Yasir Tarabichi1,2,3,4, Jake Goyden1,2, Rujia Liu5,6, Steven Lewis5,6, Joseph Sudano3,6, David C Kaelber1,2,3,7,5,6. 1. Center for Clinical Informatics Research and Education, The MetroHealth System, Cleveland, Ohio, USA. 2. School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA. 3. Department of Internal Medicine, The MetroHealth System, Cleveland, Ohio, USA. 4. Division of Pulmonary and Critical Care Medicine, The MetroHealth System, Cleveland, Ohio, USA. 5. Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA. 6. Center for Healthcare Research and Policy, The MetroHealth System, Cleveland, Ohio, USA. 7. Department of Pediatrics, The MetroHealth System, Cleveland, Ohio, USA.
Abstract
OBJECTIVE: The study sought to assess the feasibility of nationwide chronic disease surveillance using data aggregated through a multisite collaboration of customers of the same electronic health record (EHR) platform across the United States. MATERIALS AND METHODS: An independent confederation of customers of the same EHR platform proposed and guided the development of a program that leverages native EHR features to allow customers to securely contribute de-identified data regarding the prevalence of asthma and rate of asthma-associated emergency department visits to a vendor-managed repository. Data were stratified by state, age, sex, race, and ethnicity. Results were qualitatively compared with national survey-based estimates. RESULTS: The program accumulated information from 100 million health records from over 130 healthcare systems in the United States over its first 14 months. All states were represented, with a median coverage of 22.88% of an estimated state's population (interquartile range, 12.05%-42.24%). The mean monthly prevalence of asthma was 5.27 ± 0.11%. The rate of asthma-associated emergency department visits was 1.39 ± 0.08%. Both measures mirrored national survey-based estimates. DISCUSSION: By organizing the program around native features of a shared EHR platform, we were able to rapidly accumulate population level measures from a sizeable cohort of health records, with representation from every state. The resulting data allowed estimates of asthma prevalence that were comparable to data from traditional epidemiologic surveys at both geographic and demographic levels. CONCLUSIONS: Our initiative demonstrates the potential of intravendor customer collaboration and highlights an organizational approach that complements other data aggregation efforts seeking to achieve nationwide EHR-based chronic disease surveillance.
OBJECTIVE: The study sought to assess the feasibility of nationwide chronic disease surveillance using data aggregated through a multisite collaboration of customers of the same electronic health record (EHR) platform across the United States. MATERIALS AND METHODS: An independent confederation of customers of the same EHR platform proposed and guided the development of a program that leverages native EHR features to allow customers to securely contribute de-identified data regarding the prevalence of asthma and rate of asthma-associated emergency department visits to a vendor-managed repository. Data were stratified by state, age, sex, race, and ethnicity. Results were qualitatively compared with national survey-based estimates. RESULTS: The program accumulated information from 100 million health records from over 130 healthcare systems in the United States over its first 14 months. All states were represented, with a median coverage of 22.88% of an estimated state's population (interquartile range, 12.05%-42.24%). The mean monthly prevalence of asthma was 5.27 ± 0.11%. The rate of asthma-associated emergency department visits was 1.39 ± 0.08%. Both measures mirrored national survey-based estimates. DISCUSSION: By organizing the program around native features of a shared EHR platform, we were able to rapidly accumulate population level measures from a sizeable cohort of health records, with representation from every state. The resulting data allowed estimates of asthma prevalence that were comparable to data from traditional epidemiologic surveys at both geographic and demographic levels. CONCLUSIONS: Our initiative demonstrates the potential of intravendor customer collaboration and highlights an organizational approach that complements other data aggregation efforts seeking to achieve nationwide EHR-based chronic disease surveillance.
Authors: Michael D Buck; Sheila Anane; John Taverna; Sam Amirfar; Remle Stubbs-Dame; Jesse Singer Journal: J Am Med Inform Assoc Date: 2011-11-09 Impact factor: 4.497
Authors: Shawn N Murphy; Griffin Weber; Michael Mendis; Vivian Gainer; Henry C Chueh; Susanne Churchill; Isaac Kohane Journal: J Am Med Inform Assoc Date: 2010 Mar-Apr Impact factor: 4.497
Authors: Katharine H McVeigh; Elizabeth Lurie-Moroni; Pui Ying Chan; Remle Newton-Dame; Lauren Schreibstein; Kathleen S Tatem; Matthew L Romo; Lorna E Thorpe; Sharon E Perlman Journal: EGEMS (Wash DC) Date: 2017-12-07
Authors: John D D'Amore; Joshua C Mandel; David A Kreda; Ashley Swain; George A Koromia; Sumesh Sundareswaran; Liora Alschuler; Robert H Dolin; Kenneth D Mandl; Isaac S Kohane; Rachel B Ramoni Journal: J Am Med Inform Assoc Date: 2014-06-26 Impact factor: 4.497
Authors: Matthew L Romo; Pui Ying Chan; Elizabeth Lurie-Moroni; Sharon E Perlman; Remle Newton-Dame; Lorna E Thorpe; Katharine H McVeigh Journal: Prev Chronic Dis Date: 2016-04-28 Impact factor: 2.830
Authors: Brent A Williams; Stephen Voyce; Stephen Sidney; Véronique L Roger; Timothy B Plante; Sharon Larson; Michael J LaMonte; Darwin R Labarthe; Bailey M DeBarmore; Alexander R Chang; Alanna M Chamberlain; Catherine P Benziger Journal: J Am Heart Assoc Date: 2022-04-12 Impact factor: 6.106
Authors: Yasir Tarabichi; Adam Frees; Steven Honeywell; Courtney Huang; Andrew M Naidech; Jason H Moore; David C Kaelber Journal: ACI open Date: 2021-01