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Health care organizations face the challenge of reducing costs while improving health outcomes. Currently, more than 39 million seniors are enrolled in a Medicare Part D prescription benefit plan, many of whom also qualify for medication therapy management (MTM) services. MTM programs provide valuable services designed to prevent or resolve medication-related problems (MRPs). Two core components of all MTM programs include comprehensive medication reviews (CMRs) with followup interventions and focused non-CMR interventions. Currently, there is limited research comparing the rate of MRPs resolved by CMR and non-CMR interventions.To determine whether CMRs versus non-CMR interventions resulted in more pharmacist-initiated intervention recommendations and positive medication changes.This retrospective, cross-sectional quality improvement project evaluated outcomes for individuals who received interventions following a CMR compared with those who received non-CMR interventions as part of a comprehensive MTM program. For this project, eligible individuals qualified for an MTM program. A positive medication change was defined as the addition of an appropriate medication or the removal of an inappropriate medication within 120 days of a pharmacist recommendation. Differences between the groups were calculated using the Wilcoxon rank sum test.During the 2-year period (2012 and 2013), 788,756 beneficiaries were enrolled in the MTM program and evaluated for potential MRPs. In both years, pharmacists recommended more medication changes per member for those receiving a CMR (0.81 in 2012 and 0.82 in 2013) compared with the non-CMR group (0.68 in 2012 and 0.61 in 2013; P value0.001 for both years). However, recommendations made via non-CMR interventions (representing the vast majority of all medication reviews) were more likely to result in a positive medication change (OR = 1.24, 95% CI = 1.21-1.28 for 2012; OR = 1.26, 95% CI = 1.22-1.30 for 2013).These quality improvement project results suggest that Medicare Part D beneficiaries participating in a university-based MTM program who received non-CMR interventions had a higher likelihood of having positive medication changes than those receiving CMRs. These results are enlightening and may provide initial evidence to support inclusion of a non-CMR performance metric for the Centers for MedicareMedicaid Service's star ratings for MTM programs.No outside funding supported this study. The University of Arizona Medication Management Center (UAMMC)/SinfoniaRx provides grant funding to the University of Arizona for research. Augustine reports support from the UAMMC and ownership of Pfizer stock and was a graduate student at the University of Arizona College of Pharmacy at the time of this study. The authors report no other potential conflicts of interest. Buhl and Augustine contributed study concept and design, collected the data, and wrote the manuscript. Data interpretation was performed by all the authors. Taylor, Warholak, Augustine, and Martin revised the manuscript.

In recent years, comparative effectiveness tools and methods have evolved to assist health care decision makers in identifying optimal therapies. In-person training programs on comparative effectiveness research may be helpful in understanding and applying this information.To provide a follow-up assessment of the use of comparative effectiveness research (CER) in the pharmacy and therapeutics (PT) committee decision-making process, using information collected from participants 1 year after attending a live continuing education program, in which participants were taught about CER designs and how to access available CER resources through the Agency for Healthcare Research and Quality (AHRQ) Effective Health Care (EHC) Program.A retrospective, cross-sectional questionnaire was developed and mailed to 2 groups of individuals: CER workshop attendees and interested nonattendees (expressing an interest in attending a workshop but did not attend for unknown reasons). The questionnaire asked respondents to indicate personal and organizational use of CER in the decision-making process. Participants were asked to indicate whether their knowledge, ability, and use of CER studies increased since participating in the program. Data were analyzed using nonparametric tests to compare the responses of attendees and nonattendees, as well as overall reliability of the instrument.A total of 164 respondents completed the questionnaire (63 attendees and 101 nonattendees; overall response rate = 44%). The majority of respondents were pharmacists (n = 157, 95.7%) and were affiliated with a hospital (n = 106, 64.6%). Proportions of attendees and nonattendees differed significantly in the use of EHC research reviews/reports (45% and 28%, respectively; P = 0.02) and EHC executive summaries of research reviews/reports (48% and 29%, respectively; P = 0.01). At 1-year follow-up, the majority of attendees reported an increase ("somewhat" or "very much") in knowledge of CER (91.5%), ability to use CER (83.0%), and use of CER studies (58.7%).Health professionals attending a continuing education CER program reported higher use of EHC CER materials compared with nonattendees. Additionally, attendees reported increased use of CER in clinical decision making. A continuing education program such as this may provide an effective avenue for introducing CER methods and resources to the PT committee and clinical decision-making processes.This project was supported by grant number R18HS019220 from the Agency of Healthcare Research and Quality. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency of Healthcare Research and Quality. Augustine has stock in Pfizer. The authors declared no other potential conflicts of interest associated with this study. Study concept and design were primarily contributed by Malone, along with Warholak, Hines, Brown, Hurwitz, and Taylor. Warholak, Hines, Brown, Hurwitz, and Taylor collected the data, assisted by Malone, Brixner, Cobaugh, and Schlaifer. Data interpretation was performed by Malone and Augustine, with assistance from the other authors. The manuscript was written primarily by Augustine, with assistance from Malone, Sun, Warholak, Hines, Brown, Hurwitz, and Taylor. Malone revised the manuscript, assisted by Warholak, Hines, Brown, Hurwitz, Taylor, Brixner, Cobaugh, and Schlaifer.

