Currently Viewing:
The American Journal of Managed Care November 2017
Using the 4 Pillars to Increase Vaccination Among High-Risk Adults: Who Benefits?
Mary Patricia Nowalk, PhD, RD; Krissy K. Moehling, MPH; Song Zhang, MS; Jonathan M. Raviotta, MPH; Richard K. Zimmerman, MD, MPH; and Chyongchiou J. Lin, PhD
The Influence of Provider Characteristics and Market Forces on Response to Financial Incentives
Brock O’Neil, MD; Mark Tyson, MD; Amy J. Graves, SM, MPH; Daniel A. Barocas, MD, MPH; Sam S. Chang, MD, MBA; David F. Penson, MD, MPH; and Matthew J. Resnick, MD, MPH
Patients' Perspectives of Care Management: A Qualitative Study
Ann S. O’Malley, MD, MPH; Deborah Peikes, PhD, MPA; Claire Wilson, PhD; Rachel Gaddes, MPH; Victoria Peebles, MSW; Timothy J. Day, MSPH; and Janel Jin, MSPH
Impact of Health Reform on Young Adult Prescription Medication Utilization
Amy Pakyz, PharmD, PhD, MS; Hui Wang, PhD; and Peter Cunningham, PhD
Reframing the Unaffordability Debate: Patient Responsibility for Physician Care
Katherine Hempstead, PhD; Josh Gray, MBA; and Anna Zink, BA
Electronic Reminder's Role in Promoting Human Papillomavirus Vaccine Use
Jaeyong Bae, PhD; Eric W. Ford, PhD, MPH; Shannon Wu, BA; and Timothy Huerta, PhD, MS
Improving Antibiotic Stewardship: A Stepped-Wedge Cluster Randomized Trial
Adam L. Sharp, MD, MS; Yi R. Hu, MS; Ernest Shen, PhD; Richard Chen, MD; Ryan P. Radecki, MD, MS; Michael H. Kanter, MD; and Michael K. Gould, MD, MS
Changes in Cardiovascular Care Provision After the Affordable Care Act
Joseph A. Ladapo, MD, PhD; and Dave A. Chokshi, MD, MSc
Currently Reading
Diabetes Care Improvement in Pharmacist- Versus Nurse-Supported Patient-Centered Medical Homes
Lillian Min, MD, MSHS; Christine T. Cigolle, MD, MS; Steven J. Bernstein, MD, MPH; Kathleen Ward, MPA; Tisha L. Moore, MPH; Jinkyung Ha, PhD; and Caroline S. Blaum, MD, MS

Diabetes Care Improvement in Pharmacist- Versus Nurse-Supported Patient-Centered Medical Homes

Lillian Min, MD, MSHS; Christine T. Cigolle, MD, MS; Steven J. Bernstein, MD, MPH; Kathleen Ward, MPA; Tisha L. Moore, MPH; Jinkyung Ha, PhD; and Caroline S. Blaum, MD, MS
In this longitudinal comparative effectiveness study of different chronic disease self-management support approaches within 1 system, both pharmacist- and nurse-led patient-centered medical home approaches improved diabetes care.

Objectives: In 2009 and 2010, 17 primary care sites within 1 healthcare system became patient-centered medical homes (PCMHs), but the sites trained different personnel (pharmacists vs nurses) to improve diabetes care using self-management support (SMS). We report the challenges and successes of our efforts to: 1) assemble a new multipayer (Medicare, Medicaid, commercial) claims dataset linked to a clinical registry and 2) use the new dataset to perform comparative effectiveness research on implementation of the 2 SMS models. 

Study Design: Longitudinal cohort study. 

Methods: We lost permission to use private-payer data. Therefore, we used claims from Medicare fee-for-service and Medicare/Medicaid dual-eligible patients merged with chronic disease registry data. We studied 2008 to 2010, which included 1 year pre- and 1 year post the 2009 implementation time period. Outcomes were outpatient and emergency department visits, hospitalizations, care process (use of statin), and 3 intermediate outcomes (glycemic control, blood pressure [BP], and low-density lipoprotein cholesterol [LDL-C]). 

Results: In our sample of 2826 patients, quality of care improved and utilization decreased over the 2.5 years. Both approaches improved lipid control (LDL-C decreased by an average of 4 mg/dL for pharmacy-SMS and 5.6 mg/dL for nurse-SMS) and diastolic BP (–1.5 mm Hg for pharmacy-SMS and –1.3 mm Hg for nurse-SMS), whereas only the pharmacy-led approach decreased primary care visits (by 0.8 visits). The groups differed slightly on 2 measures (glycated hemoglobin, systolic BP) with respect to the trajectory of improvement over time, but performance was similar by 2.5 years. 

Conclusions: Diabetes care improved during PCMH implementation systemwide, supporting both nurse-led and pharmacist-led SMS models. 

