Daniel R. Touchette, PharmD, MA

 To determine if therapeutic gap identification, notification of community pharmacists, and intervention results in increased gap closure, reduced gap closure time, and fewer adherence gaps reopening.

Prospective, controlled, clusterrandomized study.

State of Illinois employees and beneficiaries of State health plans filling prescriptions at independently owned community pharmacies were included. For selected chronic conditions and medications, gaps in medication adherence and omitted essential therapies were identified from prescription claims and sent as alerts for resolution with the patient and/or physician. Adherence and omission gap closure at 90 days were analyzed with Kaplan-Meier (KM) survival curve approach and Cox proportional hazards models including covariates.

 A total of 1433 intervention and 1181 control adherence gaps were identified, while 677 intervention and 534 control omission gaps were generated. Pharmacists intervened on 639 (44.6%) adherence and 506 (74.7%) omission gaps. Gaps were closed more often in intervention than control at 30 days (55.5% in intervention vs 50.6% in control), 45 days (61.1% vs 58.4%, respectively), 60 days (66.1% vs 65.2%, respectively), and 90 days (73.0% vs 72.9%, respectively; adjusted hazard ratio [HR] = 1.242;

 = .022; 95% confidence interval [CI] 1.115-1.385). Adherence gaps reopened less frequently in the intervention group (HR = 0.863; 

 = .012; 95% CI 0.769-0.968). A total of 89 (13.1%) intervention and 29 (5.4%) control omission gaps closed within 90 days (adjusted HR = 1.770;

Independent community pharmacists reduced gaps in care and had fewer reopened adherence gaps, suggesting improvement in adherence. A continuation study will examine the impact of the program on long-term adherence.

This targeted patient outreach program involved identifying patients with poor adherence and omitted therapies (referred to as gaps in care) and notifying independent community pharmacists of these gaps in care.

		Prior to the program, community pharmacists underwent case-based training focused on disease management, motivational interviewing, and communication.

		The program resulted in increased adherence and omission gap closure and fewer adherence gaps reopened. An indicator of adherence was also improved by the program.

		This evaluation of our program can be used to develop more effective, targeted, evidence- based strategies to improve patient adherence and reduce errors of omission.

Underutilization of medications, exemplified by poor medication adherence and errors of omission, is a significant barrier to optimizing patient care. Adherence to chronic medications is reported to be as low as 50% after 1 year of therapy, thereby reducing the clinical effectiveness of drug therapy.

Medication non-adherence is commonly associated with cost, number of medications, duration of treatment, regimen complexity, and patient cognition.3 Likewise, omission gaps, where essential drug therapies are underutilized, are also common. In 1 Veterans Affairs study, omission errors accounted for 20% of all identified medication errors.

 Omissions in therapy have been shown to increase morbidity and mortality with greater potential to cause harm than unjustified prescribing.

In patients with diabetes, hypertension, hypercholesterolemia, and heart failure, poor adherence has been associated with increased hospitalizations and emergency department visits.

Poor adherence to medications has been shown to increase mortality, as exemplified by increased post—myocardial infarction fatality in patients with low adherence to statins and beta-blockers.

 In patients with diabetes and dyslipidemia, poor adherence has also been associated with increased medical and total costs of care.

 Overall, non-adherence to medications is believed to cost the United States healthcare system an estimated $100 billion annually.

While there are no available direct estimates for the implications and cost associated with omitted therapies, these costs could be greater because indicated, evidence-based therapies are not being used at all.

Automated physician notifications of needed omitted therapies have also been tried to increase use of these medicines.

 While many notifications were either ignored or perhaps were not true gaps, automated physician notification did result in increased use of omitted therapies. The involvement of a pharmacist to screen out these unnecessary notifications may be more efficient, but has not been evaluated.

Studies in the community setting aimed at improving patient adherence using mailings10 and telephone reminder systems

 have metwith mixed results. Those programs that focus solely on reminders tend to be less effective, since medication adherence is generally considered to be multifaceted and affected by system and health barriers, patient behaviors and beliefs, and patient self-efficacy.

As such, multifaceted interventions are more likely to be successful in changing patient

behavior and improving adherence. 16 More comprehensive interventions addressing adherence have generally shown better results.

Pharmacists are in an optimal position to address therapeutic gaps in care. Community pharmacists are knowledgeable about potential barriers and solutions to adherence issues. They have frequent contact with their patients, know them well, and when needed, reach out to physicians on their behalf. They are also widely considered a trusted source for providing information to patients and practitioners about medication therapies.

