Estimating Risk Factors for Patients with Potential Drug-Related Problems Using Electronic Pharmacy Data

Estimating Risk Factors for Patients with Potential Drug-Related Problems Using Electronic Pharmacy Data

OBJECTIVE: To validate a computer-based program to identify patients at high risk for drug-related problems. DESIGN: Computerized analysis of pharmacy dispensing records and manual review of medical records. SETTING: Ambulatory clinics at a Veterans Affairs Medical Center. PATIENTS: 246 randomly sel...

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Bibliographic Details
Published in:The Annals of pharmacotherapy Vol. 33; no. 4; pp. 406 - 412
Main Authors: Isaksen, Sune Faurschou, Jonassen, Jacob, Malone, Daniel C, Billups, Sarah J, Carter, Barry L, Sintek, Charles D
Format: Journal Article
Language:English
Published: Cincinnati, OH SAGE Publications 01-04-1999
Whitney
Subjects:
Biological and medical sciences
Computerized management (medical records, files, hospital management)
Computerized, statistical medical data processing and models in biomedicine
Medical sciences
risk
computers
drug-related problems
Human
Drug
Validation
Methodology
Toxicity
Identification
Forecasting
Risk factor
Secondary effect
Ambulatory
Electronic data interchange
Computer aid
Public health
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Summary:OBJECTIVE: To validate a computer-based program to identify patients at high risk for drug-related problems. DESIGN: Computerized analysis of pharmacy dispensing records and manual review of medical records. SETTING: Ambulatory clinics at a Veterans Affairs Medical Center. PATIENTS: 246 randomly selected patients who were receiving at least one outpatient medication in the previous 24 months. MAIN OUTCOME MEASURES: Presence of six previously established criteria regarding medication use. These criteria are five or more medications, ≥12 doses per day, four or more changes to the medication regimen, three or more chronic diseases, history of noncompliance, and presence of a drug requiring therapeutic drug monitoring (TDM). RESULTS: Spearman rho rank order correlation coefficients ranged from 0.63 to 0.91 for criteria pertaining to the number of medications, daily doses, changes in the medication regimen, and number of chronic diseases (all significant, p = 0.0001). The computer program underestimated the number of chronic diseases and overestimated the number of daily doses. The level of agreement between the computer program and chart review for patient noncompliance was low (Kappa = 0.38), with the computer more likely to indicate a patient was noncompliant. A high level of agreement was seen between the computer program and chart review for the presence of a drug requiring TDM (Kappa = 0.83). For all six criteria, the computer program had a sensitivity of 65.7% and specificity of 88.2%. CONCLUSIONS: When compared with medical records, the use of this program to evaluate electronic pharmacy data can be efficient to screen large numbers of patients who may be at high risk for drug-related problems. This method may be useful for clinical pharmacists in providing pharmaceutical services to patients who are most likely to benefit.
ISSN:1060-0280
1542-6270
DOI:10.1345/aph.18268