Objective: To evaluate the impact of pharmacist-delivered medication safety reviews (MSRs) on total medical expenditures, hospitalizations, emergency department (ED) visits, and mortality in Medicare Part D beneficiaries, whose plan was a participant of the Enhanced Medication Therapy Management model.
Study Design: Retrospective, pre-post, cohort study.
Methods: We evaluated the aforementioned outcomes for beneficiaries who were targeted, according to their MedWise Risk Scores (MRS), for MSR services in both 2018 and 2019. The “MSR” cohort included those who received their first-ever MSR in 2018 and received another MSR in 2019. The “failed to engage” (FTE) cohort included beneficiaries who were targeted in both 2018 and 2019 but did not engage in an MSR at any point through the end of 2019. For both cohorts, we calculated the change from 2018 to 2019 for each outcome and then determined whether unadjusted year-over-year changes differed significantly between cohorts. Additionally, these difference-in-differences (DiD) analyses were adjusted for baseline MRS and multimorbidity.
Results: A total of 11,436 beneficiaries were targeted for MSRs in both 2018 and 2019. Beneficiaries were, on average, aged 76.6 ± 10.0 years. The MSR cohort (N = 4384) outperformed the FTE cohort (N = 7052) in total medical costs (DiD = $958/y [7.5% savings]; P = .042), hospitalizations (DiD = 3.9 admissions/100 beneficiaries/y [10% reduction]; P = .032), ED visits (DiD = 6.2 visits/100 beneficiaries/y [10% reduction]; P = .014), and mortality (2.1% fewer died in 2019; P < .001). Each outcome remained significant after adjusting for baseline MRS and multimorbidity.
Conclusion: MSRs were effective at improving annual health care costs, hospitalizations, ED visits, and mortality in Medicare beneficiaries targeted according to MRS.
Am J Manag Care. 2021;27(suppl 16):S300-S308. https://doi.org/10.37765/ajmc.2021.88755
For author information and disclosures, see end of text.
In 2003, the Medicare Prescription Drug, Improvement, and Modernization Act required that Part D prescription drug plans (PDPs) offer beneficiaries a nondispensing clinical pharmacy service, called medication therapy management (MTM), as a way to optimize medication use and ultimately improve outcomes.1 PDPs have substantially differed in their ability to achieve those goals.2,3 In 2017, the Center for Medicare & Medicaid Innovation (CMMI) initiated a 5-year Enhanced MTM model test. CMMI is testing whether providing PDPs with regulatory latitude and financial incentives can promote enhancements in the MTM program, catalyzing improved therapeutic outcomes and reduced net Medicare expenditures. To meet these objectives, participating PDPs are permitted to try innovative strategies that can optimize beneficiary medication use.4-6
PDPs that identify and implement innovative strategies that prioritize adverse drug event (ADE) prevention could be successful within this model. ADEs are prevalent among older Americans and contribute to several negative outcomes, such as emergency department (ED) visits, hospitalization, death, and higher health care costs.7-13 Improving these outcomes is possible because consistent research shows that about half of all ADEs are preventable.14 However, achieving these ends requires a strategic approach. First, individuals at high risk of ADEs must be accurately identified. Next, their specific pharmacological risk factors must be made known to clinicians, who can design interventions that mitigate relevant safety concerns.
In the 2 previous articles in this supplement,15,16 we demonstrated how a computer-based risk algorithm with an integrated clinical decision-support system (CDSS) has facilitated this process in Medicare Part D beneficiaries. To recap, the MedWise Risk Score (MRS) identified beneficiaries at risk of medication-related morbidity; the inputs to the risk-scoring algorithm came exclusively from pharmacological characteristics found within beneficiaries’ Part D claims. Specifically, each 1-point increase in the MRS was associated with a greater chance of ADEs, higher medical costs, more hospitalizations, more ED visits, and greater mortality. Next, high-MRS beneficiaries were then targeted for risk-mitigating interventions as part of their PDP’s participation within the CMMI model. When supported by MedWise—a CDSS that helps identify medication-related problems contributing to a beneficiary’s MRS—pharmacists overwhelmingly prioritized problems pertaining to medication safety and proposed recommendations that would significantly decrease the MRS. Specifically, adverse drug reactions, drug interactions, and unnecessary medication use accounted for approximately 85% of all medication-related problems identified. To resolve these problems, pharmacists proposed recommendations—particularly deprescribing and changing existing therapies to safer alternatives—capable of reducing the mean MRS by nearly 5 points.
