Medical Homes and Cost and Utilization Among High-Risk Patients | Page 2
Published Online: March 24, 2014
Susannah Higgins, MS; Ravi Chawla, MBA; Christine Colombo, MBA; Richard Snyder, MD; and Somesh Nigam, PhD
Propensity score matching was used to select a sample of controls which were similar to the PCMH cases with respect to both practice- and patient-level demographics and characteristics. The propensity score included practice size, practice location, age, gender, DxCG risk score, chronic conditions (including asthma, diabetes, congestive heart failure, chronic obstructive pulmonary disease, and coronary artery disease), and median income for member’s zip code of residence. Matching successfully controlled for the significant differences at baseline between the group of cases and pool of potential controls, both for the overall comparison and among the cohort of highest risk patients (Table 2A
Differences in PMPM cost and utilization per 1000 patients between PCMH and non-PCMH practices for the 3 follow-up years were compared using regression analysis. This method provides an estimate of the differences between treatment and control groups before and after treatment. Since the members included in this study are the same in each time period (ie, panel data), the difference-in-differences model can be simplified and is more statistically powerful. The difference between baseline and each time period can be modeled as follows (separate model for each time period difference):
Y i,1 – Y i,0 = δ + βX i,1 + ε I
where Y i,1
– Y i,0
is the difference between the repeated outcome measure for each observation, δ is the effect of time on all units, X i,1
is the treatment indicator, and β is the treatment effect.17
Cost and utilization regressions controlled for concurrent risk in the comparison year and having a chronic condition in the baseline year, factors that could influence both cost and utilization in the comparison year. For example, when comparing 2010 with baseline, the 2010 risk score was added to the model to adjust for any new diagnoses a member might have acquired in 2010. Presence of a chronic condition at baseline was used as a covariate because chronic members often continue to incur costs, or incur even higher costs, over time. In order to test for the treatment effect for just the high-risk members, a high-risk indicator was introduced as covariate (ie, as a main effect and interaction term with the treatment indicator) into the model using the whole population. Additionally, a random intercept term was used in the model to account for any practice-level effects.RESULTS
When comparing utilization and PMPM cost for all matched cases and controls during the 3 follow-up years with respect to baseline characteristics, no difference-in-differences comparison reached significance at α = .1 (Appendices B
). The regression results limited to the 10% of pooled cases and controls with the highest DxCG risk scores resulted in clear differences between PCMH and non-PCMH. PCMH practices achieved statistically significant decreases in utilization of inpatient medical services in all 3 program years, amounting to reductions of 61, 48, and 94 hospitalizations per 1000 patients. In the first 2 program years, these reductions were accompanied by statistically significant adjusted savings in inpatient costs ($115 PMPM in 2009 and $62 PMPM in 2010). The decline in utilization of inpatient hospital services contributed to lower total medical costs for patients enrolled in PCMH practices during the first 2 study years as well. Cases had an adjusted total savings of $107 PMPM in 2009, representing an 11.2% reduction from baseline. For 2010, the difference was $75 PMPM, corresponding to a 7.9% reduction.
While total medical costs decreased, there were areas in which high-risk patients in PCMH practices experienced increases in costs and utilization. This cohort saw a statistically significant increase in specialist visits in 2009, and corresponding increases in PMPM spending for specialist care in 2009 and 2010 (Tables 3
This study offers particular methodological contributions to the PCMH literature. The approach of combining propensity score matching to create comparable case and control pools with difference-in-differences regression analysis to adjust for practice- and patient-level variation and baseline cost and utilization characteristics allows improved control for factors which might otherwise confound efforts to study the effects of PCMH adoption. Inclusion of a non-PCMH control group and assessing differences over time allows the analyses to control for trends which may have affected all practices, regardless of PCMH status. Along with controlling for practice size, excluding practices which transitioned to the PCMH model in 2010 helped to limit the impact of self-selection on results. Because adoption of the PCMH reforms does not occur instantaneously, some non-PCMH practices may have incorporated aspects of the medical home without having yet risen to the level of eligibility for NCQA recognition. Confining the control group to later adopters diminishes this issue.