 Therefore, with the right supportive infrastructure, the community pharmacist may be very effective at validating and addressing therapeutic gaps in care related to underutilization of medications. Such an infrastructure includes pharmacists having access to evidence-based gaps in care alerts and comprehensive medication and fill history at the point of care.

The objective of this study was to determine if the identification of therapeutic gaps in care from prescription claims data, notification of community pharmacists of gaps through alerts, and intervention on those alerts results in higher gap closure, reduced time to closure, and reduced proportion of gaps reopening in the case of adherence gaps.

This study was a prospective, cluster-randomized, controlled quality assurance study. The study was approved by the University of Illinois at Chicago institutional review board and conducted in compliance with all regulations of the Health Insurance Portability and Accountability Act of 1996.

All pharmacies were required to 1) service members in the State of Illinois network, 2) be located in Illinois, 3) be independently owned, 4) utilize a commercially available web-based clinical documentation platform capable of relaying real-time gap information from Medco to pharmacists and capturing pharmacists’ documentation of gap causes and status, patient disposition, and comments, and 5) have 10 or more patients with identified care gaps over the 13-week period prior to randomization. In all, 96 independently owned community pharmacies met inclusion criteria and were invited to participate. Pharmacies were randomized to either provide the intervention or not (usual care control).

Population and Patient Inclusion Criteria

State of Illinois employees and beneficiaries (spouses, children, and retired workers) with continuous eligibility during the study period, and all of their pharmacy claims, were included. Patients were assigned to a particular pharmacy based on where the patient was filling the majority of their prescriptions in the 6 months prior to the study. Patient assignment was solely for the purpose of providing the intervention. Specifically, gap alerts were sent to the assigned intervention pharmacy. Control pharmacies did not receive gap in care alerts. At the start of the project, a letter was sent to the intervention pharmacy patients to inform them of the services being offered. Patient participation was optional; patients were not informed about the study. Patients were not required to fill medications at the assigned pharmacy.

Adherence and omission gaps in care were identified daily, using Medco’s prescription claim warehouse and defined clinical criteria. Gaps in care were transmitted as alerts to the pharmacy via the web-based platform. Adherence gaps occurred when a patient on selected chronic medications had a medication possession ratio (MPR) of less than 80% over the last 18 months and was late to fill the medication. Omission gaps occurred when a patient with a selected chronic condition was missing a medication on their profile. Omission gaps were selected based on documentation of clinical evidence and general consensus supporting the medication’s use for a particular condition. The alert definitions and situations for which they were generated are shown in 

Intervention Pharmacy Training and Practice

Control pharmacies and pharmacists continued to provide the services normally provided to their patients and did not receive gap in care alerts. Beyond the usual patient services, intervention pharmacies provided the additional service of addressing care gaps identified by Medco and communicated via a web-based clinical documentation and communication platform. All participating pharmacists at intervention pharmacies were required to undergo a certified continuing education program comprising 7.5 hours of online and 3.5 hours of in-person case-based training conducted by residencytrained clinical pharmacists with experience in medication therapy management and ambulatory care specialty clinics. Training focused on disease management (see Table 1 for conditions), motivational interviewing, and communication. Program sponsors held monthly telephone conferences with the intervention pharmacists to gather feedback on process improvement and best practice sharing and provide updates. Additional education and communication materials were provided when requested, such as the American Diabetes Association treatment guidelines.

The approach to addressing therapeutic gaps depended on the type of gap identified, as well as patient-, pharmacist-, and pharmacy-specific factors. In-person communication was encouraged for the first patient visit, with follow-up visits being in-person or by telephone. Communication with prescribers by telephone or facsimile was encouraged. However, pharmacists were allowed to conduct the intervention as appropriate, according to the pharmacy’s policies, procedures, and established relationships.

An adherence gap was considered “closed” once a prescription was filled for the medication in question. An adherence gap was considered “reopened” if an alert was generated for the gap for a second time during the study period. An omission gap was considered closed when a new prescription was written for an appropriate medication. Typically, adherence gaps were addressed directly with the patient, while omission gaps were addressed with the patient’s primary care or other provider as appropriate.

Pharmacists documented their interventions for the identified gap in care using the web-based clinical platform. All pharmacist activities were within the scope of practice, as defined by Illinois State and national pharmacy organizations and by the Illinois State practice laws. Pharmacies were reimbursed $30 for the initial session and $15 for each of up to 2 follow-up sessions to a maximum of $60 per gap addressed.

