@article{e6c124d0c49241bbbbfd884a1ff8c950,
title = "Research-grade data in the real world: Challenges and opportunities in data quality from a pragmatic trial in community-based practices",
abstract = "Randomized controlled trials face cost, logistic, and generalizability limitations, including difficulty engaging racial/ethnic minorities. Real-world data (RWD) from pragmatic trials, including electronic health record (EHR) data, may produce intervention evaluation findings generalizable to diverse populations. This case study of Project IMPACT describes unique barriers and facilitators of optimizing RWD to improve health outcomes and advance health equity in small immigrant-serving community-based practices. Project IMPACT tested the effect of an EHR-based health information technology intervention on hypertension control among small urban practices serving South Asian patients. Challenges in acquiring accurate RWD included EHR field availability and registry capabilities, cross-sector communication, and financial, personnel, and space resources. Although using RWD from community-based practices can inform health equity initiatives, it requires multidisciplinary collaborations, clinic support, procedures for data input (including social determinants), and standardized field logic/rules across EHR platforms.",
keywords = "Data quality, Health equity, Health information technology, Immigrants, Pragmatic trials, Real world data",
author = "Divney, {Anna A.} and Lopez, {Priscilla M.} and Huang, {Terry T.} and Thorpe, {Lorna E.} and Chau Trinh-Shevrin and Islam, {Nadia S.}",
note = "Funding Information: This publication was supported in part by cooperative agreement U48DP005008 from the Centers for Disease Control and Prevention (CDC), Prevention Research Centers (PRC) Program. NI and CTS contributions were also partially supported by the National Institutes of Health (NIH) National Institute on Minority Health and Health Disparities (NIMHD) grants P60MD000538 and U54MD000538; NIH National Center for the Advancement of Translational Science (NCATS) grant UL1TR001445; and NIH National Institute of Diabetes and Digestive Kidney Diseases (NIDDK) grants R01DK110048. The findings and conclusions in this journal article are solely the responsibility of the authors and may not represent the official view of the CDC, NIH NIMHD, NIH NIDDK, or NIH NCATS Funding Information: This publication was supported in part by cooperative agreement U48DP005008 from the Centers for Disease Control and Prevention (CDC), Prevention Research Centers (PRC) Program. NI and CTS contributions were also partially supported by the National Institutes of Health (NIH) National Institute on Minority Health and Health Disparities (NIMHD) grants P60MD000538 and U54MD000538; NIH National Center for the Advancement of Translational Science (NCATS) grant UL1TR001445; and NIH National Institute of Diabetes and Digestive Kidney Diseases (NIDDK) grants R01DK110048. The findings and conclusions in this journal article are solely the responsibility of the authors and may not represent the official view of the CDC, NIH NIMHD, NIH NIDDK, or NIH NCATS. Publisher Copyright: {\textcopyright} 2019 The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved.",
year = "2019",
month = apr,
day = "17",
doi = "10.1093/jamia/ocz062",
language = "English (US)",
volume = "26",
pages = "847--854",
journal = "Journal of the American Medical Informatics Association : JAMIA",
issn = "1067-5027",
publisher = "Oxford University Press",
number = "8-9",
}