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The American Journal of Managed Care January 2015
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Medical Homes: Cost Effects of Utilization by Chronically Ill Patients
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Medical Homes: Cost Effects of Utilization by Chronically Ill Patients

Jason Neal, MA; Ravi Chawla, MBA; Christine M. Colombo, MBA; Richard L. Snyder, MD; and Somesh Nigam, PhD
A longitudinal case-control design was used to evaluate the effects of the patient-centered medical home model on medical costs and utilization among chronically ill patients.
To control for variation at baseline, statistical models were employed to generate adjusted figures for the program effects on costs and utilization for each of the follow-up years. PMPM costs were adjusted using a generalized regression model, which controlled for age, gender, and costs at baseline. For utilization per 1000 patients, Poisson regression models were used to estimate utilization counts after adjustment for baseline differences in age, gender, and risk score. These methods parallel those used in the GHC study. Risk scores were calculated using Verisk Health DxCG Risk Solutions version 3.1,27 a commercial population risk adjustment package. The risk score used patient age, gender, and claims information from all medical encounter and enrollment data to estimate annual total medical resource use. Once adjusted cost and utilization figures were imputed for each of the program years, differences between PCMH practices and controls were compared using the t test for dependent means, using Satterthwaite or unequal variances. The results appear in Tables 2 and 3.


Differences in Utilization

Controlling for baseline differences, PCMH practices maintained significantly lower utilization for hospital admissions (P <.0001) and specialist visits (P <.01) for each year in the follow-up period. PCMH practices also saw 0.3 fewer admissions per patient in 2009, and 0.2 fewer admissions per patient in both 2010 and 2011. Specialist visits were reduced by 12.3 visits per 1000 patients in 2009, and by more than 10 visits per 1000 patients in 2010 and 2011. However, PCMH practices observed significantly higher utilization in ED and outpatient visits, though the adjusted difference in ED visits shrank over the period from 2009 to 2011 (Table 2).

Differences in Cost

Predictably, the observed differences in utilization translated into lower adjusted total PMPM costs. Total costs were significantly lower in PCMH practices during all 3 follow-up years (P <.05). Relative to baseline, overall PMPM costs were $16.50 lower in 2009, a difference of 5.5%. Total PMPM costs were $13.00, and $13.70 lower in subsequent years as well. This reduction was driven by significantly lower inpatient (P <.01) and specialist (P <.0001) costs among PCMH practices over all 3 program years. The relative reduction in specialist costs was particularly pronounced: in 2009, adjusted costs for PCMH were 17.5% lower than those in non-PCMH practices. While significant relative increases in ED PMPM costs (P <.0001) partially offset these reductions, PCMH practices did not experience a significant increase in outpatient costs despite the observed increase in outpatient utilization (Table 3).


This study adds to the growing body of literature, including prior work by the GHC team, which indicates that the initial investments required for primary care practices to adopt PCMH reforms can yield successes in controlling growth in cost and utilization of high-cost, high-intensity medical services.14 GHC is a nonprofit health insurance and care delivery system based in Seattle. That study looked at cost and utilization results between 12 and 21 months after implementation in an attempt to track progress in quality improvement, in creating a sustainable work environment, and in lowering costs. The data source on utilization and costs was the GHC’s costing system, which allocates use and costs for all services provided at GHC facilities and from external claims. Utilization and costs from 7018 continuously enrolled adults at the prototype clinic were compared with those of 200,970 adults enrolled at other clinics in the Puget Sound area.

Three particularly important differences exist between the work by the GHC and this study: this study explores only cost and utilization, without commenting on patient or provider satisfaction; the end points do not match exactly (the GHC study measured cost and utilization after 12, 18, and 21 months, whereas the Pennsylvania study measured results annually over 3 years). Most importantly, this study focuses on chronically ill patients, whereas GHC analyzed all patients. Data comparing the GHC study results with this study’s results appear in Table 4. Variables discussed include differences in inpatient admissions, ambulatory care utilization, and ED and specialty care costs and utilization. For this study, there is also discussion on possible reasons for gaps in cost and utilization between PCMH and non-PCMH practices.

Regarding inpatient admissions, the GHC study showed that differences in all-cause inpatient admissions did not reach significance until the 21-month follow-up, whereas the differences in inpatient admissions per 1000 patients were significant in all 3 program years of the Pennsylvania study. This gap between the 2 studies, in terms of utilization, was also reflected in PMPM costs: the GHC study found a consistent relative reduction of more than $10 PMPM in total costs, but this did not meet statistical significance at α = .05. By comparison, we found statistically significant relative reductions amounting to $16.50 PMPM in the first program year and $13.00 and $13.70 PMPM in subsequent years.

