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Original Investigation | Health Care Reform

Efficacy of an Evidence-Based Clinical Decision Support in Primary Care Practices:  A Randomized Clinical Trial

Thomas G. McGinn, MD, MPH1; Lauren McCullagh, MPH1; Joseph Kannry, MD2; Megan Knaus, MPH1; Anastasia Sofianou, MS2; Juan P. Wisnivesky, MD, DrPH2; Devin M. Mann, MD, MS3
[+] Author Affiliations
1Division of Internal Medicine, Department of Medicine, Hofstra North Shore–LIJ School of Medicine, Manhasset, New York
2Division of Internal Medicine, Department of Medicine, Mount Sinai School of Medicine, New York, New York
3Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
JAMA Intern Med. 2013;173(17):1584-1591. doi:10.1001/jamainternmed.2013.8980.
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Importance  There is consensus that incorporating clinical decision support into electronic health records will improve quality of care, contain costs, and reduce overtreatment, but this potential has yet to be demonstrated in clinical trials.

Objective  To assess the influence of a customized evidence-based clinical decision support tool on the management of respiratory tract infections and on the effectiveness of integrating evidence at the point of care.

Design, Setting, and Participants  In a randomized clinical trial, we implemented 2 well-validated integrated clinical prediction rules, namely, the Walsh rule for streptococcal pharyngitis and the Heckerling rule for pneumonia.

Interventions and Main Outcomes and Measures  The intervention group had access to the integrated clinical prediction rule tool and chose whether to complete risk score calculators, order medications, and generate progress notes to assist with complex decision making at the point of care.

Results  The intervention group completed the integrated clinical prediction rule tool in 57.5% of visits. Providers in the intervention group were significantly less likely to order antibiotics than the control group (age-adjusted relative risk, 0.74; 95% CI, 0.60-0.92). The absolute risk of the intervention was 9.2%, and the number needed to treat was 10.8. The intervention group was significantly less likely to order rapid streptococcal tests compared with the control group (relative risk, 0.75; 95% CI, 0.58-0.97; P = .03).

Conclusions and Relevance  The integrated clinical prediction rule process for integrating complex evidence-based clinical decision report tools is of relevant importance for national initiatives, such as Meaningful Use.

Trial Registration  clinicaltrials.gov Identifier: NCT01386047

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Figures

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Figure 1.
Study Activities for Health Care Providers in the Control and Intervention Groups

EHR indicates electronic health record.

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Figure 2.
Integrated Clinical Prediction Rule (iCPR)

iCPR tool workflow in the electronic medical health record.

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