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1.
Am J Manag Care ; 28(1): e14-e23, 2022 01 01.
Article in English | MEDLINE | ID: mdl-35049262

ABSTRACT

OBJECTIVES: Computable social risk factor phenotypes derived from routinely collected structured electronic health record (EHR) or health information exchange (HIE) data may represent a feasible and robust approach to measuring social factors. This study convened an expert panel to identify and assess the quality of individual EHR and HIE structured data elements that could be used as components in future computable social risk factor phenotypes. STUDY DESIGN: Technical expert panel. METHODS: A 2-round Delphi technique included 17 experts with an in-depth knowledge of available EHR and/or HIE data. The first-round identification sessions followed a nominal group approach to generate candidate data elements that may relate to socioeconomics, cultural context, social relationships, and community context. In the second-round survey, panelists rated each data element according to overall data quality and likelihood of systematic differences in quality across populations (ie, bias). RESULTS: Panelists identified a total of 89 structured data elements. About half of the data elements (n = 45) were related to socioeconomic characteristics. The panelists identified a diverse set of data elements. Elements used in reimbursement-related processes were generally rated as higher quality. Panelists noted that several data elements may be subject to implicit bias or reflect biased systems of care, which may limit their utility in measuring social factors. CONCLUSIONS: Routinely collected structured data within EHR and HIE systems may reflect patient social risk factors. Identifying and assessing available data elements serves as a foundational step toward developing future computable social factor phenotypes.


Subject(s)
Health Information Exchange , Delphi Technique , Electronic Health Records , Humans , Risk Factors
2.
Am J Public Health ; 110(7): 1031-1033, 2020 07.
Article in English | MEDLINE | ID: mdl-32437282

ABSTRACT

We evaluated the effectiveness of a community health worker-supported home visitor program on perinatal outcomes of 455 at-risk pregnant women with program data merged with electronic medical records from July 2015 through October 2017 in Rochester, New York. Program participants had fewer adverse outcomes than did nonparticipants, including lower rates of preterm birth (12% vs 20%; χ2, P = .05) and low birth weight (14% vs 22%; χ2, P = .05). This program was effective at achieving improved perinatal outcomes.


Subject(s)
Community Health Workers , House Calls , Pregnancy Outcome , Prenatal Care/organization & administration , Adult , Female , Humans , Infant , Infant, Low Birth Weight , Infant, Newborn , New York , Outcome Assessment, Health Care , Postnatal Care/statistics & numerical data , Pregnancy , Pregnancy Complications/prevention & control , Pregnancy, High-Risk , Premature Birth/prevention & control
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