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The American Journal of Managed Care November 2017
Using the 4 Pillars to Increase Vaccination Among High-Risk Adults: Who Benefits?
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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
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Changes in Cardiovascular Care Provision After the Affordable Care Act
Joseph A. Ladapo, MD, PhD; and Dave A. Chokshi, MD, MSc
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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.
During 2010, UMHS cared for more than 12,000 established patients with diabetes (Figure 1) from any source of all-payer claims data. We constructed all utilization and comorbidity variables across five 6-month time periods from July 2008 to December 2010. We then merged claims data from 4 payers with which we had agreements (Medicare FFS, Medicare/Medicaid dual-eligible, commercial-Medicaid managed care, and a large commercial plan) with chronic disease registry data by patient and time period. This resulted in a merged dataset with 9637 unique patients with diabetes (Figure 1). 

However, in 2012, the commercial insurers decided to limit any reporting of results of our analyses to quality improvement reporting. We had an executed Data Use Agreement (DUA) with Medicare, as well as University of Michigan Institutional Review Board approval, so we were able to report analyses using Medicare FFS and dual-eligible patients. When we restricted the study group to Medicare, we included 2826 unique patients in Medicare FFS and dual Medicare/Medicaid (Figure 1). After restricting the data to those 51 years and older, the number of patients per time period ranged from 2221 in the first time point to 1701 in the fifth time point. 

There were a few notable differences in baseline demographic and clinical characteristics of patients in the 2 SMS models (Tables 1 and 2). Patients in the pharmacy-SMS were sicker (2.6 vs 2.3 chronic conditions) than those in the nurse-SMS. The nurse-SMS sites started with higher mean BP of patients at baseline (Table 2) (133/70 vs 129/68 mm Hg; P <.001) and the patients used more primary care (5.7 vs 5.3 annual visits; P <.03), whereas the pharmacy-SMS clinics’ patients used more specialty care (4.9 vs 3.2; P <.001) and had more hospitalizations (0.55 vs 0.39; P <.01) annually. 

Over the duration of the study (2010 vs 2008, which includes implementation of the PCMHs), there were significant (P <.05) downward trends in unadjusted LDL-C (Table 1) (–4 mg/dL for pharmacy-SMS vs –5.6 mg/dL for nurse-SMS), diastolic BP (–1.5 mm Hg for pharmacy-SMS, –1.3 mm Hg for nurse-SMS), and utilization of primary care (0.8 office visits for pharmacy-SMS only). 

When we measured nonlinear trajectories across 2008 to 2010 (including PCMH implementation during 2009) between the 2 models, however, we found small, but statistically significant, differences in quality of care trajectories between the groups. The nurse-SMS patients had an initial increase (ie, positive slope from prior to during PCMH implementation) followed by a decrease in A1C after PCMH implementation, resulting in a net 0.1% improvement. For pharmacy-SMS, the patients’ A1C remained flat at 7.1% (P <.001 for the difference in time trajectories between SMS types) (Figure 2). Second, systolic BP started higher in the nurse-SMS patients (133 vs 129 mm Hg) (Table 2) and had a steeper decline in the early period (P <.01) (Figure 2). 

When we applied propensity scores (inverse probability weights based on comorbid condition count, age, and gender) to the final multivariable models, there were no changes in any of the results concerning differences in change over time by SMS type. 


The use of EHR and registry data merged with administrative claims across healthcare sites and payers has been championed as a hallmark of modern patient-oriented CER.2-5 We were ideally poised to assemble a longitudinal dataset for such use during a 2.5-year period that spanned time pre- and post implementation of a PCMH model across multiple clinical sites in a large healthcare system, with sites differing only by type of SMS: pharmacy-based versus nurse-based SMS. However, our efforts were challenged by changes in the private sector with regard to using claims data for research. Despite the challenges, we were able to use Medicare and dual-eligible data (CMS DUA#23675). We found modest longitudinal improvements in unadjusted LDL-C (4 and 5.6 mg/dL for pharmacy- and nurse-based SMS, respectively) and diastolic BP (1.5 and 1.3 mm Hg for pharmacy- and nurse-based SMS, respectively), thus supporting SMS in general. When comparing the trajectories of the 2 SMS models across the 2.5-year study period, the clinical outcomes were similar. We found small differences in the rate of improvement of glycemic control and systolic BP that modestly favored the nurse-based SMS model, possibly due to a poorer baseline performance (therefore they had more room to improve more quickly). Both groups decreased annual utilization of primary care, but the pharmacy-based SMS group significantly decreased utilization by 0.83 visits compared with their pre-implementation rates. 