National guidelines and initiatives have promoted the use of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) for patients with diabetes. The University of Arizona Medication Management Center (UA-MMC) is contracted by Medicare health plans, pharmacy benefit managers (PBMs), and multiple commercial health insurance plans to provide medication therapy management (MTM) services for plan members. As part of the MTM program, recommendations have been made for those patients who may benefit from the addition of an ACEI/ARB. Although the intervention benefits and guidelines for using ACEIs/ARBs are clear, real-world evidence is needed to understand and potentially increase uptake of guideline interventions among eligible patients.To (a) identify patient characteristics that predict acceptance of guideline recommendations to add ACEI/ARB medications to diabetic treatment via MTM services and (b) examine how well different case characteristics (i.e., patient age and sex, type and number of recommendation attempts, type of health care plan) predict the odds of adding ACEI/ARB medications to diabetic regimens when recommended through an MTM call center.This was a retrospective analysis of secondary data provided by the UA-MMC. The de-identified national data included adult plan members with diabetes who the UA-MMC recommended adding an ACEI/ARB prescription based on 2012 national guidelines. The UA-MMC made recommendations by either patient letters, patient phone calls, physician faxes, or any combination thereof. We conducted a binary logistic regression analysis to assess the impact of case characteristics on the likelihood of accepting recommendations to add ACEI/ARB medications. The outcome variable was recommendation acceptance (yes/no), defined as new prescription claims for an ACEI/ARB within 120 days following the recommendation. Five predictor variables were assessed: (1) patient's age quartile; (2) method of communicating recommendations (letter, phone call, fax, or some combination thereof); (3) whether recommendations were made once or twice on separate dates; (4) patient's sex; and (5) type of health care plan.Recommendations were made for 31,495 members of health plans or PBMs that contracted with the UA-MMC. Patients' ages ranged from 19-90 (Mean =72.01; SD =10.21), with females comprising 56% of the sample. The recommendation to add ACEI/ARB medications was accepted for 14.5% (4,559) of patients. In most cases (73%), recommendations occurred via a letter to patients together with a fax to their providers. The fitted model, containing 3 predictor variables (age quartile, type of contact to communicate the recommendations, and whether recommendation contacts were made twice), was statistically significant, χ(2) (10; N = 31,495) = 112.82 (P0.001), indicating that the model was able to distinguish between those who did and did not accept UA-MMC's recommendations to add ACEI/ARB medications. The likelihood of recommendation acceptance decreased as patient age increased compared with patients in the first age quartile (ages 19-67; P ≤ 0.005 at all levels). Compared with sending only a provider fax, patients who received all 3 types of contact (provider fax with patient phone call and letter) were estimated to be 1.34 times more likely (34% increase) to have recommendation acceptance ( P = 0.004; 95% CI = 1.10-1.63). Similarly, patients who received only letters were also 1.32 times more likely (32% increase) than provider faxes alone to result in recommendation acceptance ( P = 0.003; 95% CI = 1.10-1.59). Patients for whom recommendations were made twice were less likely to have recommendation acceptance than for those contacted once, controlling for all other predictor variables in the model ( P0.001; OR = 0.77; 95% CI = 0.69-0.86).Recommendations to add an ACEI/ARB to diabetic regimens are more likely to be accepted for younger patients and those who receive recommendations through all 3 communication types (provider fax combined with patient phone call and letter) or just letters than provider faxes alone. Further research is needed to understand why prescribers are not accepting MTM recommendations.