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Monday, 18 October 2004

This presentation is part of: Poster Session - CEA: Methods and Applications; Health Services Research

PREDICTORS OF USE OF HANDHELD DECISION SUPPORT TOOLS IN THE CLINICAL SETTING

Feliciano B. Yu, MD, MSHI1, Thomas K. Houston, MD, MPH1, Midge N. Ray, RN, MSN2, and Eta S. Berner, Ed.D.3. (1) University of Alabama at Birmingham, Center for Outcomes and Effectiveness Research and Education, Department of Preventive Medicine, Birmingham, AL, (2) University of Alabama at Birmingham, Department of Health Services Adnmistration, Birmingham, AL, (3) University of Alabama at Birmingham, Health Services Administration Department, Health Informatics Program, Birmingham, AL

PURPOSE: To identify factors associated with the use of handheld clinical decision support tools by Internal Medicine providers in clinical settings.

METHODS: 82 internal medicine residents of an urban teaching hospital were given personal digital assistants (PDAs), each containing a suite of clinical decision support programs, which include MedMath, MedCalc, and ePocrates, among others. Descriptive data including age, sex, race, years in residency training, previous ownership with PDA and perceived barriers to PDAs were collected on baseline surveys. A tracking program was used to prospectively track the number of times a program was entered. The program was considered to be used by the resident, if the program was accessed 3 or more times during the follow-up period. Our outcome, “breadth of use,” was defined as the number of programs used by a physician during follow-up (an ordinal variable ranging from 0 to 6). Multivariate analyses were conducted using linear regression.

RESULTS: 68 physicians were included in the study. Thirty-two percent were postgraduate year one (PGY1), 38% PGY2, and 29% PGY3. Most residents were white (76%), male (75%), and over half had previously owned a PDA (59%). Data were collected at least once (mean data collection interval=5.6 months). Nineteen percent of the residents did not use any of the installed programs. Among those who used the programs, ePocrates was accessed more often than the others. Mean breadth of use was 2.6 programs (SD=2.0, range=0 to 6). Mean use declined by residency year [PGY1=3.4 (SD=1.8); PGY2=2.6 (2.2) and PGY3=1.8 (1.8)]. Univariate analysis showed that higher levels of residency training were associated with a decrease in breadth of use of handheld clinical decision support tools. Independent effects were not observed during multivariable analysis due to a limited sample size.

CONCLUSION: Our data show that increasing resident years is associated with using a smaller range of handheld decision support tools in the clinical setting. Further study is needed to identify barriers to using handheld decision support tools and develop effective strategies to overcome those barriers.


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