Our findings are consistent with prior study results on PCMHs overall that found little effect on clinical outcomes,6 including a study that also used longitudinal methods.27 Specifically regarding diabetes care, our results are consistent with a recent meta-analysis of 48 PCMHs, which found average decreases in LDL-C of 3.9 mg/dL and of 1.5 mm Hg in diastolic BP.14 

To our knowledge, ours is the first study to conduct a concurrent comparison of 2 different SMS models within otherwise similar PCMH models within 1 health system.11 Prior studies have grouped the SMS models, whether nurse- or pharmacist-led, into promotion of self-management or team changes.14,28 Overall, we found statistically significant, but not substantial, differences between SMS models. Slightly poorer baseline values among the nurse-SMS patients likely contributed to greater room for improvement, a finding also observed in meta-analysis.14 It is important to note that improved intermediate measures (LDL-C, A1C, BP) suggest improved cardiovascular risk profiles, but finding cardiovascular benefits would require longer follow-up to realize, a larger sample size of sites beyond our system, and more aggressive interventions.

Because the patients attributed to the 2 SMS models also differed by medical complexity, the small differences in quality we observed could also have been due, in part, to patient-level differences. Patients with more complex comorbid condition burden are more likely to receive recommended healthcare.19-21,23,24,29-31 We lacked the sample size (number of sites) and variation to be able to formally test for confounding by comorbidity, however, so we applied a propensity score by patient as an alternative approach. 

Limitations and Strengths

There are several limitations to this study. First, we lacked a concurrent control group (ie, non-PCMH patients), which decreased our ability to detect differences. Second, because we only had 2 sources of payer data, we cannot generalize our findings across multiple payer sources. Third, clinical outcomes (glycemic control, BP, and lipid control) were calculated only among those monitored for the outcomes (ie, a patient without a lipid level could not be included in the lipid control outcome). When 1 measure depends on the proper performance of an upstream variable, biases in performance can be introduced. One approach is to give a failing score for the downstream performance measure if the earlier process is not performed,32 which would provide additional incentive to perform the upstream care. 

Despite the limitations, our study had certain strengths. We used multiple snapshots across time, which can capture differences in trajectories in addition to overall differences over time. We harnessed 17 sites, garnering a larger sample than would be possible within 1 clinical quality improvement site. 

We faced a key barrier to performing multipayer health services research: loss of permission to use private-payer claims data for research. Until merged datasets can be constructed with equivalent health services variables and durable continuing permission to perform research, it will be difficult to efficiently perform CER across or between payers. Future research in comparing chronic care delivery approaches may be best served in large single-payer system with a diversity of patient complexity, such as the Veterans Affairs Healthcare System.


This research makes use of sponsored creation of a relational database linking clinical and administrative claims data. PCMH and 2 models of SMS improved diabetes care. However, our permission to use the merged dataset for CER was not durable and therefore limited the potential power of performing multipayer research. Future efforts will need to be redoubled to develop further infrastructure that will allow for multipayer CER and care process research.

Author Affiliations: Division of Geriatric and Palliative Medicine (LM, CTC, TLM, JH) and Division of General Medicine (SJB), Department of Internal Medicine, and Department of Family Medicine (CTC), University of Michigan Medical School, Ann Arbor, MI; University of Michigan Health System (KW), Ann Arbor, MI; Geriatrics Research, Education and Clinical Care Center (LM, CTC) and Center for Clinical Management Research (LM, SJB), Ann Arbor VA Healthcare System, Ann Arbor, MI; Departments of Medicine and Population Health, New York University Langone Medical Center (CSB), New York, NY.

Source of Funding: This project was supported by the AHRQ R24 HS019459 (Blaum, Clinical Database to Support Comparative Effectiveness Studies of Complex Patients). Dr Min was also supported by the NIA-Claude Pepper Older Americans Independence Centers at the University of Michigan (AG024824, 2010-12) and the Hartford Foundation. Dr Cigolle was also supported by K08 and the Hartford Foundation.