Am J Manag Care. 2017;23(11):e374-e381
Takeaway Points

We compared 2 chronic disease self-management support approaches (pharmacist- vs nurse-led) within 1 healthcare system over 2.5 years. 
  • Both approaches improved lipid control and diastolic blood pressure.
  • Only the pharmacy-led approach decreased primary care visits. 
  • When examining the trajectory of change over 2.5 years, there were small differences in the trajectory of glycated hemoglobin and systolic blood pressure change favoring the nurse-led approach. However, both differences could be explained by preexisting differences in patient populations prior to patient-centered medical home implementation. By study end, there was no difference in either measure. 
  • Therefore, we conclude that both approaches can improve the quality of diabetes self-management support.
The American Recovery and Reinvestment Act (ARRA) invested more than $1 billion in redesigning healthcare delivery systems.1 In response to interest in using electronic health records (EHRs) to perform comparative effectiveness research (CER) on chronic disease management,2-5 we were funded by ARRA to develop a unique database that linked a longitudinal chronic disease registry database to multipayer claims from Medicare, Medicaid, commercial, and county insurance plans. The chronic disease registry, originally designed to support the quality metrics required by the various payers, contained care process measures and some intermediate outcomes. It also had the advantage of using a physician-adjudicated process to confirm patient inclusion. Our goal was to create a large relational database with the flexibility to allow construction of longitudinal datasets to answer specific questions about quality and utilization. The power of such a database would facilitate research that compares care among clinical sites and payer types and examines differing approaches to care.

As a proof of concept, we aimed to test for measurable improvement over time in diabetes care quality and utilization during the implementation of patient-centered medical homes (PCMHs) in the University of Michigan Health System (UMHS) in 2009, including 1 year pre- and 1 year post implementation (resulting in the study window 2008-2010). Of the conditions captured by our registry (including diabetes, heart failure, coronary artery disease [CAD], and asthma), we chose to study diabetes, the largest and most well-established disease registry. Prior research on PCMHs has shown inconsistent benefits from PCMH efforts, such as improved quality or utilization of healthcare,6 so we used this unique opportunity to measure the longitudinal improvement in individual patients’ quality of diabetes care before, during, and after PCMH implementation.

To meet required PCMH elements,7-9 UMHS recognized the importance of self-management support (SMS) in accomplishing 2 principles, whole-patient and multidisciplinary care. UMHS devoted the most resources to developing SMS for diabetes, a high-priority condition. 

All sites were operating under a common administration and therefore were unified under the same EHR, staffing formularies, quality metrics, chronic disease registries, and efforts to add extended hours and improve communication with patients. Personnel from all participating sites underwent the same quality improvement to improve diabetes management SMS care, which included education sessions, training in the use of laminated cards, standing lab-order sets, note templates, patient handouts, a database, and flow sheets to track improvement. However, there was lack of consensus among the sites regarding which type of staff to train: some sites chose to train pharmacists, while others felt that a clinical nurse coordinator model would be more effective. Both SMS models have been shown, separately, to improve diabetes care quality,10-14 making SMS a reasonable question for CER. 

In this article, we report the challenges and successes of our experience with assembling and using the new multipayer diabetes dataset, and we perform CER on PCMH and 2 SMS models implemented within a single health system. We hypothesized that there would be an overall improvement over time in the diabetes care process and intermediate outcomes (eg, glycemic control) and declining utilization of outpatient and inpatient visits, but that there would be no difference on any of the measures between the 2 SMS models. 



UMHS is a large multisite health system consisting of a main university hospital and 4 specialty hospitals. UMHS provides ambulatory care to more than 220,000 established patients. At the time of the PCMH implementation in 2009, there were 17 primary care sites; as of 2017, there are 27. 


In 2005, the UMHS faculty group practice developed chronic disease registries for several diseases: diabetes, heart failure, asthma, and CAD. These registries were maintained by the UMHS Quality Management Program (QMP), which was responsible for determining patient eligibility for the chronic disease registries by triangulation of patients’ problem lists, laboratory test results, and medications relevant to that particular condition. 

For entry into the diabetes registry, a patient needed to have 2 outpatient visits (with primary care or endocrinology) or 1 hospitalization or emergency department (ED) visit in the past 3 years with a billing diagnosis of diabetes, which was validated by evidence of a diabetes medication (eg, insulin), diabetic supply (eg, glucometer), or glycated hemoglobin (A1C) greater than 6.4%; excluded were gestational diabetes and steroid-induced diabetes. Quality reporting was limited to those patients considered active, defined as 2 ambulatory visits within the past 2 years and 1 visit within the past 13 months.15 This method allowed an unbiased assessment of care in which providers cannot selectively enter patients into the registry. 

All registries were composed of longitudinal semi-annual data calculated on June 30 and December 31 reflecting whether recommended healthcare was provided in the prior 12 months. This enabled the QMP to provide health plans with longitudinal reports regarding the quality of care provided to their patients. 