In sum, the first article in this supplement15 demonstrated the association between MRS and poor health outcomes, and the second16 demonstrated that MedWise-CDSS–supported pharmacists prioritized interventions capable of reducing the risk of poor outcomes in individuals targeted according to their MRS. But do these interventions actually improve patient health and save money for the health care system? To conclude this supplement, in this article, we will evaluate the impact of MedWise-CDSS–supported, pharmacist-driven interventions on Medicare Parts A and B costs, hospital admissions, ED visits, and all-cause mortality in beneficiaries of a PDP that is participating in CMMI’s Enhanced MTM model. The research presented here was conducted by Tabula Rasa HealthCare (TRHC), with the oversight of ClearStone Solutions, the contracted Part D administrator for plan participant Blue Cross and Blue Shield Northern Plains Alliance (BCBS-NPA), without input from CMS.
Study Design and Data Sources
This was a retrospective, pre-post, cohort study that evaluated the outcomes of PDP beneficiaries in 2 cohorts: one that received MSRs and a comparable group that did not. This study used Medicare Part A and Part B claims as the primary data source for outcomes. The Biomedical Research Alliance of New York Institutional Review Board (protocol number 19-12-172-427; approved May 24, 2019) provided ethical oversight and waived requirements for obtaining informed consent.
Context and Setting
This study involved beneficiaries living in Medicare Part D Region 25 (Iowa, Minnesota, Montana, Nebraska, North Dakota, South Dakota, and Wyoming) covered under the BCBS-NPA. NPA members qualify for Medicare, but they may have types of insurance outside NPA. NPA’s Medicare Part D administrator, ClearStone Solutions, hired TRHC to deliver Enhanced MTM services. TRHC is a medication risk–mitigation company that developed the aforementioned computer-based risk-predictive model (ie, the MRS) and the advanced CDSS (ie, MedWise). Several previously published studies describe the validity of the MRS17-21 and the application of the advanced CDSS in cases.22-29
A pharmacist-provided medication safety review (MSR) was the intervention under investigation. Compared with comprehensive medication reviews in traditional MTM,30 MSRs have 2 distinct features described in the previous literature.31 First, MSRs do not focus on improving CMS star ratings or on ensuring medication and guideline adherence. Rather, MSRs apply the principles of pharmacodynamics, pharmacokinetics, pharmacogenomics, and chronopharmacology to enhance medication safety and prevent ADEs. Second, MSRs address simultaneous multidrug interactions in the context of the entire drug regimen using the aforementioned CDSS. This approach updates other methodologies of assessing drug interactions as pairwise comparisons and subsequently generating numerous alerts.
Our previous paper16 described the process of MSR delivery. As a brief summary, beneficiaries were stratified according to their MRS, which was computed from Part D claims. Next, beneficiaries with the highest risk scores were prioritized for MSR outreach by pharmacy staff (technicians, interns, or pharmacists). Staff then conducted a medication reconciliation with each beneficiary who agreed to an MSR, asking them to verify the medications they were taking. The result was an accurate, up-to-date medication list that pharmacists could use to guide their recommendations. Beneficiaries then met with pharmacists either face to face or by telephone for the official MSR consultation. Once the consultation was complete, pharmacists produced several documents to close out the encounter. First, each beneficiary received an action plan. Written in patient-friendly language, this document described the medication-related problems(s) identified during the MSR with the corresponding resolving recommendation(s). If appropriate, the pharmacist sent a similar document to the beneficiary’s prescriber, except that this document used professional and clinical terminology. Prescribers would not receive such correspondence if patients did not provide consent to the pharmacist, or if the medication-related problems were either of minor severity or resolvable by the patient (eg, time-of-day administration changes). Last, beneficiaries received an updated medication list, which was based on their MSR encounter. Pharmacy technicians and pharmacists contacted beneficiaries for follow-up MSRs approximately 3 months later, but no earlier.
Participants and Outcomes
To be considered for this study, beneficiaries must have been (a) aged at least 21 years, and (b) continuously enrolled in and eligible for Enhanced MTM for the entirety of 2018 and 2019 (n = 170,820). Because of their propensity to consume a disproportionate amount of financial resources,32 patients with end-stage renal disease (ESRD) were excluded from all analyses. A patient with ESRD was defined as anyone with a Beneficiary Medicare Status Code (ie, the BENE_MDCR_STUS_CD variable) of 11, 21, or 31 (from the Claim and Claim Line Feed files). From the continuously eligible beneficiaries without ESRD, we included anyone targeted (because of MRS) for an MSR in both years (n = 13,941). Our intervention cohort (ie, MSR) included anyone who had their first-ever MSR in 2018 and also had at least 1 MSR in 2019 (n = 4384). The MSR in 2018 had to be the beneficiary’s first-ever MSR, because we believe the opportunity to make an impact is greatest with patients who have never had an MSR. This MSR cohort was compared against targeted beneficiaries who failed to receive any MSR services within the study window or at any point before (“failed to engage” [FTE] cohort, n = 7052). Such individuals failed to engage with the pharmacy team (eg, did not answer the phone or refused the MSR). eAppendix Figure 1 (eAppendix available here) summarizes data management for cohort selection.
