Background: Medication-prescribing discrepancies are used as a quality measure for patients transferred between sites of care. The objective of this study was to quantify the rate of adverse drug events (ADEs) caused by prescribing discrepancies and the discrimination of an index of high-risk transition drug prescribing.
Methods: We examined medical records of patients transferred between seven nursing homes and three hospitals between 1999 and 2005 in New York and Connecticut for transfer-associated prescribing discrepancies. ADEs caused by discrepancies were determined by two clinician raters. We calculated the fraction of medication discrepancies that caused ADEs in each of 22 drug classes by calculating positive predictive values (PPVs). We calculated the discrimination of a count of high-risk drug discrepancies, selected from published lists of high-risk medications and using observed PPVs.
Results: 208 patients were hospitalised 304 times. Overall, 65 of 1350 prescribing discrepancies caused ADEs, for a PPV of 0.048 (95% CI 0.037 to 0.061). PPVs by drug class ranged from 0 to 0.28. Drug classes with the highest PPVs were opioid analgesics, metronidazole, and non-opioid analgesics. Patients with 0, 1–2 and ⩾3 high-risk discrepancies had a 13%, 23% and 47% chance of experiencing a discrepancy-related ADE, respectively.
Conclusions: Discrepancies in certain drug classes more often caused ADEs than other types of discrepancies in hospitalised nursing-home patients. Information about ADEs caused by medication discrepancies can be used to enhance measurement of care quality, identify high-risk patients and inform the development of decision-support tools at the time of patient transfer.
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Funding: Financial support was provided by the VA Health Services Research and Development Service and the New York State Department of Health. SL is the recipient of a Doris Duke Clinical Research Fellowship.
Competing interests: None.
Ethics approval: Ethics approval was obtained from the Institutional Review Boards of Mount Sinai School of Medicine, James J. Peters VA Medical Center and Jewish Home Lifecare (all in New York City).
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