Currently Viewing:
The American Journal of Managed Care March 2015
Evaluation of Care Management Intensity and Bariatric Surgical Weight Loss
Sarit Polsky, MD, MPH; William T. Donahoo, MD; Ella E. Lyons, MS; Kristine L. Funk, MS, RD; Thomas E. Elliott, MD; Rebecca Williams, DrPh, MPH; David Arterburn, MD, MPH; Jennifer D. Portz, PhD, MSW; and Elizabeth Bayliss, MD, MSPH
Potential Savings From Increasing Adherence to Inhaled Corticosteroid Therapy in Medicaid-Enrolled Children
George Rust, MD, MPH, FAAFP, FACPM; Shun Zhang, MD, MPH; Luceta McRoy, PhD; and Maria Pisu, PhD
Innovation in Plain Sight
Karen Ignagni, MBA, President and Chief Executive Officer, America's Health Insurance Plans
Currently Reading
Early Changes in VA Medical Home Components and Utilization
Jean Yoon, PhD, MHS; Chuan-Fen Liu, PhD, MPH; Jeanie Lo, MPH; Gordon Schectman, MD; Richard Stark, MD; Lisa V. Rubenstein, MD, MSPH; and Elizabeth M. Yano, PhD, MSPH
Developing a Composite Weighted Quality Metric to Reflect the Total Benefit Conferred by a Health Plan
Glen B. Taksler, PhD; and R. Scott Braithwaite, MD, MSc, FACP
Insurance Impact on Nonurgent and Primary Care-Sensitive Emergency Department Use
Weiwei Chen, PhD; Teresa M. Waters, PhD; and Cyril F. Chang, PhD
Cost Differential by Site of Service for Cancer Patients Receiving Chemotherapy
Jad Hayes, MS, ASA, MAAA; J. Russell Hoverman, MD, PhD; Matthew E. Brow, BA; Dana C. Dilbeck, BA; Diana K. Verrilli, MS; Jody Garey, PharmD; Janet L. Espirito, PharmD; Jorge Cardona, BS; and Roy Beveridge, MD
The Combined Effect of the Electronic Health Record and Hospitalist Care on Length of Stay
Jinhyung Lee, PhD; Yong-Fang Kuo, PhD; Yu-Li Lin, MS; and James S. Goodwin, MD
Strategy for a Transparent, Accessible, and Sustainable National Claims Database
Robin Gelburd, JD, BA
Treatment Patterns, Healthcare Utilization, and Costs of Chronic Opioid Treatment for Non-Cancer Pain in the United States
David M. Kern, MS; Siting Zhou, PhD; Soheil Chavoshi, MS; Ozgur Tunceli, PhD; Mark Sostek, MD; Joseph Singer, MD; and Robert J. LoCasale, PhD
Trends in Mortality Following Hip Fracture in Older Women
Joan C. Lo, MD; Sowmya Srinivasan, MD; Malini Chandra, MS, MBA; Mary Patton, MD; Amer Budayr, MD; Lucy H. Liu, MD; Gene Lau, MD; and Christopher D. Grimsrud, MD, PhD
Long-Term Outcomes of Analogue Insulin Compared With NPH for Patients With Type 2 Diabetes Mellitus
Julia C. Prentice, PhD; Paul R. Conlin, MD; Walid F. Gellad, MD, MPH; David Edelman, MD; Todd A. Lee, PharmD, PhD; and Steven D. Pizer, PhD
Factors Affecting Medication Adherence Trajectories for Patients With Heart Failure
Deborah Taira Juarez, ScD; Andrew E. Williams, PhD; Chuhe Chen, PhD; Yihe Goh Daida, MS; Sara K. Tanaka, MPH; Connie Mah Trinacty, PhD; and Thomas M. Vogt, MD, MPH

