Features of effective computerized clinical decision support systems: meta-regression of 162 randomized trials

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  • Objectives: To identify factors that differentiate between effective and ineffective computerised clinical decision support systems in terms of improvements in the process of care or in patient outcomes.
  • Data sources: A database of features and effects of these support systems derived from 162 randomised controlled trials identified in a recent systematic review. Trialists were contacted to confirm the accuracy of data and to help prioritise features for testing.
  • Main outcome: measures “Effective” systems were defined as those systems that improved primary (or 50% of secondary) reported outcomes of process of care or patient health. Simple and multiple logistic regression models were used to test characteristics for association with system effectiveness with several sensitivity analyses.
  • Results: Systems that presented advice in electronic charting or order entry system interfaces were less likely to be effective (odds ratio 0.37, 95% confidence interval 0.17 to 0.80). Systems more likely to succeed provided advice for patients in addition to practitioners (2.77, 1.07 to 7.17), required practitioners to supply a reason for over-riding advice (11.23, 1.98 to 63.72), or were evaluated by their developers (4.35, 1.66 to 11.44). These findings were robust across different statistical methods, in internal validation, and after adjustment for other potentially important factors.
  • Conclusions: We identified several factors that could partially explain why some systems succeed and others fail. Presenting decision support within electronic charting or order entry systems are associated with failure compared with other ways of delivering advice. Odds of success were greater for systems that required practitioners to provide reasons when over-riding advice than for systems that did not. Odds of success were also better for systems that provided advice concurrently to patients and practitioners. Finally, most systems were evaluated by their own developers and such evaluations were more likely to show benefit than those conducted by a third party.



Clinical Decision Support (CDSS) provide to physicians a tool that could help to make better and more informed decisions at the point of care. However, issues such as workflow disruption, lack of adequate training as well as cost of implementation prevent the wide adoption of CDSS.


Evidence-based medicine provides to clinicians resources that can potentially decrease the amount of mistakes committed in the medical setting, enhancing the quality of healthcare services. In order to successfully implement a clinical decision support system there are important features and elements that a system should have in order to be efficient.


Meta-regression analysis of randomized controlled trials


Analysis was based on a dataset of 162 randomized controlled trials in a recent series of systematic reviews of computerized clinical decision support systems.


Results showed that systems presenting advice within electronic health records or order entry systems were less likely to improve outcomes when compared with standalone programs. Moreover, systems more likely to succeed are those that involve both clinician and patient probably because they make patients be more involved in their own care; however, the estimate of association was imprecise and requires further study.[1]


Researchers’ main objective was to identify elements that differentiate between effective and ineffective CDSS. Providing advice to both practitioners and patients as well as requiring practitioners to give explanations for over-riding advice, might independently improve success.


  1. Features of effective computerized clinical decision support systems: meta-regression of 162 randomized trials http://www.bmj.com/content/346/bmj.f657.full.pdf+html