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Computerised clinical decision support systems and absolute improvements in care: meta-analysis of controlled clinical trials

BMJ 2020; 370 doi: https://doi.org/10.1136/bmj.m3216 (Published 17 September 2020) Cite this as: BMJ 2020;370:m3216

Linked Editorial

How effective are clinical decision support systems?

Linked Opinion

What I have learned about clinical decision support systems over the past decade

  1. Janice L Kwan, assistant professor1 2,
  2. Lisha Lo, research coordinator3,
  3. Jacob Ferguson, medical student4,
  4. Hanna Goldberg, resident physician4,
  5. Juan Pablo Diaz-Martinez, doctoral student5,
  6. George Tomlinson, professor5,
  7. Jeremy M Grimshaw, professor6,
  8. Kaveh G Shojania, professor2 3 7
  1. 1Sinai Health System, Department of Medicine, 600 University Avenue, Toronto, ON M5G 1X5, Canada
  2. 2Department of Medicine, University of Toronto, Toronto, ON, Canada
  3. 3Centre for Quality Improvement and Patient Safety, University of Toronto, Toronto, ON, Canada
  4. 4Faculty of Medicine, University of Toronto, Toronto, ON, Canada
  5. 5Biostatistics Research Unit, University Health Network and Sinai Health System, Toronto, ON, Canada
  6. 6Clinical Epidemiology Program, Ottawa Hospital Research Institute and Department of Medicine, University of Ottawa, Ottawa, ON, Canada
  7. 7Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
  1. Correspondence to: Janice L Kwan janice.kwan{at}utoronto.ca (or @KwanJanice on Twitter)
  • Accepted 7 August 2020

Abstract

Objective To report the improvements achieved with clinical decision support systems and examine the heterogeneity from pooling effects across diverse clinical settings and intervention targets.

Design Systematic review and meta-analysis.

Data sources Medline up to August 2019.

Eligibility criteria for selecting studies and methods Randomised or quasi-randomised controlled trials reporting absolute improvements in the percentage of patients receiving care recommended by clinical decision support systems. Multilevel meta-analysis accounted for within study clustering. Meta-regression was used to assess the degree to which the features of clinical decision support systems and study characteristics reduced heterogeneity in effect sizes. Where reported, clinical endpoints were also captured.

Results In 108 studies (94 randomised, 14 quasi-randomised), reporting 122 trials that provided analysable data from 1 203 053 patients and 10 790 providers, clinical decision support systems increased the proportion of patients receiving desired care by 5.8% (95% confidence interval 4.0% to 7.6%). This pooled effect exhibited substantial heterogeneity (I2=76%), with the top quartile of reported improvements ranging from 10% to 62%. In 30 trials reporting clinical endpoints, clinical decision support systems increased the proportion of patients achieving guideline based targets (eg, blood pressure or lipid control) by a median of 0.3% (interquartile range −0.7% to 1.9%). Two study characteristics (low baseline adherence and paediatric settings) were associated with significantly larger effects. Inclusion of these covariates in the multivariable meta-regression, however, did not reduce heterogeneity.

Conclusions Most interventions with clinical decision support systems appear to achieve small to moderate improvements in targeted processes of care, a finding confirmed by the small changes in clinical endpoints found in studies that reported them. A minority of studies achieved substantial increases in the delivery of recommended care, but predictors of these more meaningful improvements remain undefined.

Footnotes

  • Contributors: JLK, LL, JMG, and KGS led the study design. JLK, LL, JF, HG, and KGS extracted the data. JLK, JPDM, GT, and KGS analysed the data. JLK and KGS drafted the manuscript. All authors provided critical revision of the manuscript for important intellectual content and approval of the final submitted version. JLK and KGS are the guarantors. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

  • Funding: JMG holds a Canada Research Chair in health knowledge transfer and uptake. The funders had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication.

  • Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

  • Ethical approval: Ethical approval for this evidence synthesis was not required.

  • Data sharing: No additional data available.

  • The lead author (the manuscript’s guarantor) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

  • Dissemination to participants and related patient and public communities: Dissemination of the study results to study participants is not applicable. We plan to use media outreach (eg, press release) and social media to disseminate our findings and communicate with the population at large.

  • Provenance and peer review: Not commissioned; externally peer reviewed.

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