Cited by 305Electronic Health Records SystemsArtificial Intelligence in Healthcare and EducationTechnology and Human Factors in Education and Health

The DOI matches its Crossref record; 81 of the 81 references it cites were found in a registry or an index; none of the 39 sources its claims cite is checked for support yet.

Computerized clinical decision support for prescribing: provision does not guarantee uptake

Open access
Moxey, Annette, Robertson, Jane, Newby, David, Hains, Isla, Williamson, Margaret, Pearson, Sallie-Anne·2010·Journal of the American Medical Informatics Association

Abstract

There is wide variability in the use and adoption of recommendations generated by computerized clinical decision support systems (CDSSs) despite the benefits they may bring to clinical practice. We conducted a systematic review to explore the barriers to, and facilitators of, CDSS uptake by physicians to guide prescribing decisions. We identified 58 studies by searching electronic databases (1990-2007). Factors impacting on CDSS use included: the availability of hardware, technical support and training; integration of the system into workflows; and the relevance and timeliness of the clinical messages. Further, systems that were endorsed by colleagues, minimized perceived threats to professional autonomy, and did not compromise doctor-patient interactions were accepted by users. Despite advances in technology and CDSS sophistication, most factors were consistently reported over time and across ambulatory and institutional settings. Such factors must be addressed when deploying CDSSs so that improvements in uptake, practice and patient outcomes may be achieved.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This systematic review of 58 studies found that making computerized prescribing decision support available does not ensure clinicians use it; uptake depends on organizational, provider, patient, and system-specific factors. Barriers and facilitators were largely stable across time and settings, with workflow integration, technical support, training, message relevance and timing, and autonomy concerns recurring.

Key findings

  • Across 58 studies from 60 manuscripts (1990–2007), factors affecting CDSS use spanned infrastructure and access, technical problems, implementation support, integration, endorsement, roles, liability and privacy; provider knowledge, training, and autonomy; patient characteristics and doctor–patient interaction; and CDSS usability, content, and prompt timing. Up to 96% of alerts may be overridden or ignored.
  • Most included studies examined a specific CDSS (50/58), were conducted since 2000 (43), took place in ambulatory care (38), and were from North America (35); 29 focused only on physicians. Common clinical areas were cardiovascular disease (12), respiratory conditions (5), and antibiotic prescribing (5); 19 focused on drug alerts.
  • Among 50 specific-system studies, 38 used prompts, 33 were integrated with existing software, 28 were system-initiated, and 35 gave assessment plus treatment recommendations. Ease of use, simple and visible messages, workflow fit, and relevant and timely content facilitated use; dense messages, re-entering data, poor timing, and excessive alerts hindered it.
  • Time and setting comparisons showed endorsement and financial incentives mainly in post-2000 studies; earlier studies raised liability and privacy concerns while later ones noted risk mitigation; ambulatory studies more often reported technical support, integration, and patient-related factors; CDSS-specific issues were consistent over time and settings.

Methods

  • Systematic review of peer-reviewed literature; searched Medline, PreMedline, Embase, CINAHL, PsycINFO, INSPEC, Cochrane Database of Systematic Reviews, and hand-searched reference lists; English-language studies from 1990 to 2007.
  • Included studies if they examined electronic decision support or evidence-based information for prescribing, primarily targeted physicians, and reported primary data on barriers or facilitators; editorials and speculation were excluded. 58 studies from 60 manuscripts were included.
  • Data extraction was done by two reviewers with third-reviewer validation; thematic analysis used four domains—organizational, provider, patient, and CDSS-specific—overall and stratified by study period (1990–1999 vs 2000–2007) and setting (ambulatory vs institutional).

Limitations

  • Stated: possible missed studies not in the public domain or outside peer-reviewed literature; heterogeneous designs meant no comprehensive quality assessment; time periods were arbitrary and year of conduct was imputed when missing; the framework may not capture interplay or relative importance; frequency counts do not indicate importance; limited reporting precluded stratifying by specific CDSS features.
  • Evident: many included studies relied on self-reported opinions and experiences; no formal quality appraisal was performed; publication and reporting biases are possible; findings describe barriers and facilitators rather than causal effects on uptake or patient outcomes.

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Integrity signals

Registry

Crossref · 10.1197/jamia.m3170

Cites

81 of 81 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

0 of 39 cited sources checked · 0 supported · 0 flagged

Show all 54 claims

  • Probably not a substantial claim. 35
  • It may state two separate claims as one. 20
50 of 50
  • The authors state this as an assumption.

    Year of study conduct more accurately captures changes over time in factors influencing CDSS uptake than publication year does.

    “Although time periods were defined somewhat arbitrarily, we believed that year of study conduct would more accurately capture any changes in the factors influencing CDSS uptake over time.”
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Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Final study count differs between the figure and its caption

    Minor

    The figure reports 59 studies, but its accompanying text says the review reported 58.

  • Retrieved-article count differs between the figure and results

    Minor

    The figure reports 175 articles retrieved for detailed evaluation, while the results section says there were 174 potentially relevant articles.

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Citations

Cites

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Show all 81 references

Research works printed but not linked: 1

  1. Sowerby Centre for Health Informatics at Newcastle (SCHIN), 1998, PRODIGY phase one summary report. R-38

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Metadata

Authors
Moxey, A., Robertson, J., Newby, D., Hains, I., Williamson, M., & Pearson, S.-A.
Affiliations
  • University of Newcastle Australia (AU)
  • UNSW Sydney (AU)
Corresponding author
Pearson, Sallie-Anne
Venue
Journal of the American Medical Informatics Association
Published
2010-01
Volume
17
Issue
1
Pages
25-33
ISSN
  • 1067-5027 (ISSN-L)
  • 1527-974X
OpenAlex
W2148284486
Cited by
305
References
81
Citations by year
2026: 10 · 2025: 17 · 2024: 20 · 2023: 30 · 2022: 28 · 2021: 22 · 2020: 22 · 2019: 16 · 2018: 15 · 2017: 16 · 2016: 19 · 2015: 26 · 2014: 11 · 2013: 21 · 2012: 14
FWCI
2.40
Type
journal article
Language
en
Open access
Licence
public-domain
Locations
  • Journal of the American Medical Informatics Association (publishedVersion, public-domain)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
University of New South Wales
Topics
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
Keywords
computerized clinical decision support, drug prescribing, technology adoption, barriers to adoption, adoption facilitators, physician acceptance, clinical workflow integration, clinical alerts, professional autonomy, doctor-patient interaction
MeSH terms
Drug Therapy, Computer-Assisted, Humans, Practice Patterns, Physicians', Drug Prescriptions, Decision Support Systems, Clinical

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