Jordan Everson1,2, Vaishali Patel3, Julia Adler-Milstein4. 1. Department of Health Policy, Vanderbilt University School of Medicine, Nashville, Tennessee, USA. 2. Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA. 3. Data Analysis Branch, Office of the National Coordinator for Health Information Technology, Washington, DC, USA. 4. Department of Medicine, UCSF School of Medicine, San Francisco, California, USA.
Abstract
OBJECTIVE: Recent policy making aims to prevent health systems, lectronic health record (EHR) vendors, and others from blocking the electronic sharing of patient data necessary for clinical care. We sought to assess the prevalence of information blocking prior to enforcement of these rules. MATERIALS AND METHODS: We conducted a national survey of health information exchange organizations (HIEs) to measure the prevalence of information blocking behaviors observed by these third-party entities. Eighty-nine of 106 HIEs (84%) meeting the inclusion criteria responded. RESULTS: The majority (55%) of HIEs reported that EHR vendors at least sometimes engage in information blocking, while 30% of HIEs reported the same for health systems. The most common type of information blocking behavior EHR vendors engaged in was setting unreasonably high prices, which 42% of HIEs reported routinely observing. The most common type of information blocking behavior health systems engaged in was refusing to share information, which 14% of HIEs reported routinely observing. Reported levels of vendor information blocking was correlated with regional competition among vendors and information blocking was concentrated in some geographic regions. DISCUSSION: Our findings are consistent with early reports, revealing persistently high levels of information blocking and important variation by actor, type of behavior, and geography. These trends reflect the observations and experiences of HIEs and their potential biases. Nevertheless, these data serve as a baseline against which to measure the impact of new regulations and to inform policy makers about the most common types of information blocking behaviors. CONCLUSION: Enforcement aimed at reducing information blocking should consider variation in prevalence and how to most effectively target efforts.
OBJECTIVE: Recent policy making aims to prevent health systems, lectronic health record (EHR) vendors, and others from blocking the electronic sharing of patient data necessary for clinical care. We sought to assess the prevalence of information blocking prior to enforcement of these rules. MATERIALS AND METHODS: We conducted a national survey of health information exchange organizations (HIEs) to measure the prevalence of information blocking behaviors observed by these third-party entities. Eighty-nine of 106 HIEs (84%) meeting the inclusion criteria responded. RESULTS: The majority (55%) of HIEs reported that EHR vendors at least sometimes engage in information blocking, while 30% of HIEs reported the same for health systems. The most common type of information blocking behavior EHR vendors engaged in was setting unreasonably high prices, which 42% of HIEs reported routinely observing. The most common type of information blocking behavior health systems engaged in was refusing to share information, which 14% of HIEs reported routinely observing. Reported levels of vendor information blocking was correlated with regional competition among vendors and information blocking was concentrated in some geographic regions. DISCUSSION: Our findings are consistent with early reports, revealing persistently high levels of information blocking and important variation by actor, type of behavior, and geography. These trends reflect the observations and experiences of HIEs and their potential biases. Nevertheless, these data serve as a baseline against which to measure the impact of new regulations and to inform policy makers about the most common types of information blocking behaviors. CONCLUSION: Enforcement aimed at reducing information blocking should consider variation in prevalence and how to most effectively target efforts.
Authors: David C Chang; Aki Shiozawa; Louis L Nguyen; Kristin L Chrouser; Bruce A Perler; Julie A Freischlag; Paul M Colombani; Fizan Abdullah Journal: J Am Coll Surg Date: 2010-11-30 Impact factor: 6.113
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