Early Changes in VA Medical Home Components and Utilization

Jean Yoon, PhD, MHS; Chuan-Fen Liu, PhD, MPH; Jeanie Lo, MPH; Gordon Schectman, MD; Richard Stark, MD; Lisa V. Rubenstein, MD, MSPH; and Elizabeth M. Yano, PhD, MSPH
Significant changes were found in patients' utilization of healthcare related to early implementation of medical home components in VA primary care clinics.
In the baseline year, the low and high categories of each PCMH component represented roughly equal distributions of clinics; the exceptions were access and scheduling, which had more scores in the low category (Table 2). The range of the component scores overall, and for low and high categories, is also shown in Table 2. The proportion of clinics in the high-scoring groups increased over time, and the majority of clinics reported PCMH component scores in the high-scoring groups for all components in FY 2011. Almost one-fifth of primary care clinics represented in the study sample were large VAMC-based clinics, and most were community-based outpatient clinics (CBOCs), with the majority of these being leased CBOCs. Almost one-third of clinics were in nonmetropolitan areas.

Changes in Utilization and Costs Over Time

During the study period from just prior to widespread PACT implementation to 2 years after PACT implementation began, the mean number of primary care visits decreased from 4.81 to 3.99 visits per patient, which represented a 17% decrease. The rate of specialty care visits was negligibly higher, and telephone visits rose by 85% (all P <.001) (Table 3). ED visits per patient rose slightly (7%), and ACSC hospitalizations per patient also rose from 0.02 to 0.03 per patient (all P <.001). The mean total costs of VA care increased from $8469 to $9887 per patient during the study period (P <.001).

Increased Medical Home Features and Utilization and Costs

Multi-level, multivariable models adjusted for both time-varying factors and fixed effects for time-invariant factors, and estimated how changes in reported PCMH components in primary care clinics explained some of these increases or decreases in use of VA care over time (Table 4). High organization of practice scores was significantly related to 0.13 fewer mean primary care visits per patient compared with patients in low-scoring clinics (P = .012). High care coordination and transitions in care scores were significantly related to 0.06 more specialty care visits compared with the low scores (P = .010), after controlling for patients’ health status and time trend. The change in telephone visits was significantly higher over time (P <.001), but changes due to increases from low to high PCMH scores across all components were not statistically significant. High scores in care coordination and transitions in care decreased the mean number of ED visits by 0.04 visits per patient (P = .018), but high quality and performance improvement increased ED visits by 0.03 visits per patient (P = .032), relative to low scores. Admissions for an ACSC also increased over time after adjusting for worse health status (both P <.001), and none of the PCMH components were related to higher admissions. The only significant increase in costs was explained by high risk or worse comorbidity (P <.001), and again, none of the PCMH component scores were significantly related to total costs per patient.


VA primary care clinics reported large improvements in adoption of all PCMH components from FY 2009 to FY 2011, and there were significant changes in utilization and costs for a cohort of primary care patients during this time. While various PCMH components (eg, access and scheduling) were hypothesized to increase access to and frequency of primary care, the only component that was related in adjusted models was organization of practice. Features such as team huddles and tracking lab tests were actually associated with fewer primary care visits per patient, possibly through better efficiency of primary care practice. Greater specialty care visits were modestly related to higher care coordination/transitions in care scores, so better procedures to coordinate care appeared to facilitate referrals to specialty care.

While increased care coordination/transitions of care was significantly related to fewer ED visits, higher quality and performance improvement scores were related to more ED visits. Care coordination features that reduced problems due to transitions from hospitalization or referral appeared to reduce ED care that may be unnecessary. It is unclear why greater quality/performance improvement features would be related to more use of ED care by patients. There was a significant increase in telephone visits over time, and although it was not related to any specific changes from low to high PCMH components, the push overall to increase virtual care under PACT may have led to increased telephone care. None of the PCMH components were related to potentially avoidable hospitalizations for an ACSC, although the rates in both years were very low in this cohort. Changes in healthcare costs were also not associated with increased PCMH component scores.