We considered several outcomes for analysis: Medicare Parts A and B costs, number of hospitalizations, and number of ED visits not resulting in a hospitalization. We examined changes from 2018 to 2019 in the various outcomes for each cohort.
In addition to examining changes from 2018 to 2019 in outcomes among beneficiaries who were eligible across both years, we assessed 2019 all-cause mortality among beneficiaries in the 2 cohorts who were alive at the end of 2018. To qualify for this analysis, beneficiaries had to be continuously eligible and targeted only in 2018. We then identified those who had their first-ever MSR in 2018 and those who failed to engage in 2018. Finally, we calculated the fraction of beneficiaries in each cohort who died in 2019.
Parts A and B costs were the total annual adjudicated amount paid by CMS (US$) in the claims data. Hospitalizations and ED visits not resulting in a hospitalization were identified by line-item claims. Death date was obtained from the beneficiary demographics provided as part of the claims data.
First, we evaluated descriptive statistics to ensure the 2 cohorts were comparable at baseline and to adjust for any differences that did emerge. In addition to looking at basic demographics (eg, age, sex, ethnicity), we examined baseline scores on 2 indicators of acuity: (1) the hierarchical condition category (HCC) score and (2) the MRS of the beneficiary just before their data were input into MedWise CDSS and queued for MSR outreach (typically, beneficiary data are imported into MedWise CDSS very shortly after targeting).
Our analysis methods differed for mortality vs the other outcomes. For the other outcomes, we first calculated the change in each outcome for both cohorts from 2018 to 2019. Next, we compared the 2 cohorts’ changes in outcomes (ie, difference-in-differences [DiD]) from 2018 to 2019 using 2-sample t-tests and Wilcoxon tests. To account for any potential confounding due to multimorbidity or MRS, t-tests were weighted using each beneficiary’s baseline CMS HCC score33 and MRS (at the time of targeting). In other words, we weighted the FTE cohort to make its HCC and MRS distributions closely resemble those of the MSR cohort. For the DiD analysis, our alternative hypothesis assumed that those receiving MSRs would outperform the FTE cohort (1-sided test). This was based on MTM studies that have demonstrated that pharmacist-driven interventions can reduce total costs,34-36 ED utilization,37 and hospitalization rates35-37 compared with patients not receiving MTM services. We considered P values < .05 statistically significant. All analyses were performed in R (v. 3.5.2). eAppendix Figure 2 summarizes the method for analysis.
The mortality analysis used the same procedures, except that it did not examine changes in outcomes from 2018 to 2019. Instead, it defined 2 cohorts of targeted beneficiaries in 2018: those who had their first-ever MSR in 2018 (MSR) and those who failed to engage in 2018 (FTE). Among those alive at the end of 2018, we then calculated the fraction of beneficiaries in each cohort who died in 2019 and compared the 2 death rates using a 2-sample t-test. As with the other outcomes, we ran the same analysis adjusting for baseline HCC and MRS using weighted t-tests.
The 2 cohorts consisted of 11,436 beneficiaries, of which 4384 and 7052 beneficiaries, respectively, made up the MSR and FTE cohorts. Table 1 reports each cohort’s individual and combined demographics (as received by claims data), along with those of all beneficiaries (n = 170,820). To summarize, the 2 cohorts were well balanced at baseline. Both groups were predominantly female (MSR: 63.4% vs FTE: 64.3%), were overwhelmingly non-Hispanic white (MSR: 99.1% vs FTE: 98.7; P < .009), were aged an average of 76.6 ± 10.0 years (MSR: 77.1 ± 9.0 vs FTE: 76.3 ± 10.6; P < .001), and were taking an average of 10.9 ± 3.7 medications (MSR: 10.6 ± 3.6 vs FTE: 11.0 ± 3.7; P < .001). Regarding medication and multimorbidity, both groups had similar HCC scores (MSR: 1.4 ± 1.1 vs FTE: 1.4 ± 1.0; P = .60); however, the FTE group had a markedly higher mean MRS just prior to being imported into MedWise CDSS (MSR: 17.8 ± 6.1 vs FTE: 19.0 ± 6.1; P < .001). eAppendix Figure 3 shows distributions of HCC and MRS in the 2 cohorts.
