Using computerized data to identify adverse drug events in outpatients.
OBJECTIVE
To evaluate the use of a computer program to identify adverse drug events (ADEs) in the ambulatory setting and to evaluate the relative contribution of four computer search methods for identifying ADEs, including diagnosis codes, allergy rules, computer event monitoring rules, and text searching.


METHODS
Retrospective analysis of one year of data from an electronic medical record, including records for 23,064 patients with a primary care physician, of whom 15,665 actually came for care.


METHODS
Presence of an ADE; sensitivity and specificity of computer searches for ADE.


RESULTS
The computer program identified 25,056 incidents, which were associated with an estimated 864 (95 percent confidence interval [CI], 750-978) ADES. Thus, the ADE rate was 5.5 (CI, 5.2-5.9) per 100 patients coming for care. Furthermore, in 79 (CI, 68-89) ADEs, the patient required hospitalization, resulting in an estimated rate of 3.4 (CI, 2.7-4.3) admissions per 1,000 patients. The sensitivity of the search methods for identifying ADEs was estimated to be 58 (CI, 18-98) percent, and the estimated specificity was 88 (CI, 87-88) percent. The positive predictive value was 7.5 (CI, 6.5-8.5) percent, and the negative predictive value was 99.2 (CI, 95.5-99.98) percent. Compared with age and gender-matched controls with no positive screen, patients with ADEs had twice as many outpatient visits and were taking nearly three times as many drugs. Antihypertensives, ACE-inhibitors, antibiotics, and diuretics were associated with 56 (CI, 47-65) percent of ADES. Among ADEs, 23 (CI, 16-32) percent were life-threatening or serious, and 38 (CI, 29-47) percent were judged preventable.


CONCLUSIONS
Computerized search programs can detect ADEs, and free-text searches were especially useful. Adverse drug events were frequent, and admissions were not rare, although most hospitals today do not identify them. Thus, such detection programs demonstrate "value-added" for the electronic record and may be useful for directing and assessing the impact of quality improvement efforts.
Honigman
B
B
University of Colorado Health Sciences Center, Denver, USA. benjamin.honigman@uchsc.edu
Lee
J
J
Rothschild
J
J
Light
P
P
Pulling
R M
RM
Yu
T
T
Bates
D W
DW
D016907:Adverse Drug Reaction Reporting Systems; D000465:Algorithms; D000553:Ambulatory Care; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D016247:Information Storage and Retrieval; D012189:Retrospective Studies; D012984:Software; D013223:Statistics as Topic