Our findings contribute new evidence on the specific aspects of PCMH implementation that are related to different types of healthcare utilization, and some of these results are consistent with early evidence from a PCMH demonstration in Group Health, which showed that patients had higher rates of telephone visits and specialist visits, and lower rates of ED visits in PCMH sites after 1 year.2 In addition, the Group Health demonstration showed significant reductions in hospitalizations and costs per patient after 2 years, which we did not observe.1 Unlike the Group Health demonstration, which was implemented in 1 clinic, VA’s PACT initiative was rolled out to all clinics—that have considerable heterogeneity in size, staffing, governance, quality improvement orientation, and patient populations served—which may partly explain differences in evaluation results.

An earlier VA study found that higher baseline adoption of PCMH components of access/scheduling and care coordination/transitions were significantly related to lower risk of avoidable hospitalizations among primary care patients.4 While we found that increased scores of these PCMH components were not related to avoidable hospitalizations, there may be a lag between implementation of features and observing effects in patients’ outcomes. Moreover, reducing ACSC hospitalizations in a stable cohort of patients may be difficult to achieve.

It is also unknown to what extent individual PCMH features (eg, using an open-access model or maintaining a disease registry) were fully functioning. The percent of clinics in the high categories of all medical home components score jumped during the study period so that most clinics reported adopting a high number of possible PCMH features. Hence, some implementation may have been rudimentary. Preliminary evidence suggests wide variation in how VA practices adopted various PCMH features, such as enhancing open access through practice redesign, or new interventions13 with successful implementation influenced by local VA leadership engagement, staffing resources, and access to information and knowledge. 13 Additional challenges, including defining team roles and interpersonal confl ict, have also caused implementation barriers in some sites.14 In addition to site-specific factors affecting PACT implementation, the total number of VA enrollees increased from 5.1 million veterans in 2009, to 5.5 million veterans in 2011.15 The challenges of serving a larger volume of patients may have contributed to fewer face-to-face visits and placed additional burdens on providers in some sites.


The Medical Home Builder survey was self-reported, so we were not able to validate the adoption of reported PCMH features; because of ongoing PACT evaluation efforts, clinics may have been motivated to provide more positive responses as a result. While the statistical methods used here accounted for measurable and unmeasurable fixed factors over time, it did not account for any time-varying omitted factors, so if unmeasured time-varying factors such as some other technological change were related to reported PCMH features and patients’ utilization and costs, the effects of PCMH features would be biased.

We did not have reports of non-VA utilization, so if patients shifted to using more non-VA care such as ED visits, we could not measure this change. Finally, we focused on regular primary care users who continued to use VA outpatient care during the study period, so our results may not be generalizable to less frequent users of primary care or new users who were younger and had different demands for VA care.


These results document short-term changes during the early implementation of PACT that can provide feedback as PACT continues to be implemented and developed. While take-up of many PCMH features appeared to be high after the initiative began, certain improvements in care under organization of practice and care coordination/transitions appear to have affected only certain types of non-acute outpatient care; reductions in acute care ED visits and potentially avoidable hospitalizations largely remain to be seen. Identifying areas for ongoing improvement is critical to ensuring long-term success of care transformation under PACT. Refining PACT models in later stages may require more than adding new interventions; it may involve evaluation of staffing and other resource needs, better understanding of cost-effective care, ensuring support of clinical leadership, and widespread dissemination of successful practices. Better measures of PACT implementation are also needed to evaluate the longer-term consequences of PACT.


The authors wish to acknowledge Jack Needleman and Ciaran Phibbs for valuable comments on earlier drafts.

