Improving recognition of drug interactions: benefits and barriers to using automated drug alerts.
BACKGROUND
Clinicians' perceptions about decision support systems may impact the effectiveness of these technologies.


OBJECTIVE
To explore clinicians' baseline knowledge of common drug interactions and experiences with automated drug alerts within a provider order entry system as a means to better understand the potential benefits and barriers to using this technology.


METHODS
Cross-sectional survey.


METHODS
The study population comprised 263 clinicians practicing within a Southern California Veterans Affairs health care system that used VA's Computerized Patient Record System (CPRS). Response rate was 64%.


METHODS
A 67-item survey (19 questions) was developed to elicit information including: (1) computer use for patient-related activities; (2) recognition of drug interactions; and (3) benefits and barriers to using automated drug alerts.


RESULTS
Clinicians correctly categorized 44% (range 11-64%) of all drug-drug pairs, 53% of interacting combinations, and 54% of contraindicated pairs. Providers also correctly categorized 55% (range 24-87%) of 11 drug-disease pairs and 62% of interacting combinations, and 53% of contraindicated pairs. Nearly 90% of clinicians thought drug alerts would be helpful to identify interactions yet 55% of clinicians perceived that the most significant barrier to utilizing existing alerts was poor signal to noise ratio, meaning too many nonrelevant warnings.


CONCLUSIONS
Automated drug interaction alerts have the potential to dramatically increase clinicians' recognition of selected drug interactions. However, perceived poor specificity of drug alerts may be an important obstacle to efficient utilization of information and may impede the ability of such alerts to improve patient safety.
Glassman
Peter A
PA
VA HSR&D Center of Excellence for the Study of Healthcare Provider Behavior, VA Greater Los Angeles Healthcare System-West Los Angeles Campus, 11301 Wilshire Boulevard, Los Angeles, CA 90073, USA. Peter.Glassman@med.va.gov
Simon
Barbara
B
Belperio
Pamela
P
Lanto
Andrew
A
D001291:Attitude of Health Personnel; D001331:Automation; D002140:California; D016009:Chi-Square Distribution; D003199:Computer Systems; D003430:Cross-Sectional Studies; D020000:Decision Support Systems, Clinical; D004347:Drug Interactions; D004360:Drug Therapy, Computer-Assisted; D006787:Hospitals, Veterans; D006801:Humans; D016347:Medical Records Systems, Computerized; D008508:Medication Errors / Q000517:prevention & control*; D012044:Regression Analysis; D011795:Surveys and Questionnaires