Table 2 reports economic and clinical utilization outcomes for both cohorts. First, mean total medical costs (ie,combined Parts A and B) increased from 2018 to 2019 in both cohorts, although the increase was less pronounced for the MSR cohort. Specifically, MSR members’ costs increased by $1158 (95% CI, $350-$1965) per member, whereas FTE members’ costs increased by $2116 (95% CI, $1395-$2838) per member. This $958 difference between groups was significant (P = .042 using t-test) and represented a 7.5% annual overall cost savings in favor of the MSR cohort. This trend was the same when we repeated the analysis for individual Part A and B expenditures. Next, we found that the hospitalization rate decreased by 1.3 admissions/100 beneficiaries/y (95% CI, −4.4 to 1.9) in the MSR cohort. In contrast, the FTE cohort experienced a year-over-year increase of 2.6 admissions/100 beneficiaries/y (95% CI, 0.0-5.1). Overall, the difference between groups was significant (P = .032 using t-test) and represented a 10% decrease in annual hospitalizations favoring the MSR cohort. Finally, ED utilization increased in both the MSR cohort with 1.5 visits/100 beneficiaries/y (95% CI, −2.7 to 5.7) and the FTE cohort with 7.7 visits/100 beneficiaries/y (95% CI, 4.1-11.3), but the increase was less steep in the MSR cohort. The intergroup comparison was significant (P = .014), representing an overall 10.3% decrease in annual ED visits in favor of the MSR group. As revealed in Tables 3 and 4, the significant intergroup differences in total cost, hospitalizations, and ED visits were maintained even after adjusting for HCC (Table 3) and MRS (Table 4).
Table 5 reports mortality data. Of those who had their first-ever MSR in 2018 (n = 10,442), 6.8% (95% CI, 6.3%-7.2%) died by the end of 2019. Comparatively, 8.9% (95% CI, 8.4%-9.3%) of those who were FTE in 2018 (n = 15,895) died in 2019. This 2.1 percentage point difference (8.9% minus 6.8%) in mortality rates was significant (P < .001). Significance was maintained, even after adjusting for HCC and MRS.
This study of more than 11,000 Medicare Part D beneficiaries found that those receiving repeated MedWise CDSS-supported, pharmacist-provided, safety-oriented interventions had improvements in several pertinent outcomes year-over-year. Relative to beneficiaries who were targeted for but did not receive services, the MSR cohort experienced an average savings of $958 per member (7.5% savings) in total Part A and B medical costs; an average reduction in hospitalizations of approximately 4 admissions/100 beneficiaries (10% reduction); an average reduction in ED visits of approximately 6 visits/100 beneficiaries (10% reduction); and a mortality rate that was lower by 2 percentage points. Importantly, all of the aforementioned outcomes remained significant, at a similar magnitude of effect, even after adjusting for the difference in baseline MRS between the FTE and MSR cohorts. As noted by Acumen’s most recent evaluation of Enhanced MTM, these effects may not have been robust enough to extend to NPA’s entire population of 240,000 beneficiaries.38 Nevertheless, MSRs impact a very specific subset: those deemed to be at high risk of ADEs as defined by MRS stratification (ie, those with elevated MRS within the population). Therefore, the results of this study indicate that MSRs are effective at improving annual health care costs, hospitalizations, ED visits, and mortality in Medicare Part D beneficiaries targeted according to MRS.
This finding is relevant for payers seeking to optimize MTM delivery amid a climate of rising health care costs.39 Relevance becomes apparent when placed in the context of traditional MTM and its broader literature. For several years now, Medicare programs (as well as private payers) have deployed MTM to optimize medication regimens and thus improve clinical and economic outcomes. Plans have used a fairly standard approach to target those who have the greatest need for pharmaceutical care. Specifically, beneficiaries have been targeted by disease burden, drug costs, and/or polypharmacy.4 Under this paradigm, some traditional MTM programs have demonstrated success, even when prioritizing medication-related problems unrelated to medication safety. For example, a 2019 study found that targeted medication reviews predominantly aiming to correct medication nonadherence were effective at reducing hospital and ED utilization compared with a matched control.37 In a collaborative model, Brophy et al described an adherence-focused MTM program designed strictly for patients with diabetes that was able to reduce total medical costs and hospitalizations in a treatment group compared with a control group.36 Other controlled studies have described traditional MTM programs that decreased total health care costs34 and reduced hospital admissions40 and mortality against a control.40,41
With this context, our results imply that the scope and effectiveness of MTM can expand. There are 2 specific implications. First, we have demonstrated a novel intervention whose targeting approach deviates from the normative paradigm. The MRS can help identify individuals for whom a safety-based approach with repeated MSRs is indicated. Certainly, some of these individuals likely would be missed using traditional targeting methods, leaving risks unmitigated and unknown. Now, payers can identify such individuals and deploy a specific intervention to help optimize their regimens. Nevertheless, future studies need to specifically examine (1) the extent of overlap between traditional targeting and MRS-driven targeting, as well as (2) the clinical and demographic differences between members who do not overlap. Second, integrating MRS and MedWise CDSS into traditional MTM could help avoid siloed interventions that would unknowingly and inadvertently increase the MRS despite following evidence-based clinical practice guidelines. For example, programs aiming to improve statin adherence could do so in the context of the MRS. Specifically, several statins are implicated in serious cytochrome P450–mediated drug interactions,42 which would be captured by the MRS and the MedWise CDSS. If interactions are identified, differently metabolized statins could be recommended to reduce the MRS. This ensures that the benefits obtained by improving adherence (ie, cardiovascular risk reduction) will not be undermined by a potentially serious ADE (ie, from a drug interaction). Again, future research should explore the effectiveness of the MRS and MedWise CDSS when deployed in traditional MTM settings in a complementary fashion.
