Implementing a Whole Health Model in a Community Mental Health Center: Impact on Service Utilization and Expenditures

Individuals with serious mental illnesses have a lower life expectancy than the general population (1,2). Their increased mortality risk is due, in part, to a higher prevalence of chronic general medical conditions, including obesity (3), metabolic syndrome (2), diabetes, and cardiovascular disease (1). Managing chronic conditions among individuals with serious mental illnesses is particularly challenging given that many psychiatric medications have side effects such as weight gain, high blood pressure, and increased diabetes risk (1,2). In addition, compared with the general population, individuals with serious mental illnesses have higher rates of tobacco use and are more likely to engage in other unhealthy behaviors, which further complicates treatment of their general medical conditions (1,2,4).

Many studies have documented the inadequate identification and treatment of general medical conditions among individuals with serious mental illnesses. One study found that only 30% of individuals with serious mental illnesses received preventive health care during a one-year period (5), and another study noted that general medical conditions are often not detected among individuals with serious mental illnesses until the conditions are quite severe (1,6). Mental health providers often do not routinely conduct basic health screening, such as blood pressure or weight monitoring, even among individuals taking psychiatric medications (7–10). When conditions such as diabetes or cardiovascular disease are detected among individuals with serious mental illnesses, these individuals tend to receive substandard care, despite the availability of well-defined treatment protocols (1).

Several factors contribute to inadequate treatment of general medical conditions among individuals with serious mental illnesses, including the difficulty of navigating the health care system (7) and the historical fragmentation of treatment for mental health and other medical conditions. To remedy such fragmentation, community mental health centers have made efforts to integrate treatment for mental health and other medical conditions for this population. Such efforts have been successful in affecting some health outcomes (11). There is, however, limited research on the impact of integration efforts on the use of acute services and overall health care expenditures among the population of individuals with serious mental illnesses. One study analyzed the impact of integrated care on hospitalizations for clients of two facilities and found that the facility with a more established integration program had significant reductions in hospitalizations and hospital costs relative to a comparison group; however, no significant effects on hospitalizations were identified at the facility with less experience in integrating care (12).

In January 2013, Kitsap Mental Health Services (KMHS), a community mental health center in Kitsap County, Washington, implemented Race to Health! The program addresses concerns about inadequate general medical care and poor self-management of overall health needs among individuals with mental illness. The program follows a whole health model that addresses all aspects of a client’s health, including mental health, substance use, and nonpsychiatric health needs. This program was funded through a Centers for Medicare and Medicaid Innovation (CMMI) Health Care Innovation Award (HCIA) from January 1, 2013, to June 30, 2015.

This study examined whether Race to Health! reduced acute care service use, office visits, and total Medicare expenditures for Medicare clients. Specifically, the study compared hospitalization rates, utilization of emergency department (ED) services, office visits, and total Medicare expenditures among Medicare clients at KMHS who participated in the program and a matched comparison group of Medicare clients at other mental health treatment facilities.

Methods

The Race to Health! Program

To implement the Race to Health! initiative, KMHS’ infrastructure and care delivery model was redesigned, and staff were trained to address a client’s whole health, including mental health, substance use, and nonpsychiatric health needs. KMHS implemented the model for all clients receiving outpatient treatment. Before receiving the HCIA funding, KMHS had reorganized its staff into multidisciplinary care teams, each consisting of a psychiatrist, a psychiatric nurse, bachelor’s-level case managers, master’s-level therapists, and co-occurring disorder specialists. KMHS used HCIA funding to add medical assistants to each care team to support collection of clients’ medical data and facilitate better coordination between KMHS staff and clients’ primary care providers. Over time, the medical assistants’ role in client care expanded, involving more client interaction, coaching clients on issues related to nonpsychiatric health needs, and assisting with leading wellness groups. The role of some existing clinical staff also expanded under Race to Health! For example, the role of nursing staff expanded from only focusing on psychiatric nursing to also serving as the authorities on general health for the entire care team. Similarly, specialists played an expanded role in the treatment of co-occurring mental and substance use disorders, training and consulting with care teams on substance use treatment in addition to providing direct services.