Author Affiliations: Health Economics Resource Center (JY, JL), and Center for Innovation to Implementation (JY), VA Palo Alto Health Care System, Menlo Park, CA; Center for Innovation for Veteran-Centered and Value-Driven Care, VA Puget Sound (C-FL), Seattle, WA; Department of Health Services, University of Washington (C-FL), Seattle, WA; Patient Care Services, Veterans Health Administration (GS, RS), Washington, DC; Center of Innovation for the Study of Healthcare Innovation, Implementation, & Policy, VA Greater Los Angeles (LVR, EMY), Sepulveda, CA; RAND Corp (LVR), Santa Monica, CA; Department of Health Policy and Management, Fielding School of Public Health (LVR, EMY), and School of Medicine (LVR), University of California, Los Angeles, CA.

Source of Funding: This work was supported by the Department of Veterans Affairs, Veterans Health Administration, Patient Care Services (XVA 65-018). Dr Yano’s effort was covered by a VA HSR&D Senior Research Career Scientist Award (Project #RCS 05-195). The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States federal government.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. VA Patient Care Services designed and administered the survey, and Drs. Stark and Schectman from VA Patient Care Services contributed to the interpretation of results.

Authorship Information: Concept and design (JY, LVR, EMY, CL); acquisition of data (EMY); analysis and interpretation of data (JY, CL, JL); drafting of the manuscript (JY, CL); critical revision of the manuscript for important intellectual content (LVR, EMY, RS, GS); statistical analysis (JY, JL); provision of patients or study materials (RS, GS); obtaining funding (LVR, EMY); administrative, technical, or logistic support (JL); supervision (JY, LVR, EMY).

Address correspondence to: Jean Yoon, PhD, MHS, 795 Willow Rd (152 MPD), Menlo Park, CA 94025. E-mail:
1. Reid RJ, Coleman K, Johnson EA, et al. The Group Health medical home at year two: cost savings, higher patient satisfaction, and less burnout for providers. Health Aff (Millwood). 2010;29(5):835-843.

2. Reid RJ, Fishman PA, Yu O, et al. Patient-centered medical home demonstration: a prospective, quasi-experimental, before and after evaluation. Am J Manag Care. 2009;15(9):e71-e87.

3. Rosland AM, Nelson K, Sun H, et al. The patient-centered medical home in the Veterans Health Administration. Am J Manag Care. 2013;19(7):e263-e272.

4. Yoon J, Rose DE, Canelo I, et al. Medical home features of VHA primary care clinics and avoidable hospitalizations. J Gen Intern Med. 2013;28(9):1188-1194.

5. Prevention quality indicators technical specifications. AHRQ website. Accessed February 18, 2015.

6. Medical Home Builder [computer program]. Version 1.0. Philadelphia, PA: American College of Physicians; 2009.

7. Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43(11):1130-1139.

8. Area Resource File (ARF). Rockville, MD: Health Resources and Services Administration, Bureau of Health Professions; 2009-2010. Health Resources and Services Administration; HHS website. Accessed December 1, 2012.

9. Chapko MK, Borowsky SJ, Fortney JC, et al. Evaluation of the Department of Veterans Affairs community-based outpatient clinics. Med Care. 2002;40(7):555-560.

10. Wooldridge JM. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press; 2002.

11. Froot KA. Consistent covariance matrix estimation with cross-sectional dependence and heteroskedasticity in financial data. J Financial and Quantitative Analysis. 1989;24(3):333-355.

12. Stata Statistical Software [computer program]. Version Release 11. College Station, TX: StataCorp LP; 2009.

13. True G, Butler A, Lamparska BG, et al. Open access in the patient-centered medical home: lessons from the Veterans Health Administration. J Gen Intern Med. 2013;28(4):539-545.

14. Solimeo SL, Hein M, Paez M, Ono S, Lampman M, Stewart GL. Medical homes require more than an EMR and aligned incentives. Am J Manag Care. 2013;19(2):132-140.

15. National Center for Veterans Analysis and Statistics. Number of Veteran Patients by Healthcare Priority Group: FY2000 to FY2012. Washington, DC: Veterans Health Administration, Office of Policy and Planning; 2012.
Copyright AJMC 2006-2020 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