Clinical Decision Support Systems for the Practice of Evidence-based Medicine

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This is a systematic review of the article entitled “Clinical Decision Support Systems for the Practice of Evidence-based Medicine” by Ida Sim, MD, PhD [1]


Clinical decision support systems (CDSSs) are recognized for their capacity to reduce medical errors and increase quality of care. Similarly, Evidence Based Medicine (EBM) has also been noted as a means to improve clinical outcomes. Therefore the use of CDSSs to encourage evidence-based medicine is highly regarded as a means to secure improvement in health care. The authors of this article sought to specify the actions of the Evidence and Decision Support Track of the Spring 2000 AMIA Symposium. The goal of the symposium was to evaluate the challenges facing the implementation of CDSS-facilitated evidence based medicine. In particular, the authors provided research and policy recommendations increasing the development and adoption of CDSSs for evidence-based medicine.


  • EBM: Involves the management of individual patients through clinical expertise in collaboration with the judicious use the current best evidence from clinical care research and scientific literature.
  • CDSS: Software designed to directly facilitate clinical decision-making.
  • Evidence-adaptive CDSS: These systems contain a clinical knowledge base that is derived from and reflects the most current evidence from research literature and practice-based sources.


The following process was used to derive five areas of focus that are critical to the goal of increased adoption of CDSSs for evidence based medicine:

  • Collection of literature-based and practice-based research evidence into machine-interpretable formats appropriate for CDSS use.
  • Institution of a technical and methodological basis for applying research evidence to individual patients at point of care.
  • Assessment of clinical effects and costs of CDSSs.
  • Promotion of the effectual implementation and use of CDSSs that have demonstrated positive clinical performance or outcomes.
  • Establishment of policies that offer incentives for implementing CDSSs to improve health care quality.

The Role of Evidence in Evidence-adaptive CDSSs

The authors noted that Clinical Decision Support Systems can only be as useful as the strength of the underlying evidence base. Also, this evidence must be current, accessible and machine interpretable. The following are the types of evidence that are encourage evidence-adaptive CDSSs:

  • Literature-based Evidence
  • Practice-based Evidence
  • Patient-directed Evidence


The following steps were recommended for researchers, developers and implementers in order to increase adoption of evidence-adaptive CDSSs:

Clinical and Informatics Researchers

  • Conduct better quality clinical research on the effectiveness of clinical interventions especially in primary care settings.
  • Develop better methods for interpreting results from a wide range of study designs.
  • Develop machine-interpretable repositories of guidelines that can be linked to current evidence-based repositories.
  • Build standard interfaces among repositories to allow evidence to be linked automatically to among other systems.
  • Develop an informatics infrastructure for practice-based research networks to collect practice-based evidence.

Researchers and Developers

  • Continue development of a comprehensive, expressive clinical vocabulary that can scale from administrative to clinical decision support needs.
  • Explore and develop automatic methods for updating CDSS knowledge bases to reflect the current state and quality of the literature-based evidence.
  • Develop models of decision making that can simultaneously accommodate the beliefs, perspectives, and values of multiple decision makers, including those of physicians and patients.

Current CDSS Developers

  • Adopt and use standard vocabularies and standards for knowledge representation as they become available.
  • Integrate CDSSs with electronic medical records and other relevant systems using appropriate interoperability standards such as HL7.

Policy Makers, Manufacturers and Organizations

  • Fund development and demonstration of inter-organizational sharing of evidence-based knowledge and its application in diverse CDSSs.

Evaluation of Clinical Decision Support Systems

Even with the promise of CDSSs for improving care, evaluations have shown that CDSSs have only a modest capacity to improve transitional measures such as guideline adherence and drug dosing accuracy. In other words, the effect of CDSSs on clinical outcomes remains uncertain.


  • Evaluate CDSSs using an approach that identifies both benefits and unanticipated problems related to CDSS implementation and use.
  • Conduct more CDSS evaluations in actual practice settings.
  • Establish partnerships between academic groups and community practices to conduct evaluations.

CDSS Implementer

  • Establish a CDSS implementation team composed of clinicians, information technologists, managers, and evaluators to work together to adapt and implement the CDSS.
  • Plan for work flow re-engineering, organizational, and social issues and incorporate change management techniques into system development and implementation.

Policy Makers

  • Develop financial and reimbursement policies that provide incentives for health-care providers to implement and use CDSSs.
  • Develop and implement financial and reimbursement policies that reward the acquisition of measurable quality goals, as might be achieved by CDSSs.
  • Promote organization and leadership across the health care and clinical research sectors to leverage informatics promotion and development efforts by government, industry, AMIA, and others.


The combination of CDSS technology with evidence-based medicine brings together two potentially powerful methods for improving health care quality. Literature-based and practice-based evidence must be collected into computable knowledge bases, technical and methodological foundations for evidence-adaptive CDSSs must be developed and maintained, and public policies must be established to finance the implementation of electronic medical records and CDSSs and to reward health care quality improvement through the use of evidence-based CDSSs.


  1. Clinical Decision Support Systems for the Practice of Evidence-based Medicine. [J Am Med Inform Assoc. 2001 Nov-Dec; 8(6): 527–534]

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This article provides an excellent overview of the use of clinical decision support in collaboration with evidence-based medicine to provide excellent quality of care. Policy makers, organizations, researchers and innovators alike would find this study useful, especially in acquiring foundational knowledge of this topic.