A primary limitation of this study is self-selection bias. Although we attempted to adjust for any differences related to ADE risk (ie,MRS) and multimorbidity (ie, HCC), some unmeasurable or unknown variable could have confounded results. For example, members in the FTE cohort may have been less interested in their overall health compared with those who engaged in MSRs. Second, we did not account for timing of MSRs during the year or the total number of MSRs received. For example, the first MSR being performed early in 2018 compared with later in 2018 could have led us to underestimate the impact of the MSR approach, because outcomes throughout 2018 until the MSR encounter were considered in the overall outcome measures for these patients. Moreover, those receiving the minimum number of MSRs (ie, one in 2018 and one in 2019) may have performed differently from those who received multiple MSRs in both years. Therefore, future analyses are needed to determine the precise effect of repeated engagements. Third, we did not attempt to differentiate between MSR delivery methods. Our results were an aggregate of MSRs performed telephonically by call-center pharmacists and those performed face to face by community pharmacists. Fourth, our sample was almost entirely made up of non-Hispanic whites living in 7 Midwestern states. Therefore, future research should include a greater diversity of ethnicities and geographies for this proof of concept to be fully realized. Finally, we did not have cause of death available within our dataset. It is possible that causes differed between the 2 groups in such a way as to bias our findings. For example, it could be possible that there were more patients with terminal illnesses in the FTE cohort.
Risk-stratified Medicare beneficiaries who received repeated pharmacist-provided, MedWise CDSS-supported MSRs demonstrated significant improvements in year-over-year medical costs, hospital admissions, ED visits, and mortality compared with MSR-eligible beneficiaries who did not receive services. All outcomes remained significant even after adjusting analyses for the baseline differences in MRS between the MSR and FTE cohorts. Because MSRs represent an entirely novel and effective service model that deviates from traditional MTM paradigms, their incorporation should expand the scope and effectiveness of traditional MTM.
The authors thank Dr Calvin Knowlton and Dr Orsula Knowlton for their forward-looking vision and unequivocal, lifelong dedication to the advancement of the pharmacy profession; Brian Litten for legal counsel and support of the program; Steve Gilbert for supporting operational and clinical quality; Beth Eichfeld for her involvement as program manager; Stephanie Carletti for her unwavering support of the program, facilitating operational efficiency and success; Jeffrey Difrancesco for his support of data analyses; the following pharmacists for their sincere efforts to improve the safety, appropriateness, and quality of the Enhanced MTM beneficiaries’ medication regimens: Amanda Mastrogiovanni, Courtney Christopher, Craig Rein, Daniel Green, Danielle Cantalupo, Darria Zangari, Debbie Lukose, Desiree Massari, Dina Amer, Drusty Bavishi, Gabriella Latorre, JT Skelly, Jennifer Metzler, Jessica Growette, Joseph Wood, Kaitlin Savona, Kirsten Demarco, Kirsten Gallo, Lauren Petroski, Loren Martinez, Megan Lew, Melissa Metzger, Natalie Ferro, Neil Patel, Palak Gohil, Richard Chang, Scott Raymond, and Valerie Buchanan; the following technicians for their strong efforts in engaging beneficiaries and obtaining accurate medication lists: Abigail Malone, Alayna Marucci, Amanda Seals, Amber Mosteller, Andrew Palmer, Ashley Approvato, Briana Joseph, Brittany Connelly, Christina Bean, Donna Van Staveren, Emlyn Williams, Eva Duarte, Gabrielle Parker, Gregg Eppleman, Heather Green, Heather Klarmann, Helen Martin, Jaime Picchi, Jaimie Doyle, Jamie Kilmurray, Jeanna Comorote, Jonquil Steele, Josselyne Duarte, Kimberly Shaw, Krista Maniaci, Layla Pineda, Lindsey Rehberger, Maureen Molzon, Melissa Jorge, Naomi Colon, Rachael Carpenter, Raquel Rivera, Samantha O’Connell, Stacie Riker, Starr Santiago, Tara Russo, and Victor Vo; the following outreach liaisons for their strong efforts in engaging beneficiaries: Ed Demetreshon, Joan Greenhalgh, Linda Moore, and Samantha Sklikas; and the following individuals from ClearStone: Jessica Growette, Lucas Castillo, Jenny Steinke, Bonnie Haukom, Sarah Legatt, Nick Jejel, Paul Fischer, Angela Mitchell, and Nancy Sheehan. We also thank Dana Filippoli for the review of the manuscript.
