Stephen T Mennemeyer1, Nir Menachemi2, Saurabh Rahurkar3, Eric W Ford4. 1. Department of Health Care Organization and Policy, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA smenneme@uab.edu. 2. Department of Health Policy and Management, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, IN, USA. 3. Department of Health Care Organization and Policy, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA. 4. Department of Health Policy, Bloomberg School of Public Health, Johns Hopkins University, Baltimore MD, USA.
Abstract
BACKGROUND AND OBJECTIVE: The Health Information Technology for Economic and Clinical Health (HITECH) Act has distributed billions of dollars to physicians as incentives for adopting certified electronic health records (EHRs) through the meaningful use (MU) program ultimately aimed at improving healthcare outcomes. The authors examine the extent to which the MU program impacted the EHR adoption curve that existed prior to the Act. METHODS: Bass and Gamma Shifted Gompertz (G/SG) diffusion models of the adoption of "Any" and "Basic" EHR systems in physicians' offices using consistent data series covering 2001-2013 and 2006-2013, respectively, are estimated to determine if adoption was stimulated during either a PrePay (2009-2010) period of subsidy anticipation or a PostPay (2011-2013) period when payments were actually made. RESULTS: Adoption of Any EHR system may have increased by as much as 7 percentage points above the level predicted in the absence of the MU subsidies. This estimate, however, lacks statistical significance and becomes smaller or negative under alternative model specifications. No substantial effects are found for Basic systems. The models suggest that adoption was largely driven by "imitation" effects (q-coefficient) as physicians mimic their peers' technology use or respond to mandates. Small and often insignificant "innovation" effects (p-coefficient) are found suggesting little enthusiasm by physicians who are leaders in technology adoption. CONCLUSION: The authors find weak evidence of the impact of the MU program on EHR uptake. This is consistent with reports that many current EHR systems reduce physician productivity, lack data sharing capabilities, and need to incorporate other key interoperability features (e.g., application program interfaces).
BACKGROUND AND OBJECTIVE: The Health Information Technology for Economic and Clinical Health (HITECH) Act has distributed billions of dollars to physicians as incentives for adopting certified electronic health records (EHRs) through the meaningful use (MU) program ultimately aimed at improving healthcare outcomes. The authors examine the extent to which the MU program impacted the EHR adoption curve that existed prior to the Act. METHODS: Bass and Gamma Shifted Gompertz (G/SG) diffusion models of the adoption of "Any" and "Basic" EHR systems in physicians' offices using consistent data series covering 2001-2013 and 2006-2013, respectively, are estimated to determine if adoption was stimulated during either a PrePay (2009-2010) period of subsidy anticipation or a PostPay (2011-2013) period when payments were actually made. RESULTS: Adoption of Any EHR system may have increased by as much as 7 percentage points above the level predicted in the absence of the MU subsidies. This estimate, however, lacks statistical significance and becomes smaller or negative under alternative model specifications. No substantial effects are found for Basic systems. The models suggest that adoption was largely driven by "imitation" effects (q-coefficient) as physicians mimic their peers' technology use or respond to mandates. Small and often insignificant "innovation" effects (p-coefficient) are found suggesting little enthusiasm by physicians who are leaders in technology adoption. CONCLUSION: The authors find weak evidence of the impact of the MU program on EHR uptake. This is consistent with reports that many current EHR systems reduce physician productivity, lack data sharing capabilities, and need to incorporate other key interoperability features (e.g., application program interfaces).
Authors: Niam Yaraghi; Anna Ye Du; Raj Sharman; Ram D Gopal; R Ramesh; Ranjit Singh; Gurdev Singh Journal: J Am Med Inform Assoc Date: 2013-11-28 Impact factor: 4.497
Authors: Melinda Beeuwkes Buntin; Matthew F Burke; Michael C Hoaglin; David Blumenthal Journal: Health Aff (Millwood) Date: 2011-03 Impact factor: 6.301
Authors: Diane R Rittenhouse; Lawrence P Casalino; Stephen M Shortell; Sean R McClellan; Robin R Gillies; Jeffrey A Alexander; Melinda L Drum Journal: Health Aff (Millwood) Date: 2011-06-30 Impact factor: 6.301
Authors: Jonathan Purtle; Robert I Field; Thomas Hipper; Jillian Nash-Arott; Esther Chernak; James W Buehler Journal: J Public Health Manag Pract Date: 2018 Jan/Feb
Authors: Lisa Simon; Enihomo Obadan-Udoh; Alfa-Ibrahim Yansane; Arti Gharpure; Steven Licht; Jean Calvo; James Deschner; Anna Damanaki; Berit Hackenberg; Muhammad Walji; Heiko Spallek; Elsbeth Kalenderian Journal: Appl Clin Inform Date: 2019-05-29 Impact factor: 2.342
Authors: Lindsay A Stevens; Yumi T DiAngi; Jonathan D Schremp; Monet J Martorana; Roberta E Miller; Tzielan C Lee; Natalie M Pageler Journal: Appl Clin Inform Date: 2017-12-20 Impact factor: 2.342