Validation and evaluation of a prior‐to‐admission medication list discrepancy risk‐scoring tool

Abstract

AbstractIntroductionMedication reconciliation is vital in preventing medication errors during transitions of care. Implementation of effective medication reconciliation, however, remains a challenge for healthcare systems due to cost and resource constraints. The objective of this study was to evaluate a risk‐scoring tool for identifying patients at high risk for medication discrepancies and, therefore, prioritize pharmacy intervention with obtaining an admission medication history.MethodsSingle‐center validation study at an academic medical center including adult patients with a medication history note written by a pharmacy staff member. The primary outcome was the number of changes made to the prior‐to‐admission (PTA) medication list by pharmacy staff. Secondary outcomes included changes in risk score after medication history was completed, the number of changes based on disease state and medication class, and the number of clinically relevant changes for a randomized subgroup of patients.ResultsThe study included 12 122 patient encounters. The median PTA risk score was 29 (low risk), and 41% of patients were aged 40–65 years. The median number of discrepancies was higher for the high‐risk group (6, interquartile range [IQR] 3–11) compared to the low‐risk group (3, IQR 1–7), p < 0.001. For the primary outcome, Pearson's correlation coefficient was 0.37, indicating a weak correlation between the PTA risk score and the number of changes to the medication list. The linear regression also indicated a weak correlation (R2 = 0.14). Parkinsonism, diabetes mellitus, chronic obstructive pulmonary disease, and cystic fibrosis were associated with a higher median count of medication discrepancies.ConclusionsThis study demonstrated that this PTA medication risk‐scoring tool could help identify patients at high risk of medication discrepancies, but the score did not correlate well with the number of medication list changes. Therefore, subgroup analyses and further studies are needed to determine the optimal medication list risk‐scoring tools.