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First published October 5, 2003 as JAMIA PrePrint; doi:10.1197/jamia.M1166
Journal of the American Medical Informatics Association 2004;11(1):71-77
© 2004 American Medical Informatics Association

A more recent version of this article appeared on January 1, 2004
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Submitted on May 30, 2002
Accepted on August 16, 2003

Comparative impact of guidelines, clinical data and decision support on prescribing decisions: an interactive web experiment with simulated cases

Vitali Sintchenko MD1*, Enrico Coiera MB, BS, PhD1, Jonathan R. Iredell MD, PhD2, and Gwendolyn L. Gilbert MD3

Affiliation of the authors: 1 Centre for Health Informatics, University of New South Wales, Sydney, NSW, Australia; 2 Centre for Infectious Diseases and Microbiology and Intensive Care Unit, Westmead Hospital, Westmead; University of Sydney, NSW, Australia; 3 University of Sydney, NSW, Australia; Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, Westmead Hospital, Westmead, NSW, Australia

* To whom correspondence should be addressed.

Objective To compare the clinical impact of computerized decision support with and without electronic access to clinical guidelines and laboratory data on antibiotic prescribing decisions.

Design A cross-over trial of four levels of computerized decision support - no support, antibiotic guidelines, laboratory reports, and laboratory reports plus a decision support system (DSS), randomly allocated to eight simulated clinical cases accessed by the web.

Measurements Rate of intervention adoption, measured by frequency of accessing information support; cost of use, measured by time taken to complete each case; effectiveness of decision, measured by correctness of and self-reported confidence in individual prescribing decisions. Clinical impact score measured by adoption rate and decision effectiveness.

Results 31 intensive care (ICP) and infectious disease (IDP) specialist physicians participated in the study. Ventilator associated pneumonia treatment guidelines were used in 24 (39%) of the 62 case scenarios for which they were available, microbiology reports in 36 (58%) and the DSS in 37 (60%). The use of all forms of information support did not affect clinicians' confidence in their decisions. Their use of the DSS plus microbiology report improved the agreement of decisions with those of an expert panel from 65% to 97% (p = 0.0002), or to 67% (p = 0.002) when antibiotic guidelines only were accessed. Significantly fewer IDP than ICP accessed information support in making treatment decisions. On average, it took 245 seconds to make a decision using the DSS compared with 113 seconds for unaided prescribing (p < 0.001). The DSS plus microbiology reports had the highest clinical impact score (0.58), greater than that of electronic guidelines (0.26) and electronic laboratory reports (0.45).

Conclusion When used, computer-based decision support significantly improved decision quality. In measuring the impact of decision support systems, both their effectiveness in improving decisions as well as their likely rate of adoption in the clinical environment need to be considered. Clinicians chose to use antibiotic guidelines for one-third and microbiology reports or the DSS for about two-thirds of cases when they were available to assist their prescribing decisions.




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