ArticleEGEMS (Washington, DC)2019
Looking Behind the Curtain: Identifying Factors Contributing to Changes on Care Outcomes During a Large Commercial EHR Implementation.
Article in EGEMS (Washington, DC), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Team is brain: leveraging EHR audit log data for new insights into acute care processes.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Department-focused electronic health record thrive training.JAMIA open · 2022Article
- Modernizing health information technology: lessons from healthcare delivery systems.JAMIA open · 2020Article
- Twilighted Homegrown Systems: The Experience of Six Traditional Electronic Health Record Developers in the Post-Meaningful Use Era.Applied clinical informatics · 2020Article
- Capturing Clinician Reasoning in Electronic Health Records: An Exploratory Study of Under-Treated Essential Hypertension.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Health information technology as a learning health system: Call for a national monitoring system.Learning health systems · 2020Article
- An Evaluation of System End-User Support during Implementation of an Electronic Health Record Using the Model for Improvement Framework.Applied clinical informatics · 2019Article
- Unintended Consequences of Nationwide Electronic Health Record Adoption: Challenges and Opportunities in the Post-Meaningful Use Era.Journal of medical Internet research · 2019Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectiveTo identify factors contributing to changes on quality, productivity, and safety outcomes during a large commercial electronic health record (EHR) implementation and to guide future research.
methodsWe conducted a mixed-methods study assessing the impact of a commercial EHR implementation. The method consisted of a quantitative longitudinal evaluation followed by qualitative semi-structured, in-depth interviews with clinical employees from the same implementation. Fourteen interviews were recorded and transcribed. Three authors independently coded interview narratives and via consensus identified factors contributing to changes on 15 outcomes of quality, productivity, and safety.
resultsWe identified 14 factors that potentially affected the outcomes previously monitored. Our findings demonstrate that several factors related to the implementation (e.g., incomplete data migration), partially related (e.g., intentional decrease in volume of work), and not related (e.g., health insurance changes) may affect outcomes in different ways. DISCUSSION: This is the first study to investigate factors contributing to changes on a broad set of quality, productivity, and safety outcomes during an EHR implementation guided by the results of a large longitudinal evaluation. The diversity of factors identified indicates that the need for organizational adaptation to take full advantage of new technologies is as important for health care as it is for other services sectors.
conclusionsWe recommend continuous identification and monitoring of these factors in future evaluations to hopefully increase our understanding of the full impact of health information technology interventions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.