A power analysis, conducted prior to the study start, established that a sample of 410 adherence and omission gaps per group were required to detect a difference in absolute gap closure rate of 10%, assuming alpha of 0.05 and power of 0.8. We estimated that approximately 1500 adherence gaps and 1000 omission gaps would be available for analysis if 45 pharmacies were enrolled in each group. We targeted 48 intervention and 48 control pharmacies, in case any pharmacies needed to drop out.

Prescriber, pharmacy, and patient characteristics were analyzed with summary statistics of counts and proportions, as were data regarding number of gap openings, closings, and in the case of adherence gaps, reopening. Gaps that existed at the start of the study as well as new gaps that occurred within 90 days after the study start date were analyzed. Gap closure was analyzed at 90 days after an alert was sent to the pharmacy (or wouldhave been sent in the case of control pharmacies). Where a patient had 2 gaps during the intervention period for the same gap category, only the first gap was used in assessing gap closure. Unadjusted analyses of gap closure at 90 days were initially conducted using the χ

 test statistic. In the case of adherence gaps, an unadjusted χ

 analysis was conducted assessing the proportion of gaps that reopened prior to the end of the study.

Adherence and omission gap closure over time were analyzed using the Kaplan-Meier (KM) survival curve approach

 For the adherence gap model, patient age and gender and total number of Medco prescription claims during the study period and Medco patient count for the pharmacy were included as potential covariates in the model. For the omission gap model, the same patient and pharmacy covariates were included, along with prescriber age, gender, specialty, practice type, practice size (estimated by number of Medco patients), and number of Medco prescription claims. Variable selection methods were used to find the final model following the selection criteria; both the entry and stay

Articles

A program identifying and resolving care gaps involving community pharmacists resulted in increased adherence and omission gaps closure and fewer adherence gap reopenings.

Identification of and Intervention to Address Therapeutic Gaps in Care

Lessons learned from the implementation of a pharmacist-delivered medication therapy management (MTM) intervention in primary care (PC) can inform future MTM studies and be adopted into real-world clinical settings. We sought to describe the variations and challenges of patient recruitment, enrollment, MTM pharmacist visits, and telephone follow-up in a 3-arm randomized trial of MTM interventions conducted at 3 health centers.

Using a post-study structured interview, we interviewed study personnel, clinical pharmacists, and investigators about 5 study domains: recruitment, enrollment visits, MTM pharmacist visits, telephone follow-up, and data collection.

All centers screened clinic schedules and conducted queries of administrative databases to identify eligible participants. Patients were recruited either during existing primary care visits or by mailing letters with telephone follow-up. Patients with many medical problems, with transportation difficulties, or who were unaccompanied by a family member were less likely to enroll. MTM visits scheduled separately from other clinic appointments had higher cancellation or no-show rates. Provider response to pharmacist recommendations was low overall but better when the provider was acquainted with the pharmacist who was making contact.

 Off-site implementation of MTM services results in lower participation by patients and providers. Future MTM studies should consider integrating MTM services within the clinic during existing appointments by a pharmacist familiar to the primary care provider.

This paper describes the lessons learned from implementing a pharmacist-delivered medication therapy management (MTM) intervention.

		Patients with many medical problems, transportation difficulties, or who were unaccompanied by a family member were less likely to enroll. MTM visits scheduled separately from other clinic appointments had higher cancellation or no-show rates. Provider response to pharmacist recommendations was better when the provider was acquainted with the pharmacist who was making contact.

		Off-site implementation of MTM services resulted in lower participation by patients and providers. Usual care implementation can be improved by integrating these services within the clinic during existing appointments by a pharmacist familiar to the primary care provider.

The Medication Evaluation and Drug Use Problem Identification to Improve Safety in High Risk Medicare Beneficiaries (MEDIS-MB) study was a randomized, multisite trial of different medication therapy management (MTM) strategies conducted at the University of Illinois at Chicago (UIC), the Baylor Health Care System in Dallas, Texas, and the Duke Primary Care Research Consortium (PCRC) in Durham, North Carolina. Patients 65 years or older with 3 or more chronic illnesses, 6 or more medications, and 1 or more risk factors for development of a drug-related problem (DRP) (eg, recent hospitalization or multiple providers) were randomly assigned to 1 of 3 treatment arms: usual care; basic MTM by patient interview only; or enhanced MTM with access to a clinical synopsis of medical history, laboratory data, and medications from the patient’s medical record.1 The overall results showed that MTM reduced DRPs and increased patient satisfaction. Access to the clinical synopsis in the enhanced MTM arm resulted in fewer medication list discrepancies.

