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Estimating risk factors for patients with potential drug-related problems using electronic pharmacy data. IMPROVE
S F Isaksen1, J Jonassen, D C Malone
1Royal Danish School of Pharmacy, University of Colorado Health Sciences Center, Denver, CO.
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, > or = 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.