OBJECTIVES: To describe the types and frequencies of medication discrepancies identified through medication reconciliation in a community pharmacy setting, to identify potential correlations between a patient's electronic medical record (EMR) and pharmacy medication list, and to determine the relationship between patients who use prescribers and/or pharmacies outside of the Family Medicine Center (FMC) and the occurrence of medication discrepancies. METHODS: Cross-sectional comparison of patients' EMR medication lists and pharmacy medication fill history for a sample of patients presenting to the Family Medicine Pharmacy (FMP), which is located in the FMC on the University of Oklahoma Health Sciences Center campus in Oklahoma City. Discrepancies identified were classified according to one of six categories that included therapeutic duplication, medication exclusion, medications that should be designated inactive in the EMR medication list, and differences in medication strength, dosage form, or dosing regimen. RESULTS: A total of 100 patients were included. Most patients reported having all of their medications dispensed from FMP (89%), and most patients had prescriptions prescribed by FMC physicians only (57%). Each patient had an average of six medication discrepancies. Most discrepancies belonged to the inactive medication category (41%). The correlation between patients' FMP medication lists and their EMR medication lists was 0.73. Patients with one or more non-FMC prescribers had a greater number of medication discrepancies than patients with FMC prescribers only, but this relationship was not identified for those who used pharmacies outside of FMP (P = 0.0264 and 0.2580, respectively). CONCLUSION: A variety of medication discrepancies were observed, signaling a need for medication reconciliation in the outpatient setting. Future research on this topic should focus on the implications of such discrepancies in the outpatient setting, interventions to reduce the number of discrepancies, and identifying patients at high risk for such discrepancies.
OBJECTIVES: To describe the types and frequencies of medication discrepancies identified through medication reconciliation in a community pharmacy setting, to identify potential correlations between a patient's electronic medical record (EMR) and pharmacy medication list, and to determine the relationship between patients who use prescribers and/or pharmacies outside of the Family Medicine Center (FMC) and the occurrence of medication discrepancies. METHODS: Cross-sectional comparison of patients' EMR medication lists and pharmacy medication fill history for a sample of patients presenting to the Family Medicine Pharmacy (FMP), which is located in the FMC on the University of Oklahoma Health Sciences Center campus in Oklahoma City. Discrepancies identified were classified according to one of six categories that included therapeutic duplication, medication exclusion, medications that should be designated inactive in the EMR medication list, and differences in medication strength, dosage form, or dosing regimen. RESULTS: A total of 100 patients were included. Most patients reported having all of their medications dispensed from FMP (89%), and most patients had prescriptions prescribed by FMC physicians only (57%). Each patient had an average of six medication discrepancies. Most discrepancies belonged to the inactive medication category (41%). The correlation between patients' FMP medication lists and their EMR medication lists was 0.73. Patients with one or more non-FMC prescribers had a greater number of medication discrepancies than patients with FMC prescribers only, but this relationship was not identified for those who used pharmacies outside of FMP (P = 0.0264 and 0.2580, respectively). CONCLUSION: A variety of medication discrepancies were observed, signaling a need for medication reconciliation in the outpatient setting. Future research on this topic should focus on the implications of such discrepancies in the outpatient setting, interventions to reduce the number of discrepancies, and identifying patients at high risk for such discrepancies.
Authors: Eliz Markowitz; Elmer V Bernstam; Jorge Herskovic; Jiajie Zhang; Ben Shneiderman; Catherine Plaisant; Todd R Johnson Journal: AMIA Annu Symp Proc Date: 2011-10-22
Authors: Taylor L Watterson; Jamie A Stone; Roger Brown; Ka Z Xiong; Anthony Schiefelbein; Edmond Ramly; Peter Kleinschmidt; Michael Semanik; Lauren Craddock; Samantha Pitts; Taylor Woodroof; Michelle A Chui Journal: J Am Med Inform Assoc Date: 2021-07-14 Impact factor: 4.497