 Touchette and colleagues discussed the potential benefits of expanding MTM services to include platforms for clinical record data sharing for community pharmacist access, thus potentially improving patient outcomes by reducing adverse drug events (ADEs).

Communicating the study implementation issues that we experienced can inform clinicians, administrators, researchers, and payers who may be interested in 1) clinical adoption of the intervention, or 2) conducting future MTM studies. In this paper, we intend to describe the variations and challenges of patient recruitment, enrollment, MTM pharmacist visits, and telephone follow-up within this comparative effectiveness trial.

The detailed MEDIS-MB study design has been previously described.

 Institutional review boards at participating health systems approved the study; written informed consent was obtained from all patients. During the enrollment phase, weekly screening and enrollment reports, including reasons for ineligibility and patient refusal, were sent from Duke and Baylor to the UIC coordinating center and reported to the study sponsor. At the end of study, the investigators developed a questionnaire of 5 study domains—recruitment, enrollment visits, MTM pharmacist visits, telephone follow-up, and data collection issues&mdash;to understand the challenges of study implementation and gather feedback on how to improve future studies.

Under the recruitment domain we asked questions about the recruitment method (mail, phone, or in person), time spent on recruitment, previous MTM program enrollment, differences in patient participation, whether incentives were adequate, consent form problems, and how the various study team members approached the clinics and providers. Under the enrollment visit domain we asked questions about the length of visit and space available. Under the MTM pharmacist visit domain we asked about scheduling the visit and the number of visits needed/required, work flow issues, contacting or faxing the provider with recommendations, access to pill bottles, and completing the clinical synopsis, medication lists, and drug-related problem surveys. Under the telephone follow-up domain we asked about the length of the phone call and issues with filling out the symptom survey scale, resource utilization survey, and pharmacist satisfaction form. Finally, under the data collection domain, we asked questions about the methods of data collection (ie, filling out, making copies of, and mailing paper forms to the coordinating center), monthly study calls, and overall clinic participation. An initial conference call with the study personnel, clinical pharmacists, and investigators was held to gather initial responses to the questionnaire items; a second call confirmed their responses and gathered additional feedback. Personnel who were unable to attend the calls submitted written responses to the questionnaire.

The MEDIS-MB study was conducted at 3 institutions to reach the enrollment goal of 600 participants (approximately 200 participants per site). UIC worked with 1 family medicine (FM) and 1 internal medicine (IM) clinic, Baylor enrolled patients from 2 senior health centers, and Duke recruited participants from 6 primary care clinics (1 FM, 5 IM) within its practice-based research network (PBRN). Enrolling at 3 institutions resulted in ethnic diversity among participants (51% black, 48% white, 1% Asian/American Indian).

 outlines the number of patients that were screened, contacted, and enrolled. Of the 3084 patients who were screened, we enrolled 637 (21%) participants. Patients were most often deemed ineligible for participation because they did not meet the inclusion criteria of taking at least 6 chronic medications (42%); patients most commonly refused to participate due to lack of interest (69.5%). Enrollnment lasted 9 months at Duke, 12 months at Baylor, and 13 months at UIC. Both Duke and Baylor enrolled more than 200 participants to help reach the total enrollment goal. Additional subjects (more than 600) were enrolled to replace patients who were lost to follow-up.

Feedback about the 5 study domains from investigators, coordinators, and pharmacists is shown in 

. Under the recruitment domain, study staff reported that administrative and pharmacy databases were useful for identifying eligible patients. All 3 sites initially used recruitment letters and phone calls to mimic real-world pharmacy implementation; however, Baylor and UIC switched to in-person clinic recruitment because response rates were low with the mail approach. Duke was able to continue the letter/telephone recruitment strategy given the larger patient population from 6 participating clinics. The differences in recruitment approach translated into the differences in the proportion of participants enrolled to the patients screened for this study (35.3% at UIC, 30.7% at Baylor, and 13.3% at Duke). Patients who were approached at the clinic visit and accompanied by a caregiver were more likely to participate. These caregivers viewed the MTM intervention as a benefit. The presence of a provider champion and a motivated clinic staff was felt to be useful for enhancing recruitment.