In the case of ambulatory care–sensitive treatment, the medical home practices in the GHC study were able to achieve significant relative reductions in admissions when the analysis was limited to inpatient admissions for ambulatory care–sensitive conditions (P <.001). By controlling for the presence of chronic illness—either by limiting the population to such patients, as in the case of the Pennsylvania study, or by framing the outcomes to focus only on treatment for chronic illness—significant differences become apparent in the outcomes achieved by PCMH and non-PCMH practices. This suggests that, with respect to costs and utilization, the PCMH may lead to meaningful improvements only when applied to relevant subpopulations, such as chronically ill patients.

In the Pennsylvania study, the increased utilization of outpatient care is actually suggestive of further success for the PCMH model. By improving coordination of care, doctors may have been appropriately directing their patients to lower-cost, lower-intensity services, which acted as substitutes for costlier hospital admissions and other services. This finding is especially promising, given that the increased utilization in terms of visits per 1000 patients did not lead to a significant increase in PMPM costs. Similar findings of increased primary care intensity leading to lower costs via substitution have been reported elsewhere.28 Also, Wang et al report using a case-control matched longitudinal cohort study in patients with type 1 and type 2 diabetes mellitus, and found that adoption of the PCMH reduced overall medical cost for diabetic members by 21% in the first year.29 This result largely reflects a drop in inpatient costs, which fell by 44%.

The 2 studies also report disparate findings concerning costs and utilization of specialty care. Patients enrolled in the GHC study’s PCMH practices exhibited fewer ED visits and more specialty care visits, whereas the opposite was true of the Pennsylvania PCMH patients. This difference may be attributable in part to the Pennsylvania study’s focus on chronically ill patients. For example, increasing focus on patient needs and satisfaction may identify unmet healthcare needs across all patients, whereas among patients with chronic illness who are likely to have frequent and ongoing contact with the healthcare system, improved disease management may reduce the need for specialized care. Alternatively, physicians in the Pennsylvania managed care practices may be under-referring patients to specialist services, but these differences in specialty care may simply reflect regional variation in practice.

Finally, with respect to the Pennsylvania study, it is worth noting that in most cases, the gap in cost and utilization between PCMH and non-PCMH practices was seen to decrease over the 3 follow-up years. Although the control practices were required to have completed all 3 program years without formally receiving NCQA recognition, it is possible that some of the innovations adopted as a package by PCMH practices were embraced piecemeal by non-PCMH practices during follow-up years, leading to a gradual flattening of the results across practice type. One possible explanation stems from the fact that non-PCMH practices tended to be in the medium- / average-practice size category, whereas PCMH practices were typically larger group practices. The scale advantages of larger practices might have made information technology or staffing improvements more affordable, with average-size practices lagging behind on the trend. Previous research has indicated that larger practice size is positively correlated with the presence of elements of the PCMH infrastructure.30 Additionally, because the PCMH practices began adopting elements of the NCQA guidelines before obtaining formal recognition, the process of improving outcomes may have started ahead of the medical home pilot program. This self-selection bias may have reduced the apparent impact of PCMH adoption.

The PCMH impact may extend to smaller practices as well. A recent PCMH study that focused on small, independent primary care practices in Rhode Island found that 5 pilot practices increased their NCQA recognition score from 42 to 90 points over a 2-year study period.31 There was a significantly lower rate of ambulatory care– sensitive ED visits in the pilot practices compared with 34 nonpilot practices.

Policy Relevance

Both the GHC experience and the results of the PACCI study suggest that the introduction of medical home innovations into primary care practices may help to curb utilization of high-intensity medical services as well as overall cost growth, despite initial investments in staffing changes and technology infrastructure. The differences in results observed when analyzing the primary care population as a whole, as opposed to the chronically ill cohort tracked in the Pennsylvania study, stresses the importance of quality risk stratification of patients, not only for research purposes, but also for making fair assessments of practice quality and for targeting of resources by medical practices.

A study by the Commonwealth Fund has suggested that with the exception of information technology infrastructure, incremental costs do not rise with increasing “medical homeness” of primary care practices.32 However, both the GHC medical home practices and those in the Pennsylvania pilot made staffing changes as part of their transition, adding physicians and nurse practitioners to allow enhanced patient contact.14 With this in mind, financial incentives to defray the costs of introducing care managers, electronic medical records, registries, and related improvements may be particularly crucial to promoting adoption of the medical home as a model. Cost savings may eventually make the PCMH self-sustaining, with cost savings redirected to improving compensation for primary care doctors5 and to other practice improvements.

Implications for Future Research and Limitations

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