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      National Trends in the Safety Performance of Electronic Health Record Systems From 2009 to 2018

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          Key Points

          Question

          How did safety performance of electronic health record systems (EHRs) change in the US from 2009 to 2018?

          Findings

          In this case series using 8657 hospital-year observations from adult hospitals nationwide that used the National Quality Forum Health IT Safety Measure, a computerized physician order entry and EHR safety test, from 2009 to 2018, mean scores on the overall test increased from 53.9% in 2009 to 65.6% in 2018. There was considerable variation in test performance by hospital and EHR vendor.

          Meaning

          These findings suggest that, despite broad adoption and optimization of EHR systems in hospitals, wide variation in the safety performance of operational EHR systems remains across a large sample of hospitals and EHR vendors, and serious safety vulnerabilities persist in these operational EHRs.

          Abstract

          This case series examines the safety performance of computerized provider order entry in electronic health record systems in US hospitals from 2009 to 2018.

          Abstract

          Importance

          Despite the broad adoption of electronic health record (EHR) systems across the continuum of care, safety problems persist.

          Objective

          To measure the safety performance of operational EHRs in hospitals across the country during a 10-year period.

          Design, Setting, and Participants

          This case series included all US adult hospitals nationwide that used the National Quality Forum Health IT Safety Measure EHR computerized physician order entry safety test administered by the Leapfrog Group between 2009 and 2018. Data were analyzed from July 1, 2018 to December 1, 2019.

          Exposure

          The Health IT Safety Measure test, which uses simulated medication orders that have either injured or killed patients previously to evaluate how well hospital EHRs could identify medication errors with potential for patient harm.

          Main Outcomes and Measures

          Descriptive statistics for performance on the assessment test over time were calculated at the overall test score level, type of decision support category level, and EHR vendor level.

          Results

          Among 8657 hospital-years observed during the study, mean (SD) scores on the overall test increased from 53.9% (18.3%) in 2009 to 65.6% (15.4%) in 2018. Mean (SD) hospital score for the categories representing basic clinical decision support increased from 69.8% (20.8%) in 2009 to 85.6% (14.9%) in 2018. For the categories representing advanced clinical decision support, the mean (SD) score increased from 29.6% (22.4%) in 2009 to 46.1% (21.6%) in 2018. There was considerable variation in test performance by EHR.

          Conclusions and Relevance

          These findings suggest that despite broad adoption and optimization of EHR systems in hospitals, wide variation in the safety performance of operational EHR systems remains across a large sample of hospitals and EHR vendors. Hospitals using some EHR vendors had significantly higher test scores. Overall, substantial safety risk persists in current hospital EHR systems.

          Related collections

          Most cited references18

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          Use of electronic health records in U.S. hospitals.

          Despite a consensus that the use of health information technology should lead to more efficient, safer, and higher-quality care, there are no reliable estimates of the prevalence of adoption of electronic health records in U.S. hospitals. We surveyed all acute care hospitals that are members of the American Hospital Association for the presence of specific electronic-record functionalities. Using a definition of electronic health records based on expert consensus, we determined the proportion of hospitals that had such systems in their clinical areas. We also examined the relationship of adoption of electronic health records to specific hospital characteristics and factors that were reported to be barriers to or facilitators of adoption. On the basis of responses from 63.1% of hospitals surveyed, only 1.5% of U.S. hospitals have a comprehensive electronic-records system (i.e., present in all clinical units), and an additional 7.6% have a basic system (i.e., present in at least one clinical unit). Computerized provider-order entry for medications has been implemented in only 17% of hospitals. Larger hospitals, those located in urban areas, and teaching hospitals were more likely to have electronic-records systems. Respondents cited capital requirements and high maintenance costs as the primary barriers to implementation, although hospitals with electronic-records systems were less likely to cite these barriers than hospitals without such systems. The very low levels of adoption of electronic health records in U.S. hospitals suggest that policymakers face substantial obstacles to the achievement of health care performance goals that depend on health information technology. A policy strategy focused on financial support, interoperability, and training of technical support staff may be necessary to spur adoption of electronic-records systems in U.S. hospitals. 2009 Massachusetts Medical Society
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            Electronic health record adoption in US hospitals: the emergence of a digital “advanced use” divide