The findings of this study add new texture to the existing PCMH literature. After controlling for baseline differences, no statistically significant differences between patients enrolled in PCMH and non-PCMH practices were observed. However, when looking at the patients with highest risk scores in the pool of matched patients and practices, PCMH model adoption was shown to lead to a significant relative reduction in total costs in years 1 and 2, and significantly lower numbers of inpatient admissions in all 3 years. This suggests that the average patient may not be the relevant unit of observation for evaluating the impact of PCMH adoption. Rather, high risk patients with multiple comorbidities are the most logical targets for interventions aimed at supporting self-management, conveying test results in a timely and clear fashion, and coordinating follow-up and specialist care. Researchers may miss cost and utilization improvements if they confine their analyses to the typical patient, since healthcare costs are primarily driven by relatively rare events concentrated in few individuals. For example, during the baseline year, all cases and controls had 73 and 78 admissions per 1000 patients, respectively; but among the high-risk pool, these numbers increased to 566 and 540.
The observation that relative cost reductions are driven by decrease in inpatient admissions among high-risk patients suggests that the PCMH model is having its intended effect— improvements to information technology permit physicians to better assess patient needs, and better coordination of care means that ongoing issues can be managed at lower levels of care intensity (self-management, primary care) rather than after manifesting as crises requiring hospitalization. Even the observation of increased spending for specialist care can be interpreted as being consistent with this conceptualization: as providers gain access to better quality information about patient needs through improved medical record-keeping and care is coordinated across multiple sites, the patients with the highest medical risk may be appropriately directed to more frequent contact with specialists. This could drive up the cost of 1 component of care, while the use of appropriate early interventions helps control costs overall.Study Limitations
Despite extensive controls through matching, regression analysis, and study design, some self-selection issues may persist. A number of the initial pool of control practices received NCQA recognition in 2011, meaning that there may have been some “wind up” time in the prior year for practices identified as controls. However, this should have had the effect of reducing the apparent difference between PCMH and non- PCMH practices for the 2010 analyses.
Additionally, other studies of the medical home model have focused on patient satisfaction and quality improvement measures, which are essential aspects of the PCMH model and central to providing exceptional primary care, but these attributes are not the focus of this study. Over a sufficient time frame, improved integration of care may lead to better outcomes for patients with lower levels of health risk as well, so ongoing assessments of PCMH adoption should be conducted with these considerations in mind.CONCLUSION
While no differences were observed when assessing patients from the full distribution of risk scores, this study demonstrated that adoption of the PCMH model was associated with significantly reduced costs and utilization for those members at highest risk, particularly with respect to inpatient care. High-risk members are the most costly to health plans, so it logically follows that the most benefit can be gained by targeting these members with programs such as the medical home. When evaluating PCMH programs, it is important to focus both interventions and evaluations on relevant populations
Author Affiliations: Independence Blue Cross, Philadelphia, PA (SH, RC, CC, RS, SN).
Source of Funding: This study was funded by Independence Blue Cross, which is an independent licensee of the Blue Cross and Blue Shield Association. All of the authors were employed by the Independence Blue Cross during the course of the study.
Author Disclosures: The authors (SH, RC, CC, RS, SN) all report employment with Independence Blue Cross, which funded this study.
Authorship Information: Concept and design (SH, RC, RS, SN); acquisition of data (SH); analysis and interpretation of data (SH, RC, CC, RS, SN); drafting of the manuscript (SH, RS); critical revision of the manuscript for important intellectual content (SH, CC, RS, SN); statistical analysis (SH); administrative, technical, or logistic support (RC, RS); supervision (RC, CC, SN).
Address correspondence to: Susannah Higgins, MS, 1901 Market St, Philadelphia, PA 19103. E-mail: firstname.lastname@example.org.
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