Clinical decision support and electronic interventions to improve care quality in chronic liver diseases and cirrhosis

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Abstract

Significant quality gaps exist in the management of chronic liver diseases and cirrhosis. Clinical decision support systems—information-driven tools based in and launched from the electronic health record—are attractive and potentially scalable prospective interventions that could help standardize clinical care in hepatology. Yet, clinical decision support systems have had a mixed record in clinical medicine due to issues with interoperability and compatibility with clinical workflows. In this review, we discuss the conceptual origins of clinical decision support systems, existing applications in liver diseases, issues and challenges with implementation, and emerging strategies to improve their integration in hepatology care.

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Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review.

Amit X Garg, Neill K. J. Adhikari, Heather McDonald … (2005)

Developers of health care software have attributed improvements in patient care to these applications. As with any health care intervention, such claims require confirmation in clinical trials. To review controlled trials assessing the effects of computerized clinical decision support systems (CDSSs) and to identify study characteristics predicting benefit. We updated our earlier reviews by searching the MEDLINE, EMBASE, Cochrane Library, Inspec, and ISI databases and consulting reference lists through September 2004. Authors of 64 primary studies confirmed data or provided additional information. We included randomized and nonrandomized controlled trials that evaluated the effect of a CDSS compared with care provided without a CDSS on practitioner performance or patient outcomes. Teams of 2 reviewers independently abstracted data on methods, setting, CDSS and patient characteristics, and outcomes. One hundred studies met our inclusion criteria. The number and methodologic quality of studies improved over time. The CDSS improved practitioner performance in 62 (64%) of the 97 studies assessing this outcome, including 4 (40%) of 10 diagnostic systems, 16 (76%) of 21 reminder systems, 23 (62%) of 37 disease management systems, and 19 (66%) of 29 drug-dosing or prescribing systems. Fifty-two trials assessed 1 or more patient outcomes, of which 7 trials (13%) reported improvements. Improved practitioner performance was associated with CDSSs that automatically prompted users compared with requiring users to activate the system (success in 73% of trials vs 47%; P = .02) and studies in which the authors also developed the CDSS software compared with studies in which the authors were not the developers (74% success vs 28%; respectively, P = .001). Many CDSSs improve practitioner performance. To date, the effects on patient outcomes remain understudied and, when studied, inconsistent.

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An overview of clinical decision support systems: benefits, risks, and strategies for success

Reed Sutton, David Pincock, Daniel Baumgart … (2020)

Computerized clinical decision support systems, or CDSS, represent a paradigm shift in healthcare today. CDSS are used to augment clinicians in their complex decision-making processes. Since their first use in the 1980s, CDSS have seen a rapid evolution. They are now commonly administered through electronic medical records and other computerized clinical workflows, which has been facilitated by increasing global adoption of electronic medical records with advanced capabilities. Despite these advances, there remain unknowns regarding the effect CDSS have on the providers who use them, patient outcomes, and costs. There have been numerous published examples in the past decade(s) of CDSS success stories, but notable setbacks have also shown us that CDSS are not without risks. In this paper, we provide a state-of-the-art overview on the use of clinical decision support systems in medicine, including the different types, current use cases with proven efficacy, common pitfalls, and potential harms. We conclude with evidence-based recommendations for minimizing risk in CDSS design, implementation, evaluation, and maintenance.

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Author and article information

Contributors

Jin Ge: (View ORCID Profile)

Valy Fontil: (View ORCID Profile)

Sara Ackerman: (View ORCID Profile)

Mark J. Pletcher: (View ORCID Profile)

Jennifer C. Lai: (View ORCID Profile)

Journal

Title: Hepatology

ISSN (Print): 0270-9139

Publication date (Electronic): August 23 2023

Affiliations

[1 ]Department of Medicine, Division of Gastroenterology and Hepatology, University of California – San Francisco, San Francisco, California, USA

[2 ]Department of Medicine, NYU Grossman School of Medicine and Family Health Centers at NYU-Langone Medical Center, Brooklyn, New York, USA

[3 ]Department of Social and Behavioral Sciences, University of California – San Francisco, San Francisco, California, USA

[4 ]Department of Epidemiology and Biostatistics, University of California – San Francisco, San Francisco, California, USA

Article

DOI: 10.1097/HEP.0000000000000583

SO-VID: 3c9c943e-346f-4a59-923a-99cde6f457c9

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