For the enrollment visit domain, patients with more chronic illnesses and multiple clinic appointments were more likely to decline participation due to perceived study visit burden. Patients who were concerned about the consent form language, cost of gas, or transportation difficulties were also less likely to participate. The free MTM intervention was perceived as a benefit; however, the study payment ($10 per completed visit, or $30 total) was perceived as too low by the patients. In addition, some patients in the control group inquired whether the MTM intervention could be offered to them after all study follow-up was completed. The amount of space available to conduct the enrollment and MTM visits varied at the 3 sites. UIC had ample space in the academic clinics, but limited space in the outpatient pharmacy. Baylor

used the clinic room space prior to the provider entering the room to see the patient, so timing was essential to limit interference with clinic work flow. Duke held all study visits at a central location that had available rooms depending on the day of the week.

In the MTM pharmacist domain, MTM visits occurring separate from an existing clinical visit had higher no-show or cancellation rates. Reminder phone calls helped reduce missed visits. A total of 186 study participants (88.6%) in the basic MTM group attended the first MTM visit, and 155 (73.8%) participants completed their second MTM visit. A similar proportion of participants in the enhanced MTM group completed their first (n = 196, 89.9%) and second (n = 165, 75.7%) visits. The first MTM visit lasted 45 to 60 minutes, and the second MTM visit lasted 15 to 30 minutes. Access to pill bottles was essential for delivering the MTM intervention because patients had trouble recalling medication names and dosages. The second visit was often unnecessary because most drug-related problems were identified during the first visit. Therefore, the second visit was often used to reinforce or educate patients on the previous medication recommendations. The DRP and ADE forms were straightforward but perceived as impractical for real-world (non-study) settings. DRP form was based on the Modified Pharmaceutical Care Network Europe (PCNE) Drug Assessment Form V 5.01 (see 

) and served as both a checklist and as a documentation tool for this study.

 The ADE form was used to assess symptoms potentially related to medications and contained questions from parts 2 and 3 of a validated research tool developed by Jarernsiripornkul and colleagues.

) of this questionnaire assesses potential side effects of medications through a system-by-system approach. Part 3 (

) of the questionnaire assesses the status of the side effect if the drug was stopped. Non-study MTM providers are unlikely to be able to use these surveys for assessing DRPs and ADEs in routine clinical practice, given the length and detail of questions contained in these documents. After the MTM visit, pharmacists sent medication recommendations via facsimile to patients’ primary care providers (PCPs). Providers often returned the study facsimiles without indicating whether they accepted the pharmacist’s recommendation, or they failed to return the form. Response to e-mail, phone, or face-to-face communication better ensured receipt of the recommendation and implementation of a plan of action.

Within the telephone follow-up domain, study coordinators reported that these calls ranged from 10 to 60 minutes (average, 20 minutes); the length depended on the number of symptoms discussed. Research coordinators asked patients questions from the symptom, utilization, and patient satisfaction surveys at 90 and 180 days. The ADE symptom survey (19 survey items with multiple potential responses to each item, followed by a 10-item survey for each symptom identified by the participant; see Appendix B) was difficult to administer by phone because the interviewer had to maintain a patient’s focus on whether the symptom was related to medication. Also, patients did not refer to the paper copy of the ADE symptom surveys while they were being asked the questions by the interviewer on the telephone. A study folder with a copy of the ADE symptom survey, patient satisfaction survey, patient visit log, consent form, medication list, and contact information of study personnel was given to each patient at the enrollment visit. For the visit (clinic/emergency department/inpatient) utilization form, patients had difficulty recalling the dates of these visits, but having access to the scheduling system allowed the coordinator to find the information. Patients were given a visit log to write down their visit dates, and patients

were either fully compliant or noncompliant with the visit log. Patients with higher socioeconomic status were more likely to complete the visit log.

Finally, under the data collection domain, the coordinators noted that paper case report forms (CRFs) required them to copy and send forms to the coordinating center, which took a lot of time and effort. Suggestions included 1) using carbonless (no carbon required [NCR]) paper to keep 1 copy of the CRF at the site and send the other to the coordinating center for data entry, or 2) creating a web-based data entry system for electronic data capture (EDC). The EDC system could be complemented by telephone follow-up surveys housed in a computer-assisted telephone interview system, which would allow immediate data entry at the time of the call.

We describe our experience implementing a prospective, randomized study to inform clinicians, researchers, and funders about the challenges and successes of communitybased MTM trials. As noted above, successes included identifying potentially eligible patients via medication and billing databases, participation of 10 community clinics resulting in enrollment of a diverse patient population, and monthly study calls that helped standardize operational issues. Challenges included the need to contact numerous patients (5 screened for every 1 enrolled), identifying space to conduct and schedule the MTM visit, contacting PCPs with medication recommendations, telephone follow-up using lengthy symptom questionnaires with variability in patients’ recall of clinic/emergency department visits or hospitalizations, and a paper-based data collection system. A description of the MTM intervention and tool kit with copies of the study forms is available on the Agency for Healthcare Research and Quality website.

