Discrepancies Identified with the Use of Prescription Claims and Diagnostic Billing Data Following a Comprehensive Medication Review

BACKGROUND: The University of Florida College of Pharmacy’s Medication Therapy Management Communication and Care Center (UF MTMCCC) provides medication therapy management (MTM) services to patients enrolled in a State of Florida Medicaid Waiver Program: Medicaid for the Aged and Disabled. To provide these services, UF MTMCCC was given access to patients’ prescription claims data and diagnostic billing data in the form of ICD-9 codes. Prior to calling a patient, a precomprehensive medication review (CMR) work-up was performed to identify potential medication-related problems (MRPs) and/or health-related problems (HRPs). Based on information provided by the patient in relation to comorbidities, medications, and medical history during the interactive telephone conversation, problems were either confirmed or eliminated. All of the reported information was also assessed to identify any new MRPs or HRPs. Accordingly, telephonic MTM services have the potential to bridge the gap between pharmacy claims data and patient self-reported information, since the MTM services provided rely on the accuracy of both informational resources. OBJECTIVES: To determine the degree of discrepancy in patient-reported information regarding chronic comorbidities and medications versus diagnostic billing data (ICD-9 codes for chronic comorbidities) and pharmacy claims data (medications) when providing MTM services during an interactive telephonic comprehensive medication review. METHODS: A retrospective chart review (n = 147 patients) was performed for patients who received a telephonic CMR. Pharmacy claims data and diagnostic billing data, in conjunction with the pre-CMR work-up data, were used to identify discrepancies in information obtained from the patient during the CMR. During the chart review, identified MRPs or HRPs were categorized as “confirmed” (patient reported the problem exists and/or it was deduced from the presence/absence of a medication that the problem does exist); “eliminated” (patient reported the problem does not exist and/or it was deduced from the presence/absence of a medication that the problem does not exist); or “new” (a problem that was not identified during precall identification of problems, but following the CMR interaction, it was determined that a problem now exists). The study evaluated the discrepancies before and after a CMR telephonic interaction in the following categories: medications, chronic comorbidities, level 1 drug-drug interactions, level 2 drug-drug interactions, gaps in therapy, therapeutic duplications, lack of therapy, preferred drug list alternatives, combination products, and tobacco use. Percent discrepancy was calculated as the sum of new and eliminated data elements divided by the total number of data elements for each MRP or HRP. RESULTS: The percent discrepancy observed was 42% for medications, 41% for chronic comorbidities, 77% for level 1 drug-drug interactions, 93% for level 2 drug-drug interactions, 35% for gaps in therapy, 87% for therapeutic duplications, 26% for lack of therapy, 36% for preferred drug list alternatives, 42% for combination products, and 54% discrepancy for report of tobacco use. Overall, 4,441 data elements were identified as confirmed, eliminated, or new across the 147 CMRs. Among those data elements, 56% of the data was confirmed; 23% was eliminated; and 21% was discovered as new. CONCLUSIONS: The study met its objective in determining the degree of discrepancies that existed when prescription claims data and ICD-9 billing data were used to identify MRPs and/or HRPs versus using patient-reported data. Data revealed that the presence of discrepancy is relatively large depending on the category, indicating a difficulty in accurately making recommendations with incomplete data or solely based on prescription claims and billing data. MTM services with patient interaction are vital in identifying information that allows for more appropriate decision making.