018-05-22) Comer, Karen F.; Gibson, P. Joseph; Zou, Jian; Rosenman, Marc; Dixon, Brian E.; Health Policy and Management, School of Public Health
    BACKGROUND: Given the widespread adoption of electronic health record (EHR) systems in health care organizations, public health agencies are interested in accessing EHR data to improve health assessment and surveillance. Yet there exist few examples in the U.S. of governmental health agencies using EHR data routinely to examine disease prevalence and other measures of community health. The objective of this study was to explore local health department (LHD) professionals' perceptions of the usefulness of EHR-based community health measures, and to examine these perceptions in the context of LHDs' current access and use of sub-county data, data aggregated at geographic levels smaller than county. METHODS: To explore perceived usefulness, we conducted an online survey of LHD professionals in Indiana. One hundred and thirty-three (133) individuals from thirty-one (31) LHDs participated. The survey asked about usefulness of specific community health measures as well as current access to and uses of sub-county population health data. Descriptive statistics were calculated to examine respondents' perceptions, access, and use. A one-way ANOVA (with pairwise comparisons) test was used to compare average scores by LHD size. RESULTS: Respondents overall indicated moderate agreement on which community health measures might be useful. Perceived usefulness of specific EHR-based community health measures varied by size of respondent's LHD [F(3, 88) = 3.56, p = 0.017]. Over 70% of survey respondents reported using community health data, but of those < 30% indicated they had access to sub-county level data. CONCLUSION: Respondents generally preferred familiar community health measures versus novel, EHR-based measures that are not in widespread use within health departments. Access to sub-county data is limited but strongly desired. Future research and development is needed as LHD staff gain access to EHR data and apply these data to support the core function of health assessment.
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    SAVI Data Catalog
    (The Polis Center at IUPUI, 2011-04) Derr, Michelle; Colbert, Jay; Nalla, Goutami; Comer, Karen F.
    The SAVI Community Information System (SAVI) is the nation’s largest spatially-enabled system of its type, providing local organizations, researchers, and involved citizens with the detailed, geographically precise information needed to make well-informed decisions. This data catalog describes the wealth of free data provided by SAVI, including data about the social, physical, and economic conditions of Central Indiana communities from counties to neighborhoods and census tracts, as well as information on thousands of non-profit and community-based organizations and programs. SAVI exists as a Web-based, interactive system that allows users to create custom maps, graphs, charts, and data profiles of over 2,000 Central Indiana communities.
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    Using Electronic Health Record Data to Improve Community Health Assessment
    (UKY, 2016) Dixon, Brian E.; Zou, Jian "Frank"; Comer, Karen F.; Rosenman, Marc; Craig, Jennifer L.; Gibson, P.; Epidemiology, School of Public Health
    Background: Community health assessments assist health departments in identifying health needs as well as disparities, and they enable linking of needs with available interventions. Electronic health record (EHR) systems possess growing volumes of clinical and administrative data, making them a valuable source of data for ongoing community health assessment. Purpose: To produce population health indicators using data from EHR systems that could be combined and visually displayed alongside social determinants data, and to provide data sets at geographic levels smaller than a county. Methods: Data from multiple EHR systems used by major health systems covering >90% of the population in a metropolitan urban area were extracted and linked using a health information exchange (HIE) network for individuals who had at least two clinical encounters within the HIE network over a 3-year period. Population health indicators of highest interest to public health stakeholders were calculated and visualized at varying levels of geographic granularity. Results: Ten population health indicators were calculated, visualized, and shared with public health partners. Indicators ranged from the prevalence of a disease to the proportion of individuals with poor maintenance of their chronic condition. Calculating rates at the census-tract level or larger (e.g., average population size > 4000 people) is preferable to smaller geographic units of analysis. Implications: Extraction and linking of EHR system data are feasible for public health via an HIE network. While indicators can be derived, biases exist in the data that require more study. Further, HIE networks do not yet possess data for all conditions and measures desired by local public health stakeholders. The data that can be extracted, however, can be combined with public datasets on social determinants