Author Disclosures: Drs Min, Cigolle, Bernstein, and Ha, and Ms Moore, are employed by the University of Michigan, where the research was conducted. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. 

Authorship Information: Concept and design (LM, CTC, KW, CSB); acquisition of data (LM, SJB, KW, TLM, CSB); analysis and interpretation of data (LM, CTC, KW, TLM, JH, CSB); drafting of the manuscript (LM, CTC, SJB); critical revision of the manuscript for important intellectual content (LM, SJB, JH, CSB); statistical analysis (LM, JH); provision of patients or study materials (CSB); obtaining funding (CTC, CSB); administrative, technical, or logistic support (LM, TLM, CSB); and supervision (LM, CSB). 

Address Correspondence to: Lillian Min, MD, MSHS, University of Michigan Medical School, 300 North Ingalls Bldg, Rm 966, Ann Arbor, MI 48109-2007. E-mail:

1. Institute of Medicine. Initial National Priorities for Comparative Effectiveness Research. Washington, DC: The National Academies Press; 2009. 

2. Etheredge LM. A rapid-learning health system. Health Aff (Millwood). 2007;26(2):w107-w118. doi: 10.1377/hlthaff.26.2.w107.

3. Panzer RJ, Gitomer RS, Greene WH, Webster PR, Landry KR, Riccobono CA. Increasing demands for quality measurement. JAMA. 2013;310(18):1971-1980. doi: 10.1001/jama.2013.282047.

4. Glasgow RE, Vinson C, Chambers D, Khoury MJ, Kaplan RM, Hunter C. National Institutes of Health approaches to dissemination and implementation science: current and future directions. Am J Public Health. 2012;102(7):1274-1281. doi: 10.2105/AJPH.2012.300755.

5. Riley WT, Glasgow RE, Etheredge L, Abernethy AP. Rapid, responsive, relevant (R3) research: a call for a rapid learning health research enterprise. Clin Transl Med. 2013;2(1):10. doi: 10.1186/2001-1326-2-10.

6. Jackson GL, Powers BJ, Chatterjee R, et al. Improving patient care: the patient-centered medical home: a systematic review. Ann Intern Med. 2013;158(3):169-178. doi: 10.7326/0003-4819-158-3-201302050-00579.

7. Rittenhouse DR, Shortell SM. The patient-centered medical home: will it stand the test of health reform? JAMA. 2009;301(19):2038-2040. doi: 10.1001/jama.2009.691.

8. American Academy of Family Physicians; American Academy of Pediatrics; American College of Physicians; American Osteopathic Association. Joint principles of the patient-centered medical home. American Academy of Family Physicians website. Published March 7, 2007. Accessed May 1, 2015.

9. Patient-centered medical home. Blue Cross Blue Shield Value Partnerships website. Published 2014. Accessed September 16, 2015.

10. Gabbay RA, Bailit MH, Mauger DT, Wagner EH, Siminerio L. Multipayer patient-centered medical home implementation guided by the chronic care model. Jt Comm J Qual Patient Saf. 2011;37(6):265-273.

11. Haas L, Maryniuk M, Beck J, et al; 2012 Standards Revision Task Force. National standards for diabetes self-management education and support. Diabetes Care. 2013;36(suppl 1):S100-S108. doi: 10.2337/dc13-S100.

12. Morgan TO, Everett DL, Dunlop AL. How do interventions that exemplify the joint principles of the patient centered medical home affect hemoglobin A1C in patients with diabetes: a review. Health Serv Res Manag Epidemiol. 2014;1:2333392814556153. doi: 10.1177/2333392814556153.

13. Ackroyd SA, Wexler DJ. Effectiveness of diabetes interventions in the patient-centered medical home. Curr Diab Rep. 2014;14(3):471. doi: 10.1007/s11892-013-0471-z.

14. Tricco AC, Ivers NM, Grimshaw JM, et al. Effectiveness of quality improvement strategies on the management of diabetes: a systematic review and meta-analysis. Lancet. 2012;379(9833):2252-2261. doi: 10.1016/S0140-6736(12)60480-2.

15. Smith KA, Sussman JB, Bernstein SJ, Hayward RA. Improving the reliability of physician “report cards.” Med Care. 2013;51(3):266-274. doi: 10.1097/MLR.0b013e31827da99c.

