This evidence report is part of a three-report series focusing on the strategic goals of the Agency for Healthcare Research and Quality's (AHRQ's) health information technology (Health IT) portfolio. This report specifically explores facilitating health care decisionmaking through Health IT. As the level of sophistication of electronic health records (EHRs) increases, the need for more sophisticated clinical decision support systems (CDSSs) and electronic knowledge management systems (KMSs) is imperative, as is the need for better operational use of these systems. The goals of this report are to summarize the available evidence related to CDSSs and KMSs, highlight the limitations of the evidence, and identify areas for future research.
Objectives: To catalogue study designs used to assess the clinical effectiveness of CDSSs and KMSs, to identify features that impact the success of CDSSs/KMSs, to document the impact of CDSSs/KMSs on outcomes, and to identify knowledge types that can be integrated into CDSSs/KMSs.
Data Sources: MEDLINE®, CINAHL®, PsycINFO®, and Web of Science®.
Review Methods: We included studies published in English from January 1976 through December 2010. After screening titles and abstracts, full-text versions of articles were reviewed by two independent reviewers. Included articles were abstracted to evidence tables by two reviewers. Meta-analyses were performed for seven domains in which sufficient studies with common outcomes were included.
Results: We identified 15,176 articles, from which 323 articles describing 311 unique studies including 160 reports on 148 randomized control trials (RCTs) were selected for inclusion. RCTs comprised 47.5 percent of the comparative studies on CDSSs/KMSs. Both commercially and locally developed CDSSs effectively improved health care process measures related to performing preventive services (n = 25; OR 1.42, 95% confidence interval [CI] 1.27 to 1.58), ordering clinical studies (n = 20; OR 1.72, 95% CI 1.47 to 2.00), and prescribing therapies (n = 46; OR 1.57, 95% CI 1.35 to 1.82).
Fourteen CDSS/KMS features were assessed for correlation with success of CDSSs/KMSs across all endpoints. Meta-analyses identified six new success features:
- Integration with charting or order entry system.
- Promotion of action rather than inaction.
- No need for additional clinician data entry.
- Justification of decision support via research evidence.
- Local user involvement.
- Provision of decision support results to patients as well as providers.
Three previously identified success features were confirmed:
- Automatic provision of decision support as part of clinician workflow.
- Provision of decision support at time and location of decisionmaking.
- Provision of a recommendation, not just an assessment.
Only 29 (19.6%) RCTs assessed the impact of CDSSs on clinical outcomes, 22 (14.9%) assessed costs, and 3 assessed KMSs on any outcomes. The primary source of knowledge used in CDSSs was derived from structured care protocols.
Conclusions: Strong evidence shows that CDSSs/KMSs are effective in improving health care process measures across diverse settings using both commercially and locally developed systems. Evidence for the effectiveness of CDSSs on clinical outcomes and costs and KMSs on any outcomes is minimal. Nine features of CDSSs/KMSs that correlate with a successful impact of clinical decision support have been newly identified or confirmed.
Enabling Health Care Decisionmaking Through Clinical Decision Support and Knowledge Management
Evidence-based Practice Center: Duke EPC