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Impact of incorporating pharmacy claims data into electronic medication reconciliation
Shobha Phansalkar1, Qoua L Her2, Alisha D Tucker2
1Shobha Phansalkar, B.S.Pharm., Ph.D., is Instructor in Medicine, Division of General Medicine, Brigham and Women's Hospital, Boston, MA, and Instructor in Medicine, Harvard Medical School, Boston. Qoua L. Her, Pharm.D., M.S., is Pharmacy Informatics and Outcomes Research Fellow, Massachusetts College of Pharmacy and Health Sciences University, Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital. Alisha D. Tucker, B.S., is Project Coordinator, Partners HealthCare System, Clinical Informatics Research and Development, Wellesley Gateway, Wellesley, MA. Esen Filiz, M.Sc., is Junior Business Analyst, Vita-Systems GmbH, Mannheim, Germany. Jeffrey Schnipper, M.D., M.P.H., is Associate Professor of Medicine, Harvard Medical School, and Associate Physician, Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital. George Getty, B.S., is Software Engineer II, Partners HealthCare, Clinical Informatics Research and Development, Wellesley Gateway. David W. Bates, M.D., M.Sc., is Senior Vice President for Quality and Safety and Chief Quality Officer, Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital. sphansalkar@partners.org.
Purpose:
The potential value of adding pharmacy claims data to the medication history in the electronic health record (EHR) to improve the accuracy of medication reconciliation was studied.
Methods:
Three medication history sources were used for this evaluation: a gold-standard preadmission medication list (PAML) created by reviewing all available medication history information, an EHR-generated PAML, and pharmacy claims data. The study population consisted of patients from the Partners Medication Reconciliation Study with medication history information available from all three medication history sources. The aggregated medication list from each medication history source was compared with the gold-standard PAML to identify and categorize missing medications, additional medications, and discrepancies in the various attributes of a medication order, including dose, route, and frequency. McNemar's test was used to compare paired proportions of medication entries across each source to the gold-standard PAMLs.
Results:
Fifteen patients had medication histories in all three medication history sources. Medication entries across all three sources included 169 from the gold- standard PAMLs, 158 from the EHR-PAMLs, and 351 from pharmacy claims data. The EHR-PAMLs and pharmacy claims data correctly reflected 52.1% and 43.2% of the gold-standard PAMLs, respectively. Combining the EHR-PAMLs and pharmacy claims resulted in 69.2% of the gold-standard PAMLs correctly reflected (p < 0.0001). Combining these two data sources increased the accuracy of medication history by 17.1%.
Conclusion:
Combining the EHR-PAML and pharmacy claims data resulted in a significant increase in the number of medications correctly reflected in the gold-standard PAML compared with the EHR-PAML or claims data separately.
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