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Purpose: Polypharmacy and medication adherence are well known challenges facing older adults. Medication regimen complexity increases the demands of self-care in the home. Some medication regimens may be more complex than others, especially when dosage form, frequency of dosing, and additional usage directions are included in complexity along with the number of medications In older adults with depression, it is unknown what features of their medications most influence their medication regimen complexity., Methods: A sample cohort of 100 adults ≥65 years old with a diagnosis of depression was randomly selected from electronic medical records (EMR) in ambulatory clinics at the University of Colorado (CU) and University of San Diego (SD). Demographic, medical history, and medication-related information was extracted from the EMR. Complexity was determined using the Medication Regimen Complexity Index (MRCI). IRB approval was obtained., Findings: The cohort mean age was 74.3 years (SD) and 79.7 years (CU). The mean unweighted Charlson comorbidity index for 1.0 (SD) and 1.8 (CU). The mean number of medications was 7.1 and 8.0, with 1.1 and 1.2 depression meds, 5.4 and 4.3 non-depression prescription meds, and 0.6 and 2.4 OTC meds for the SD and CU cohorts, respectively. 66% of SD adults and 70% of CU adults took six or more meds. Individual MRCI scores were on average 17.62 (SD) and 19.36 (CU). Dosing frequency contributed to 57-58% of the MRCI score, with patients facing an average of 7-8 unique dosing frequencies in their regimen. In both cohorts, there was an average of 3 additional directions added to the regimens to clarify dosing., Implications: As expected, in our older adult cohorts with depression the majority of patients took multiple medications. Using a standardized instrument, we characterized the regimen complexity and found that it was increasingly complex due to numerous dosing forms, frequencies and additional directions for use. Patient-level medication regimen complexity should go beyond depression medication to encompass the patient's entire regimen for opportunities to reduce complexity and improve ease of self-care., (Copyright © 2014 Elsevier HS Journals, Inc. All rights reserved.)

Purpose: A collaborative pharmacist-primary care provider (PharmD-PCP) team approach to medication-therapy management (MTM), with pharmacists initiating and changing medications at separate office visits, holds promise for the cost-effective management of hypertension, but has not been evaluated in many systematic trials. The primary objective of this study was to examine blood pressure (BP) control in hypertensive patients managed by a newly formed PharmD-PCP MTM team versus usual care in a university-based primary care clinic., Methods: This randomized, pragmatic clinical trial was conducted in hypertensive patients randomly selected for PharmD-PCP MTM or usual care. In the PharmD-PCP MTM group, pharmacists managed drug-therapy initiation and monitoring, medication adjustments, biometric assessments, laboratory tests, and patient education. In the usual-care group, patients continued to see their PCPs. Participants were aged ≥ 18 years, were diagnosed with hypertension, had a most recent BP measurement of ≥ 140/≥ 90 mm Hg (≥ 130/≥ 80 mm Hg if codiagnosed with diabetes mellitus), were on at least 1 antihypertensive medication, and were English speaking. The primary outcome was the difference in the mean change from baseline in systolic BP at 6 months. Secondary outcomes included the percentage achieving therapeutic BP goal and the mean changes from baseline in diastolic BP and low- and high-density lipoprotein cholesterol., Findings: A total of 166 patients were enrolled (69 men; mean age, 67.7 years; PharmD-PCP MTM group, n = 75; usual-care group, n = 91). Mean reduction in SBP was significantly greater in the PharmD-PCP MTM group at 6 months (-7.1 [19.4] vs +1.6 [21.0] mm Hg; P = 0.008), but the difference was no longer statistically significant at 9 months (-5.2 [16.9] vs -1.7 [17.7] mm Hg; P = 0.22), based on an intent-to-treat analysis. In the intervention group, greater percentages of patients who continued to see the MTM pharmacist versus those who returned to their PCP were at goal at 6 months (81% vs 44%) and at 9 months (70% vs 52%). No significant between-group differences in changes in cholesterol were detected at 6 and 9 months; however, the mean baseline values were near recommended levels. The PharmD-PCP MTM group had significantly fewer PCP visits compared with the usual-care group (1.8 [1.5] vs 4.2 [1.0]; P < 0.001)., Implications: A PharmD-PCP collaborative MTM service was more effective in lowering BP than was usual care at 6 months in all patients and at 9 months in patients who continued to see the pharmacist. Incorporating pharmacists into the primary care team may be a successful strategy for managing medication therapy, improving patient outcomes and possibly extending the capacity of primary care. ClinicalTrials.gov identifier: NCT01973556., (Published by Elsevier Inc.)

Background: Expected treatment effectiveness from medications can be diminished due to suboptimal adherence. Medication nonadherence has been linked to pill burden from the quantity of medications; however, medication regimens with similar quantities of medications vary in complexity due to multiple dosage forms, frequency of dosing, and additional usage directions. Thus, a simple medication count ignores medication regimen complexity, especially as it pertains to a patient-level perspective that includes prescription and over-the-counter medications. A gap exists in the study of a patient-level medication regimen complexity metric across disease-specific populations., Objective: The goal of this study was to implement the quantitative Medication Regimen Complexity Index (MRCI) at the patient level in defined populations with chronic disease (geriatric depression, HIV, diabetes mellitus, and hypertension). Patient-level medication regimen complexity included all prescribed medications and over-the-counter medications documented in the electronic medication list., Methods: Using electronic medical records at the University of Colorado Hospital ambulatory clinics, we sampled 4 retrospective cohorts of adult patients in active care in 2011 with a qualifying medical diagnosis and prescribed disease-specific medication. Samples were randomly selected from all qualifying patients; de-identified information was coded using the MRCI., Results: Cohort-defining disease-specific prescription medications (eg, antidepressants for the depression-defined cohort) contributed <20% to the total patient-level complexity MRCI score; the MRCI score was dominated by complexity associated with all other prescription medications. Within disease-specific cohorts, MRCI scores differentiated patients with the highest and lowest medication counts, comorbidity counts, and the Charlson comorbidity index scores. For example, geriatric depression patients had a highest quartile mean MRCI score of 41 and a lowest quartile mean MRCI score of 13. Between disease-specific cohorts, high and low MRCI scores differed because each cohort had its own MRCI ranges. For example, highest quartile MRCI scores varied from a mean MRCI score of 41 (geriatric depression) to 30 (hypertension); lowest quartile scores ranged from a mean MRCI score of 7 (hypertension and HIV) to 13 (geriatric depression)., Conclusions: MRCI components of dosing frequency and prescribed medications outside of the cohort-defining disease medications contributed the most to the patient-level scores. Thus, chronic disease management programs may want to consider all medications that patients are taking and examine ways to reduce complexity, such as reducing multiple dosing frequencies when possible. MRCI scores differentiated high and low patient-level complexity measures, representing possible utility as a prospective tool to identify target patients for intervention. Future work includes simplifying the MRCI and enhancing the scores with medication risk factors, as well as explicitly linking to adherence and health services., (Copyright © 2013 Elsevier HS Journals, Inc. All rights reserved.)