Author affiliations: Office of Applied Pharmacotherapy, Tabula Rasa HealthCare, Moorestown, NJ (MSA); Office of Translational Research and Residency Programs, Tabula Rasa HealthCare, Moorestown, NJ (DB); Office of Healthcare Analytics, Tabula Rasa HealthCare, Moorestown, NJ (SF, HJ); ClearStone Solutions, Government Markets Division, Blue Cross and Blue Shield of Minnesota, Eagan, MN (JJ); Office of Healthcare Analytics, Tabula Rasa HealthCare, Moorestown, NJ (AS); Tabula Rasa HealthCare, Orlando, FL, and Université de Montréal, Montréal, Québec, Canada (JT).
Funding source: Funding was obtained by Blue Cross and Blue Shield Northern Plains Alliance (NPA) in Medicare Part D Region 25 (IA, MN, MT, ND, NE, SD, and WY), the model participant, under the 5-year CMS Innovation Center’s Enhanced Medication Therapy Management (MTM) model. Through its NPA’s Part D plan administrator, ClearStone Solutions, funds were provided to Tabula Rasa HealthCare for delivery of Enhanced MTM services and research. In this study, data were analyzed regardless of patient’s enrollment in the Enhanced MTM program. This supplement was supported by Tabula Rasa HealthCare.
Author disclosures: Drs Stein, Finnel, Bankes, Awadalla, and Turgeon and Mr Jin report employment with Tabula Rasa HealthCare, ownership of Tabula Rasa HealthCare stock, and ownership of the MedWise Risk Score used in this study. Dr Johnson reports employment with ClearStone Solutions (an affiliate of Blue Cross and Blue Shield of Minnesota), which administers the Enhanced MTM Pilot on behalf of Blue Cross and Blue Shield Northern Plains Alliance. ClearStone Solutions paid Tabula Rasa HealthCare to provide the Enhanced MTM services described. Dr Turgeon reports patents received (10,890,577: Methods of treatment having reduced drug-related toxicity and methods of identifying the likelihood of patient harm arising from prescribed medications) and patents pending (Methods of treatment having reduced drug-related toxicity and methods of identifying the likelihood of patient harm arising from prescribed medications: 17/143,936; Population-based medication risk stratification and personalized medication risk score: 16/870,517).
Disclaimer: The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the US Department of Health and Human Services or any of its agencies. The research presented here was conducted by the model participant. Findings might or might not be consistent with or confirmed by the findings of the independent evaluation contractor. CMS considers any findings from the Part D Enhanced MTM model to be preliminary until certified as valid by its Research and Rapid Cycle Evaluation Group (RREG).
Authorship information: Concept and design (AS, SF, HJ, JJ, JT); acquisition of data (MSA); analysis and interpretation of data (AS, SF, DB, HJ); drafting of the manuscript (SF, DB, JT); critical revision of the manuscript for important intellectual content (AS, SF, DB, MSA, JJ, JT); statistical analysis (SF, HJ); provision of study materials or patients (JJ); obtaining funding (JJ); administrative, technical, or logistical support (AS, DB, MSA, JJ, JT); supervision (AS, JT); and other−data processing (HJ).
Address correspondence to: Jacques Turgeon, PhD, 13485 Veterans Way, Ste 410, Orlando, FL 32827. E-mail: firstname.lastname@example.org
1. Pellegrino AN, Martin MT, Tilton JJ, Touchette DR. Medication therapy management services: definitions and outcomes. Drugs. 2009;69(4):393-406. doi:10.2165/00003495-200969040-00001
2. Perlroth D, Marrufo G, Montesinos A, et al. Medication therapy management in chronically ill populations: final report. Published August 2013. Accessed June 23, 2021. https://innovation.cms.gov/files/reports/mtm_final_report.pdf
3. Viswanathan M, Kahwati LC, Golin CE, et al. Medication therapy management interventions in outpatient settings: a systematic review and meta-analysis. JAMA Intern Med. 2015;175(1):76-87. doi:10.1001/jamainternmed.2014.5841
4. Gray C, Cooke CE, Brandt N. Evolution of the Medicare part D medication therapy management program from inception in 2006 to the present. Am Health Drug Benefits. 2019;12(5):243-251.
5. Barlas S. CMS to test enhanced medication therapy management model: aims for greater use of pharmacists, cost savings, and better outcomes. P T. 2016;41(7):423-425, 441.