The HCIA funding also supported staff training in substance use and general medical conditions, implementation of chronic disease self-management and other wellness programs for clients, and delivery of psychiatric consultation to primary care providers (PCPs) in the community. KMHS staff developed their program by tailoring existing evidence-based practices, curricula, and program models. For example, KMHS hired a healthy living program developer to identify and roll out wellness programming, such as Living Well and the Stanford Chronic Disease Self-Management Program. Likewise, the agency’s internal consultant on co-occurring disorders helped identify and adapt a screening and treatment approach for substance use disorders.

In addition to funding new positions, training, and wellness programming, HCIA funding supported the expansion of KMHS’ electronic health record (EHR) system to include data on nonpsychiatric health conditions, medications, and ED visits. Care teams used these data to identify clients with health risks and engage them in wellness services. KMHS staff also used data on ED visits to identify clients who would benefit from more intensive care coordination with other social service providers and community stakeholders (for example, local police and crisis team staff who interact frequently with clients outside of a health care setting).

Data Sources

The analyses used Medicare administrative data for July 2009 through June 2015 to examine service utilization and expenditures. The data were obtained through CMS’s Virtual Research Data Center. The Medicare fee-for-service (FFS) enrollees included in this analysis represent only about 13% of all clients potentially affected by the implementation of the Race to Health! program. In addition, the evaluation team gathered qualitative information on Race to Health! through telephone interviews and site visits in the spring of 2014 and 2015, as well as through ongoing review of documents submitted to CMS by KMHS. We drew on this qualitative information to provide context to and inform our understanding of program implementation and impacts.

Institutional review board approval was not required for this study. A data use agreement with CMS governed the use of the Medicare claims data and ensured the confidentiality of KMHS clients and comparison group members.

Study Design

The regressions used a difference-in-differences framework with a comparison group to examine the impact of the program on total health care expenditures, hospitalizations, and ED visits. These three variables were chosen by CMMI as key measures for evaluating all HCIA awardees. In its award application, KMHS hypothesized that redesigning the center’s care model to include a focus on clients’ whole health—including mental health, substance use, and nonpsychiatric health conditions—would affect these outcomes. Also before the analyses under this study began, the evaluation team identified office visits as an important measure for understanding the effects of the KMHS program on use of general medical services. The office visit measure reflected the number of evaluation and management services provided to a new or established patient in a physician’s office, nursing home, or patient’s home.

The intervention entailed a transformation of outpatient service delivery at KMHS affecting all outpatient clients. Thus all individuals who used outpatient services at KMHS were deemed the intervention population for this study. The analysis population for this study was selected in a two-stage process. First, the Substance Abuse and Mental Health Services Administration’s online Behavioral Health Treatment Services Locator was used to identify 16 comparison mental health facilities in Washington State with characteristics similar to those of KMHS. All clients who had at least one outpatient mental health visit from January 2010 to June 2015 at one of the comparison facilities or at KMHS were identified and deemed the potential comparison pool or the intervention group members, respectively. Because the comparison facilities served a limited number of clients with dementia, they provided an insufficient pool of comparison clients for matching with KMHS clients with dementia. Thus the potential comparison pool was supplemented with clients with dementia from facilities in Washington that had at least 100 Medicare enrollees with outpatient claims for dementia. Clients were assigned to the first facility at which they received services during the analysis period.

Individuals whose service utilization and expenditures would not be fully represented in the Medicare administrative data used for this study were excluded from the analysis. Both the intervention and comparison pool were limited to clients who had Medicare as their primary payer, were enrolled in Medicare Parts A and B, and were not enrolled in a Medicare Advantage managed care plan (because managed care encounter data were not available). These exclusions affected 18% of Medicare enrollees.

