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Patient-Centered Medical Homes

The concept of the medical home is an old one, developed in the 1960s for the pediatric population, but lately has grown in popularity. It hopes to provide for patients comprehensive, coordinated care for the whole person.

Patient-centered medical home (PCMH) models have gained attention recently as a way to reengineer the care-delivery process so as to reduce healthcare costs and to increase healthcare quality. Geisinger Health Systems in Danville, PA began using a PCMH model of primary care in 2006 which they termed Proven Health Navigator (PHN). At its core, the PHN intervention seeks to reduce costs over time and improve quality via:

  •  Prevention: use of automation to enhance early detection screening and interventions
  • Chronic Disease Optimization: use of high-touch/high-technology tools to manage exacerbations
  • Comprehensive Care Management: application of nurse case managers focused on proactive care coordination for medically complex patients

This was a retrospective observational study that used a fixed-effects regression model to test the hypothesis that PCMH primary care clinics reduce total cost of care. The primary outcome variable was defined as the total cost of care per member. The units  of measurement were further broken down into per-member-per-month visits and included payments for inpatient and outpatient facilities,  professional services, and prescription drugs. Approximately 27,000 members age 65 years or older were assessed in a total of 1 million member-month visits from 2006-2010 at 43 PCMH primary care clinics within a single healthcare system.

Each individual acted as their own control in the fixed-effects setting. That is, each member prior to any PCMH exposure acted as his or her own control over time, up to and beyond 24 months, after PCMH exposure. Several defined covariates known to effect costs were measured: prescription drug coverage, length of exposure to PCHM model versus PCHM enrollment alone (in other words, measuring learning curve variations between clinics upon implementation of the new model of care), age, disease complexity, and baseline clinical characteristics prior to PCMH implementation (e.g. percentage of male patients, average patient age, average total cost, average disease complexity score, number of inpatient admissions/readmissions). As prescription drug coverage was expected to have a large effect on costs, regression models for PCMH exposure were run with and without prescription coverage.

Variations between PCMH clinics with regard to the details of PCMH primary clinic model elements (redesigned automation screening reminders, high-touch/high-technology chronic disease management, ratio of nurse case managers per patient) were not measured. Furthermore, quality care measures such as changes in HgbA1c for diabetes care and other disease-specific health outcomes were not measured.

The total cumulative costs savings over the study period was 7.1 percent for PCMH models with prescription drug coverage interactions and 4.4 percent for PCMH models without prescription drug coverage interactions. Coefficient estimates on the interaction terms between drug coverage and PCMH models were consistently positive suggesting that a significant interaction exists between the two variables. A longer period of PCHM model exposure was associated with lower total costs. The largest and most significant savings were achieved when PCMH models were present for at least 24 months. When return on investment (ROI) for the PCMH model redesigns from 2006-2010 were assessed, results demonstrated an upward trend but had yet to reach a statistically significant break-even point.

The authors of this study concluded that PCMH can lead to sustainable cost savings over time. In future years they anticipate significant cost savings as patients remain enrolled.


The evidence for the effectiveness of PCMH models on healthcare costs is excellent news as provisions in the Affordable Care Act specific to PCMHs begin to take shape. Although the methodology and data analysis used in this large study was very well done and complete, the study does not make clear which element of PCMH had the most significant effect on cost reduction. Was it the shared-decision making, better communication, better care coordination, better health outcomes, better care quality measures, or a combination of the above?

Further studies defining the hierarchical importance of each of these elements will help with implementation so that the most important and effective PCMH components can be adopted quickly. Speedy translation of these findings would help to accelerate an already slow-to-change healthcare system and temper an exploding national debt which will be heavily influenced by health care costs.

Perhaps a clarification in metrics to include national health costs would be a better way to measure cost containment than the ROI metric which tends to prefer quick and system-specific results.

Maeng, DD, et al. “Reducing Long-Term Cost by Transforming Primary Care: Evidence from Geisinger’s Medical Home Model.”  Am J Managed Care. 2012; 18 (3): 149-155.


Jennifer Shine Dyer, MD, MPH, FAAP

Jennifer Shine Dyer, MD, MPH, FAAP
About Jennifer Shine Dyer, MD, MPH, FAAP

Jennifer Shine Dyer, MD, MPH, FAAP is a pediatric endocrinologist in private practice at Central Ohio Pediatric Endocrinology and Diabetes Services (COPEDS) in Columbus, Ohio. Contact: Website | Facebook | Twitter | More Posts

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