16. Iglehart JK. Assessing an ACO prototype—Medicare’s Physician Group Practice demonstration. N Engl J Med. 2011;364(3):198-200. doi: 10.1056/NEJMp1013896.

17. 2011-2012 eligible professional Clinical Quality Measures (CQMs). Diabetes quality measures (575, 55, 62, 56, 64, 61). CMS website. Published 2012. Accessed May 1, 2015.

18. Jha AK, Perlin JB, Kizer KW, Dudley RA. Effect of the transformation of the Veterans Affairs Health Care System on the quality of care. N Engl J Med. 2003;348(22):2218-2227. doi: 10.1056/NEJMsa021899.

19. Higashi T, Wenger NS, Adams JL, et al. Relationship between number of medical conditions and quality of care. N Engl J Med. 2007;356(24):2496-2504. doi: 10.1056/NEJMsa066253.

20. Min LC, Wenger NS, Fung C, et al. Multimorbidity is associated with better quality of care among vulnerable elders. Med Care. 2007;45(6):480-488. doi: 10.1097/MLR.0b013e318030fff9.

21. Min L, Kerr EA, Blaum CS, Reuben D, Cigolle C, Wenger N. Contrasting effects of geriatric versus general medical multimorbidity on quality of ambulatory care. J Am Geriatr Soc. 2014;62(9):1714-1721. doi: 10.1111/jgs.12989.

22. Min L, Wenger N, Walling AM, et al. When comorbidity, aging, and complexity of primary care meet: development and validation of the Geriatric CompleXity of Care Index. J Am Geriatr Soc. 2013;61(4):542-550. doi: 10.1111/jgs.12160.

23. Min LC, Reuben DB, MacLean CH, et al. Predictors of overall quality of care provided to vulnerable older people. J Am Geriatr Soc. 2005;53(10):1705-1711. doi: 10.1111/j.1532-5415.2005.53520.x.

24. Piette JD, Kerr EA. The impact of comorbid chronic conditions on diabetes care. Diabetes Care. 2006;29(3):725-731.

25. Kerr EA, Lucatorto MA, Holleman R, Hogan MM, Klamerus ML, Hofer TP; VA Diabetes Quality Enhancement Research Initiative (QUERI) Workgroup on Clinical Action Measures. Monitoring performance for blood pressure management among patients with diabetes mellitus: too much of a good thing? Arch Intern Med. 2012;172(12):938-945. doi: 10.1001/archinternmed.2012.2253.

26. Harder VS, Stuart EA, Anthony JC. Propensity score techniques and the assessment of measured 
covariate balance to test causal associations in psychological research. Psychol Methods. 2010;15(3):234-249. doi: 10.1037/a0019623.

27. Kern LM, Edwards A, Kaushal R. The patient-centered medical home and associations with health care quality and utilization: a 5-year cohort study. Ann Intern Med. 2016;164(6):395-405. doi: 10.7326/M14-2633.

28. Shojania KG, Ranji SR, McDonald KM, et al. Effects of quality improvement strategies for type 2 diabetes on glycemic control: a meta-regression analysis. JAMA. 2006;296(4):427-440. doi: 10.1001/jama.296.4.427.

29. Petersen LA, Woodard LD, Henderson LM, Urech TH, Pietz K. Will hypertension performance measures used for pay-for-performance programs penalize those who care for medically complex patients? Circulation. 2009;119(23):2978-2985. doi: 10.1161/CIRCULATIONAHA.108.836544.

30. Woodard LD, Urech T, Landrum CR, Wang D, Petersen LA. Impact of comorbidity type on measures of quality for diabetes care. Med Care. 2011;49(6):605-610. doi: 10.1097/MLR.0b013e31820f0ed0.

31. Woodard LD, Landrum CR, Urech TH, Wang D, Virani SS, Petersen LA. Impact of clinical complexity on the quality of diabetes care. Am J Manag Care. 2012;18(9):508-514.

32. Min LC, Reuben DB, Adams J, et al. Does better quality of care for falls and urinary incontinence result in better participant-reported outcomes? J Am Geriatr Soc. 2011;59(8):1435-1443. doi: 10.1111/j.1532-5415.2011.03517.x.
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