6. Part D Enhanced Medication Therapy. CMS. Published 2018. Updated December 4, 2018. Accessed June 23, 2021. https://innovation.cms.gov/initiatives/enhancedmtm/
7. Parameswaran Nair N, Chalmers L, Peterson GM, Bereznicki BJ, Castelino RL, Bereznicki LR. Hospitalization in older patients due to adverse drug reactions—the need for a prediction tool. Clin Interv Aging. 2016;11:497-505. doi:10.2147/CIA.S99097
8. Watanabe JH, McInnis T, Hirsch JD. Cost of prescription drug-related morbidity and mortality. Ann Pharmacother. 2018;52(9):829-837. doi:10.1177/1060028018765159
9. Hanlon JT, Schmader KE, Koronkowski MJ, et al. Adverse drug events in high risk older outpatients. J Am Geriatr Soc. 1997;45(8):945-948. doi:10.1111/j.1532-5415.1997.tb02964.x
10. Poudel DR, Acharya P, Ghimire S, Dhital R, Bharati R. Burden of hospitalizations related to adverse drug events in the USA: a retrospective analysis from large inpatient database. Pharmacoepidemiol Drug Saf. 2017;26(6):635-641. doi:10.1002/pds.4184
11. Sultana J, Cutroneo P, Trifirò G. Clinical and economic burden of adverse drug reactions. J Pharmacol Pharmacother. 2013;4(suppl 1):S73-S77. doi:10.4103/0976-500X.120957
12. Budnitz DS, Lovegrove MC, Shehab N, Richards CL. Emergency hospitalizations for adverse drug events in older Americans. N Engl J Med. 2011;365(21):2002-2012. doi:10.1056/NEJMsa1103053
13. Shehab N, Lovegrove MC, Geller AI, Rose KO, Weidle NJ, Budnitz DS. US emergency department visits for outpatient adverse drug events, 2013-2014. JAMA. 2016;316(20):2115-2125. doi:10.1001/jama.2016.16201
14. Hakkarainen KM, Hedna K, Petzold M, Hägg S. Percentage of patients with preventable adverse drug reactions and preventability of adverse drug reactions—a meta-analysis. PLoS One. 2012;7(3):e33236. doi:10.1371/journal.pone.0033236
15. Michaud V, Smith MK, Bikmetov R, et al. Association of a medication risk score with health care outcomes. Am J Manag Care. 2021;27(suppl 16):S280-S291.
16. Bankes DL, Pizzolato K, Finnel S, et al. Medication-related problems identified by pharmacists in an enhanced MTM program. Am J Manag Care. 2021;27(suppl 16):S292-S299.
17. Cicali B, Michaud V, Knowlton CH, Turgeon J. Application of a novel medication-related risk stratification strategy to a self-funded employer population. Published 2018. Accessed June 23, 2021. https://www.carekinesis.com/wp-content/uploads/2018_Cicali_B_Benefits_Q_Application_of_a_novel_medication-related_risk_stratification_strategy.pdf
18. Bankes DL, Jin H, Finnel S, et al. Association of a novel medication risk score with adverse drug events and other pertinent outcomes among participants of the programs of all-inclusive care for the elderly. Pharmacy (Basel). 2020;8(2):87-103. doi:10.3390/pharmacy8020087
19. Ratigan AR, Michaud V, Turgeon J, et al. Longitudinal association of a medication risk score with mortality among ambulatory patients acquired through electronic health record data. J Patient Saf. 2021;17(4):249-255. doi:10.1097/PTS.0000000000000829
20. Al Rihani SB, Smith MK, Bikmetov R, et al. Risk of adverse drug events following the virtual addition of COVID-19 repurposed drugs to drug regimens of frail older adults with polypharmacy. J Clin Med. 2020;9(8):2591-2609. doi:10.3390/jcm9082591
21. Turgeon J, Michaud V, Cicali B, inventors. Patent WO2019089725 - Population-based medication risk stratification and personalized medication risk score. World Intellectual Property Organization. September 5, 2019. Accessed June 23, 2021. https://patentscope.wipo.int/search/en/detail.jsf?docId=WO2019089725
22. Matos A, Bankes DL, Bain KT, Ballinghoff T, Turgeon J. Opioids, polypharmacy, and drug interactions: a technological paradigm shift is needed to ameliorate the ongoing opioid epidemic. Pharmacy (Basel). 2020;8(3):154-172. doi:10.3390/pharmacy8030154
23. Bain KT, McGain D, Cicali EJ, Knowlton CH, Michaud V, Turgeon J. Precision medication: An illustrative case series guiding the clinical application of multi-drug interactions and pharmacogenomics. Clin Case Rep. 2019;8(2):305-312. doi:10.1002/ccr3.2604
24. Tranchina K, Turgeon J, Bingham J. Integrating a novel medication risk score and use of an advanced clinical decision support system into a pharmacist- and nurse-coordinated transition of care program to mitigate drug interactions. Clin Case Rep J. 2021;2(1):1-5. https://clinicalcasereportsjournal.com/download-article-pdf/1000079
25. Bardolia C, Michaud V, Turgeon J, Amin NS. Deprescribing dual therapy in benign prostatic hyperplasia: a patient case. Clin Case Rep J. 2020;10(5). doi:10.37421/jccr.2020.10.134
26. Ballinghoff T, Bain K, Matos A, Bardolia C, Turgeon J, Amin NS. Opioid response in an individual with altered cytochrome P450 2D6 activity: implications of a pharmacogenomics case. Clin Case Rep J. 2020;1(6):1-4. https://clinicalcasereportsjournal.com/download-article-pdf/1000070
27. Bain KT, Knowlton CH, Turgeon J. Medication risk mitigation: coordinating and collaborating with health care systems, universities, and researchers to facilitate the design and execution of practice-based research. Clin Geriatr Med. 2017;33(2):257-281. doi:10.1016/j.cger.2017.01.009
28. Knowlton CH. Medication risk mitigation matrix: a pharmaceutical care opportunity for precision medication. J Am Pharm Assoc (2003). 2015;55(4):354-358. doi:10.1331/JAPhA.2015.15527
29. Turgeon J, Michaud V. Clinical decision support systems: great promises for better
management of patients’ drug therapy. Expert Opin Drug Metab Toxicol. 2016;12(9):993-995.
