Meeting Brochure and registration form      SMDM Homepage

Sunday, 15 October 2006


Thomas Stern, MD, MS, Carolinas Medical Center, Charlotte, NC, Asha Garg, MD, MPH, Case Western Reserve University School of Medicine, Cleveland, OH, Neal V. Dawson, MD, Metrohealth Medical Center, Cleveland, OH, Nancy Messina, RRT, Division of Pulmonary, Critical care, and Sleep Medicine, MetroHealth Medical Center, Cleveland, OH, and E. Regis McFadden, MD, Center for Academic Clinical Research, General Clinical Research Center, Case Western Reserve University, Cleveland, OH.

Purpose: Primary care practitioners have had difficulty implementing published guidelines for the diagnosis and management of asthma. Computerized decision support systems are tools that have been proven to improve compliance with published guidelines. This study's objective is to assess the accuracy of a computerized asthma decision support system in recognizing severe asthma using expert asthma clinicians as the reference standard.

Methods: This study is an accuracy assessment of 69 consecutive patients seen in an urban asthma subspecialty clinic between August 1 and November 30, 2004. Patients' asthma was classified as “severe” or “mild” both by the decision support system and expert asthma clinicians who were blinded to the assessment of the decision support system. Accuracy was quantified using percent accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and positive and negative likelihood ratios using the expert asthma clinicians as the reference standard.

Results: The decision support system had a 91% accuracy of recognizing severe asthma defined by expert asthma clinicians. The sensitivity of the decision support system for severe asthma was 96%, the specificity was 73%, the positive predictive value was 93%, the negative predictive value was 85%, the positive likelihood ratio was 3.6 (95% CI: 1.6, 8.4), and the negative likelihood ratio was 0.05 (95% CI: 0.012, 0.21).

Conclusions: The asthma decision support system was able to discern “mild” from “severe” asthma in a similar fashion to expert asthma clinicians. The effect of the decision support system on patient outcomes should be assessed.

See more of Poster Session I
See more of The 28th Annual Meeting of the Society for Medical Decision Making (October 15-18, 2006)