The potential of training to increase acceptance and use of computerized decision support systems for medical diagnosis.
OBJECTIVE
The goals of this study were to understand the reasons underlying the limited use of medical decision-support tools and to explore the potential of a computer-based tutorial to mitigate barriers to use.


BACKGROUND
Medical decision-support tools such the Acute Cardiac Ischemia Time-Insensitive Predictive Instrument (ACI-TIPI) have demonstrated statistical validity and clinical impact for patient safety but have seen limited adoption and use.


METHODS
The study developed a brief Web-based "demystifying" ACI-TIPI tutorial employing case-based training and evaluated the effectiveness of that tutorial in changing self-reported attitudes and behaviors.


RESULTS
Clinicians using the tutorial reported greater understanding of how to use the ACI-TIPI score appropriately and increased confidence in the score. Case studies in the tutorial that provided examples of how to use the score for actual cases were rated as especially helpful.


CONCLUSIONS
This study suggests that a primary barrier to the use of statistical decision support tools for patient diagnosis is lack of training or experience in combining a population-based numerical risk score with other diagnostic information about the individual patient's case that is not considered in that score. The results of this study indicate that there is a potential for a relatively brief tutorial to increase acceptance and use of decision support tools for medical diagnosis.


CONCLUSIONS
These findings have the potential for the identification of methods to help clinicians learn how to use statistical and probabilistic information to better assess risk and to promote integration of decision support tools into medical decision making for improvement of patient safety.
Lai
Fuji
F
Aptima, Inc., 12 Gill St., Suite 1400, Woburn, MA 01801, USA. fujilai@aptima.com
Macmillan
Jean
J
Daudelin
Denise H
DH
Kent
David M
DM
D001291:Attitude of Health Personnel; D003194:Computer-Assisted Instruction; D020000:Decision Support Systems, Clinical / Q000706:statistics & numerical data*; D006801:Humans; D007318:Inservice Training; D017202:Myocardial Ischemia / Q000175:diagnosis; D014481:United States