Our enrollment challenges are similar to those experienced by pharmacy benefit managers (PBMs) in contacting a population at high risk for a medication-related adverse event due to multiple chronic conditions and medications. MTM interventions by PBMs who contact eligible patients by phone or mail also experience low participation by highly motivated patients and even lower participation by patients who would most benefit from pharmacy coaching.

 A medication review survey packet mailed to 4000 US Department of Defense beneficiaries resulted in 1469 responses (38.1%) to the consent letter, 606 consents (15.7%) to participate, and 373 (9.3%) completed surveys.

 In this study, mailed letter and telephone contact resulted in less participation (13%) than recruitment from within the clinic during existing appointments (30%-35%). Interestingly, about 70% of eligible patients stated that they were not interested in study participation. Non-participation is likely due to the presence of a consent form, required study visits, and telephone followup. Participation rates in a real-world community MTM intervention will hopefully be higher if conducted outside the context of a clinical research project.

These findings provide specific opportunities to improve the design of future MTM studies and disseminate and implement MTM within the community setting. Under study design considerations, recruiting at-risk patients during an existing clinical appointment and delivering the MTM intervention on the same day can reduce participant burden (removes the need for a visit on a separate day or location) and allow the pharmacist to contact the prescriber onsite (not via facsimile) with any newly identified problems related to medication. Flexibility for the MTM pharmacist to decide whether a second MTM visit (or additional visits for complex cases) is required and whether it is done in person or by phone can optimize the effectiveness and efficiency of visits. Lengthy patient-reported outcome surveys (drug-related adverse event reporting, satisfaction, etc) should be shortened to facilitate administration by phone or at a follow-up clinic visit. Healthcare utilization (clinic or emergency department visits, hospitalizations) is better obtained from review of an electronic health record than from patient recall. Finally, the use of a web-based data entry system is preferred to transmitting paper forms to a central location.

For the dissemination and implementation of this MTM intervention within a usual care setting, the ideal situation would be the colocation of a clinical pharmacist within the primary care clinic to deliver the intervention at the point of care. Incorporating this pharmacist as a team member within the patient-centered medical home may be feasible in integrated health systems. Smaller independent practices without the resources required for a dedicated pharmacist may consider collaboration with the MTM clinicians from PBMs, as long as this includes communication from the PCP to the patient about the importance of participation to reduce ADEs. In a usual care setting, the barriers of patient consent and lengthy study-related forms would be removed; therefore, we expect that patient buy-in would be greater than within a trial. Creation of a quality improvement strategy to reduce drug-related problems by implementing MTM would allow prospective measurement of the intervention over time.

In summary, we found that implementation of MTM services not directly linked to a primary care visit (either geographically or temporally) resulted in lower participation by patients and providers. Therefore, participation in future MTM studies or usual care implementation can be improved by integrating these MTM services within the clinic during existing appointments by a pharmacist familiar to the primary care provider.

The authors acknowledge the following study sites and collaborators:

From the University of Illinois at Chicago (coordinating site), Chicago, IL:

Medication Therapy Management Clinicians: 

Shiyun Kim, PharmD; Jessica Michaud, PharmD; Annette Pellegrino, PharmD; Daphne Smith Marsh, PharmD; Jessica Tilton, PharmD; and Lori Wilken, PharmD.

Vicki Groo, PharmD; and Mary Ann Kliethermes, PharmD.

 Ketsya Amboise, PharmD; Adriana Bautista; Zenobia Dotiwala; Yash Jalundhwala; Xiaochen Luo; Alexandra Perez; Sapna Rao; and Funda Tiryaki, PharmD.

Duke University DEcIDE Center Principal Investigator, past: 

From RTI International, Research Triangle Park, NC:

RTI International DEcIDE Center Principal Investigator, past:

From Baylor Health Care System, Dallas, TX:

Nadine Rayan, MHA. Research Assistants: Louann Cole, BA; Teri Cowling, MS; Kate Newman, RN; Bruce Koehler, MPH.

From the Agency for Healthcare Research and Quality, Bethesda, MD:

Lessons learned from implementation of a pharmacist-delivered medication therapy management intervention in primary care can inform future studies and be adopted into real-world clinical settings.

Daniel R. Touchette, PharmD, MA

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