Decision time for clinical decision support systems

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This is a review for O’Sullivan, D., Fraccaro, P., Carson, E., & Weller, P. (2014). PROFESSIONAL ISSUES. Decision time for clinical decision support systems. Clinical Medicine, 14(4), 338-341.


O’Sullivan et al. defines {Clinical decision support systems} ({CDS}Ss) or CDS, which have been defined as systems that provide clinicians or patients with computer-generated clinical knowledge and patient-related information, intelligently filtered or presented at appropriate times, to enhance patient care. Such CDSSs have been the subject of academic computer science research for more than 50 years and offer the potential for better supported decision making by clinicians, improved compliance with medical standards and improved clinical efficiency and safety. [1] Utilization of CDSSs remains limited, according to O’Sullivan et al. Most healthcare IT systems do not include robust CDSS functions that can be widely employed across organizations, clinical presentations and domains, they say. Some of the challenges to CDSS implementation include: the volume of high-quality data required for state-of-the-art systems, the translation of such data to machine-readable states and the mapping of CDSS processes to fi t with existing clinical workflows. Successful implementation of CDSSs has tended to be site and domain specific.[1]

Brief taxonomy of clinical decision support systems

Clinical decision support systems vary widely in their type and complexity according to O’Sullivan et al. Systems can be passive, semi-active or active. They have been implemented to support clinicians across the spectrum of medical specialties and have been customized for different levels of clinical expertise. Simple CDSSs usually check the input provided by a clinician and verify whether the value is allowable or within a specified range and whether there are any predefined contraindications. O’Sullivan et al. states, the output of the CDSS is usually an alert or reminder. Mid-level CDSSs include prognostic calculators and automated clinical practice guideline systems. By coupling a computer-based guideline system with an electronic health record, recommendations can be personalized to the individual patient. Complex CDSSs use artificial intelligence, data mining or statistical methods to reason about the classification or prediction of a disease or patient state. Commonly used techniques include logistic regression, artificial neural networks and support vector machines.[1]

Challenges to implementation and adoption of clinical decision support systems

O’Sullivan et al. found that most literature focuses on operational aspects that act as a barrier to implementation of CDSSs. However, valuable work is being carried out to develop standards and work on developing comprehensive biomedical terminologies. The increasing prevalence of electronic health records should improve data collection. These developments have important implications for CDSSs, state the authors.[1] O’Sullivan et al. contend that significant issues not related to technology will still present challenges. These challenges are related to so-called ‘softer’ elements and include vendors and users of systems, as well as organizational, legal and ethical challenges.[1]

Questions for clinical decision support systems’ stakeholders

> Should clinicians also be educated in the specific computer science methods that underpin CDSSs? [1] > If clinicians gain more knowledge of, and trust in, CDSSs, are vendors of CISs ready to change their approach and focus on the decision-support needs of clinicians in addition to operational tasks? [1] > Should developers of CDSSs bear some responsibility for decisions taken by clinicians based on suggestions from CDSSs? [1] > Finally, if all challenges outlined in this viewpoint were addressed and it became possible to develop CDSSs such that their performance was on a par with the expert clinician, would clinicians want to use them? [1]

Reviewer’s Comments

Jonzy’s Comments

This article is an excellent companion, I think, to Improving Outcomes with Clinical Decision Support: An Implementer’s Guide by Dean Sittig. I would go so far as to recommend it as material for any course regarding CDS development and implementation.


  1. 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 O’Sullivan, D., Fraccaro, P., Carson, E., & Weller, P. (2014). PROFESSIONAL ISSUES. Decision time for clinical decision support systems. Clinical Medicine, 14(4), 338-341.