            While most hospitals have adopted electronic health records (EHRs), we know little about whether hospitals use EHRs in advanced ways that are critical to improving outcomes, and whether hospitals with fewer resources – small, rural, safety-net – are keeping up. Using 2008–2015 American Hospital Association Information Technology Supplement survey data, we measured “basic” and “comprehensive” EHR adoption among hospitals to provide the latest national numbers. We then used new supplement questions to assess advanced use of EHRs and EHR data for performance measurement and patient engagement functions. To assess a digital “advanced use” divide, we ran logistic regression models to identify hospital characteristics associated with high adoption in each advanced use domain. We found that 80.5% of hospitals adopted at least a basic EHR system, a 5.3 percentage point increase from 2014. Only 37.5% of hospitals adopted at least 8 (of 10) EHR data for performance measurement functions, and 41.7% of hospitals adopted at least 8 (of 10) patient engagement functions. Critical access hospitals were less likely to have adopted at least 8 performance measurement functions (odds ratio [OR] = 0.58; P  < .001) and at least 8 patient engagement functions (OR = 0.68; P  = 0.02). While the Health Information Technology for Economic and Clinical Health Act resulted in widespread hospital EHR adoption, use of advanced EHR functions lags and a digital divide appears to be emerging, with critical-access hospitals in particular lagging behind. This is concerning, because EHR-enabled performance measurement and patient engagement are key contributors to improving hospital performance. Hospital EHR adoption is widespread and many hospitals are using EHRs to support performance measurement and patient engagement. However, this is not happening across all hospitals.
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              Two Decades Since To Err Is Human: An Assessment Of Progress And Emerging Priorities In Patient Safety

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                Author and article information

                Journal
                JAMA Netw Open
                JAMA Netw Open
                JAMA Netw Open
                JAMA Network Open
                American Medical Association
                2574-3805
                29 May 2020
                May 2020
                29 May 2020
                : 3
                : 5
                : e205547
                Affiliations
                [1 ]Division of Clinical Epidemiology, University of Utah School of Medicine, Salt Lake City
                [2 ]Harvard Business School, Boston, Massachusetts
                [3 ]Department of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
                [4 ]Clinical and Quality Analysis, Partners Healthcare, Somerville, Massachusetts
                [5 ]The Leapfrog Group, Washington, DC
                [6 ]Harvard Medical School, Boston, Massachusetts
                Author notes
                Article Information
                Accepted for Publication: March 19, 2020.
                Published: May 29, 2020. doi:10.1001/jamanetworkopen.2020.5547
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Classen DC et al. JAMA Network Open.
                Corresponding Author: David C. Classen, MD, MS, Division of Clinical Epidemiology, University of Utah School of Medicine, 561 E Northmont Way, Salt Lake City, Utah 84103 ( dcclassen@ 123456hotmail.com ).
                Author Contributions: Dr Classen and Mr Holmgren had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: Classen, Holmgren, Newmark, Seger, Bates.
                Acquisition, analysis, or interpretation of data: Classen, Holmgren, Co, Newmark, Danforth, Bates.
                Drafting of the manuscript: Classen, Holmgren, Co, Danforth, Bates.
                Critical revision of the manuscript for important intellectual content: Classen, Holmgren, Co, Newmark, Seger, Bates.
                Statistical analysis: Classen, Holmgren.
                Obtained funding: Classen, Bates.
                Administrative, technical, or material support: Classen, Co, Newmark, Seger, Bates.
                Supervision: Newmark, Bates.
                Conflict of Interest Disclosures: Dr Classen reported receiving grants from the Gordon and Betty Moore Foundation and Robert Wood Johnson Foundation and serving as an employee of Pascal Metrics, a federally certified patient safety organization. outside the submitted work. Dr Bates reported personal fees from EarlySense and CDI NEGEV; equity from Valera Health, CLEW, MDClone, and Aesop; and research funding from IBM Watson Health outside the submitted work. No other disclosures were reported.
                Funding/Support: This study was supported by grant No. R01HS023696 from the Agency for Healthcare Research and Quality.
                Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
                Article
                zoi200265
                10.1001/jamanetworkopen.2020.5547
                7260621
                32469412
                6583cbcc-9a3b-4e27-9af7-28690f6da518
                Copyright 2020 Classen DC et al. JAMA Network Open.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 22 January 2020
                : 19 March 2020
                Categories
                Research
                Original Investigation
                Online Only
                Health Informatics

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