30. Miller DE, Roane TE, Salo JA, Hardin HC. Evaluation of comprehensive medication review completion rates using 3 patient outreach models. J Manag Care Spec Pharm. 2016;22(7):796-800. doi:10.18553/jmcp.2016.22.7.796
31. Bankes DL, Amin NS, Bardolia C, Awadalla MS, Knowlton CH, Bain KT. Medication-related problems encountered in the Program of All-Inclusive Care for the Elderly: an observational study. J Am Pharm Assoc (2003). 2020;60(2):319-327. doi:10.1016/j.japh.2019.10.012
32. Foley RN, Collins AJ. End-stage renal disease in the United States: an update from the United States Renal Data System. J Am Soc Nephrol. 2007;18(10):2644-2648. doi:10.1681/ASN.2007020220
33. Kautter J, Ingber M, Pope GC. Medicare risk adjustment for the frail elderly. Health Care Financ Rev. 2008;30(2):83-93. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4195052/pdf/hcfr-30-02-083.pdf
34. Ward MA, Xu Y. Pharmacist-provided telephonic medication therapy management in an MAPD plan. Am J Manag Care. 2011;17(10):e399-e409. https://www.ajmc.com/view/ajmc_11oct_ward_e399to409
35. Moore JM, Shartle D, Faudskar L, Matlin OS, Brennan TA. Impact of a patient-centered pharmacy program and intervention in a high-risk group. J Manag Care Pharm. 2013;19(3):228-236. doi:10.18553/jmcp.2013.19.3.228
36. Brophy L, Williams A, Berman EJ, et al. Collaborative DTM reduces hospitalization and healthcare costs in patients with diabetes treated with polypharmacy. Am J Manag Care. 2014;20(3):e72-e81. https://www.ajmc.com/view/collaborative-dtm-reduces-hospitalization-and-healthcare-costs-in-patients-with-diabetes-treated-with-polypharmacy
37. Ferries E, Dye JT, Hall B, Ndehi L, Schwab P, Vaccaro J. Comparison of medication therapy management services and their effects on health care utilization and medication adherence. J Manag Care Spec Pharm. 2019;25(6):688-695. doi:10.18553/jmcp.2019.25.6.688
38. Do N, Fan B, Ghimire E, et al. Evaluation of the Part D enhanced medication therapy management (MTM) model: second evaluation report. Published November 2020. Accessed June 23, 2021. https://innovation.cms.gov/data-and-reports/2020/mtm-secondevalrpt
39. Keehan SP, Stone DA, Poisal JA. National health expenditure projections, 2016-25: price increases, aging push sector to 20 percent of economy. Health Aff (Millwood). 2017;36(3):553-563. doi:10.1377/hlthaff.2016.1627
40. Hui RL, Yamada BD, Spence MM, Jeong EW, Chan J. Impact of a Medicare MTM program: evaluating clinical and economic outcomes. Am J Manag Care. 2014;20(2):e43-e51. https://www.ajmc.com/view/impact-of-a-medicare-mtm-program-evaluating-clinical-and-economic-outcomes
41. Welch EK, Delate T, Chester EA, Stubbings T. Assessment of the impact of medication therapy management delivered to home-based Medicare beneficiaries. Ann Pharmacother. 2009;43(4):603-610. doi:10.1345/aph.1L524
42. Wiggins BS, Saseen JJ, Page RL 2nd, et al.; American Heart Association Clinical Pharmacology Committee of the Council on Clinical Cardiology; Council on Hypertension; Council on Quality of Care and Outcomes Research; and Council on Functional Genomics and Translational Biology. Recommendations for management of clinically significant drug-drug interactions with statins and select agents used in patients with cardiovascular disease: a scientific statement from the American Heart Association. Circulation. 2016;134(21):e468-e495. doi:10.1161/CIR.0000000000000456