In addition to maintaining the chronic disease registries, the QMP also submitted supplemental clinical data to health insurers and received multipayer claims to facilitate quality measurement and improvement. In 2010, the QMP was actively collecting ambulatory and hospital claims data for patients who selected a UMHS physician (eg, a managed care plan) or were attributed to a UMHS physician (eg, fee-for-service [FFS] plans) from 4 payers: 1) Medicare FFS, 2) dual-eligible Medicare/Medicaid, 3) a commercial plan providing Medicaid managed care, and 4) a large commercial insurer in Michigan offering a variety of levels of coverage. 

By the start of this project, we had permission from these 4 payers to merge claims data with care process measures into 1 relational dataset for future health services research. The Medicare FFS data and agreement to use the data for research were part of a Data Use Agreement (#23675) with CMS for the Physician Group Practice Medicare Demonstration Project16 and approved for human subjects research at the University of Michigan (#HUM00041118).


From the QMP diabetes registry, we selected only patients 51 years or older. Although quality measures are applied to patients aged 18 to 75 years,17 we limited the data to patients 51 years or older due to sparse data for younger patients. In addition, prior study results have shown that despite age limits on quality measures, provision of the care does not stop at age 75.18 Because there was no upper age limit for inclusion in the QMP registry, we included patients older than 75 years for whom no quality measures for diabetes are currently defined. 


In 2008, in response to nationwide efforts to improve health system performance, UMHS began developing PCMHs at each of its 17 primary care sites. By 2009, all sites were certified as PCMHs by Blue Cross Blue Shield of Michigan criteria.8,9 

Given the option of selecting either pharmacists or nurses to be trained in improved SMS for the care of diabetes, the sites were divided. Twelve sites chose the pharmacist model (pharmacy-SMS) and were, in general, the internal medicine practices, whereas the 5 sites that chose the nurse-coordinator model (nurse-SMS) were, in general, the family medicine practices. Pediatrics-only (another 9 sites) and geriatrics-only clinics (1 site) did not participate. Patients could have primary care at only 1 site. 


We considered 3 utilization outcomes (ED visits, outpatient visits, and inpatient visits) and all quality measures collected by the QMP. Utilization of each type was expressed as number of visits over the past 12 months tallied repeatedly at the end of each 6-month period. For the care of diabetes, we decided to use 4 of the 7 collected quality measures: 1 care process variable (whether or not a statin was ordered, as a dichotomous outcome) and 3 intermediate clinical outcomes (glycemic control [A1C], in % points; blood pressure [BP], systolic and diastolic, in mm Hg; and low-density lipoprotein cholesterol [LDL-C], in mg/dL, all as continuous variables). For each of five 6-month reporting periods during the 2.5 years, the quality measures were calculated using the most recent data up to 12 months, resulting in 5 time points. 


We used χ2 tests to compare patient characteristics between the 2 SMS types, using the baseline sample of patients at time point 1. We used t tests to test for adjusted simple temporal trends in the outcomes over the 2.5-year study. 

Implementation of the PCMH at each of the 17 sites took place during time points 2, 3, and 4, over varying months that could span more than one 6-month period. Because we were unable to designate a single point in time when we could directly assess a change in slope, we assessed the mean slope in outcomes across the 5 time points, where time point 1 assuredly occurred before implementation of the PCMH at all sites and time point 5 assuredly occurred at least 6 months after the implementation of the PCMH at all the clinical sites. To analyze each outcome measure, we used multilevel regression (logistic for statin use; linear for the continuous utilization and intermediate outcomes), including a random intercept for each patient. Our predictors were SMS type (pharmacy-SMS vs nurse-SMS), time (in 5 half-year increments), quadratic transformation of time (time-squared, which tests for curvilinear trajectories), and time interactions with SMS type (SMS type multiplied by both the linear and quadratic time term). Multilevel regression allows use of data from all patients, even those who entered late, exited the dataset early, or had missing time points. Because patients who utilize less ambulatory care generally are delivered less recommended care,19-21 dropping patients with incomplete data would be expected to result in biased estimates for the QMP measures. Using this method, the result of the time variable indicates mean change in each outcome per time period (ie, the slope over time) for the entire sample, the SMS-type variable indicates the overall mean difference in the outcome between the 2 groups, and the time-SMS interaction term indicates the overall difference in slope over time between the 2 groups. 

Patients seeking care at the pharmacist-SMS sites were more complex (had a higher comorbid condition count) than those at the nurse-SMS sites. Because a more complex patient population22 might facilitate providing better diabetes care,19,20,23-25 we performed a propensity score model. We calculated inverse probability weights26 for each patient based on age, gender, and comorbid condition count to adjust for each patient’s likelihood of seeking care at either of the 2 types of SMS sites, then applied the weights as controls to the final multivariable models.


Copyright AJMC 2006-2018 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
Welcome the the new and improved, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up

Sign In

Not a member? Sign up now!