Improving clinical practice using clinical decision support systems

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Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. K Kawamoto, C A Houlihan, E A Balas, D F Lobach. BMJ (published 14 March 2005). (BMJ, doi:10.1136/bmj.38398.500764.8F)


Clinical decision support systems, which provide clinicians with patient-specific assessments or recommendations to aid clinical decision making, are being increasingly used by healthcare organization to improve quality of care. The attempts to identify system features most important for improving clinical practice, have typically relied on limited number of experts.

Objective: To identify features of clinical decision support systems critical for improving clinical practice.

Design: Systematic review of randomized controlled trials.

Data Sources: Literature searches via Medicine, CINAHL, and the Cochrane Controlled Trials Register up to 2003; and searches of reference lists of included studies and relevant reviews.

Study selection: Inclusion and exclusion criteria are well defined. Studies had to evaluate the ability of decision support systems to improve clinical practice. Both electronic and non-electronic systems were included.

Data extraction: Studies were assessed for statistically and clinically significant improvement in clinical practice and for the presence of 15 decision support system features whose importance had been repeatedly suggested in the literature.

Results: Seventy studies were included. Decision support systems significantly improved clinical practice in 68% (95% CI 56%-78%) of trials. Univariate analyses revealed that, for five of the system features, interventions possessing the feature were significantly more likely to improve clinical practice than interventions lacking the feature.

Multiple logistic regression analysis identified four features as independent predictors of improved clinical practice: automatic provisions of decision support as part of clinician workflow (P<0.00001), provision of recommendations rather than just assessments (P=0.0187), provision of decision support at the time and location of decision making (P=0.0263), and computer based decision support (P=0.0294).  Of 32 systems possessing all four features, 30 (94%) significantly improved clinical practice. In contrast, clinical decision support systems lacking any of the four features improved clinical practice in only 18 out of 39 cases (46%).
Furthermore, direct experimental justification was found for providing periodic performance feedback to clinicians, requesting documentation of reasons for not following recommendations and sharing recommendation with patients.

Strengths & Limitations: The study has several strengths: thorough literature search, generation of potentially important system features based on systematic literature review and quantitative estimation of relative importance of specific clinical decision support features. Potential limitations are: pooling of different type of systems for regression analysis, inclusion of small number of studies reporting patient outcome measures and relatively small sample size compared to the number of explanatory variables.

Conclusions: Several features were closely correlated with decision support systems’ ability to improve patient care significantly. Clinicians and other stakeholders should implement clinical decision support systems that incorporate these features whenever feasible and appropriate.

Comments: The findings are consistent with the findings of other studies. Integration of reminders in work flow facilitates and their non-availability at the point of care impedes implementation of the reminders (Saleem, 2005).