Optimal matching was used to form the comparison group (13). Optimal matching aims to find the pairs of intervention and comparison group members with the smallest average absolute distance across all the matched pairs. The values in the distance matrix reflect the degree of similarity between the characteristics of the treatment and comparison group members. Considering that there are categorical covariates, Gower’s method was utilized to generate the distances (14). The algorithm used the distance matrix to search for the optimal matched pairs of intervention and comparison group members. The optmatch package in R was used to implement the optimal matching approach (15). The algorithm allowed each intervention group member to be matched with up to five members of the comparison pool. The characteristics in the matching algorithm were age, gender, disability status, the quarter in which treatment began at KMHS or the comparison facility, whether the beneficiary was enrolled in Medicare for a full 12 months prior to receiving mental health treatment at KMHS or a comparison facility, dual Medicare/Medicaid enrollment status, psychiatric diagnosis flags, and a hierarchical condition categories (HCC) condition indicators (16). The standardized differences between the KMHS clients and the comparison group were within 10% for all variables included in the matching analysis, indicating a strong match. [More information on the matching process and outcomes is available as an online supplement to this article.]

Prior to beginning our analysis, January 1, 2013, was identified as the implementation start date of the Race to Health! program. This date defined the beginning of the intervention period for individuals who were receiving services from KMHS prior to this date. For individuals who began receiving KMHS treatment services following this date, the intervention start date was their treatment initiation date.

Impacts on ED visits, hospitalizations, and office visits were estimated with a binomial regression model. Impacts on total expenditures were estimated by using a generalized linear model with log link function to account for skewness of the expenditure distribution. Each regression modeled the outcome as a function of age (linear and squared), gender, race-ethnicity, dual Medicare/Medicaid eligibility status, availability of 12 months of baseline data for the client, psychiatric diagnoses, time elapsed since the initial observation of an outpatient mental health service during the analysis period, whether the original Medicare entitlement resulted from disability, and HCC condition indicators (16). Regression coefficients and confidence intervals were estimated in Stata 14 by using nonparametric bootstrap methods. R was used to adjust the confidence intervals to account for multiple testing with the generalized Tukey method.

Results

Demographic and Diagnostic Characteristics

There were no statistically significant differences in the demographic characteristics of the intervention (N=846) and comparison groups (N=2,643) (Table 1), nor in their diagnoses prior to the start of the program. Among the treatment population, 40% (N=338) of the final analytic sample were over 64, and 55% (N=469) were female. Three-quarters (N=633) were enrolled in both Medicare and Medicaid, and 69% (N=580) were eligible for Medicare because of disability. Schizophrenia was the most common psychiatric diagnosis (N=228, 27%).

Regression Results

The regression analyses indicated that Race to Health! significantly reduced overall Medicare expenditures, hospitalizations, ED visits, and office visits for KMHS clients relative to the comparison group (Table 2). During the first two and one-half years of program implementation, Medicare expenditures decreased on average by $266 per enrolled beneficiary per month for the intervention group versus the comparison group (p<.01). There were .02 fewer hospitalizations (p<.01), .03 fewer ED visits (p<.01), and .13 fewer office visits (p=.04) per month of enrollment among KMHS clients relative to the comparison group. For the two-and-a-half-year program period, these rates translated into about one less hospitalization for every two clients served, five fewer ED visits for every six clients served, and four fewer office visits for every one client served. The direction and significance of the impacts were robust when subjected to alternative specifications.

To understand the data that underlay the results on the program’s impact, mean expenditures for the comparison and intervention groups during the baseline and intervention periods were examined in six-month intervals (Figure 1). At each interval in the intervention period, the difference observed between mean expenditures for the comparison and intervention groups was compared with the average difference in expenditures for these groups in the baseline period. There was no significant difference between the means for the two groups in the first two six-month periods of the intervention; however mean expenditures were significantly lower for the intervention group than for the comparison group during the third through fifth six-month intervention periods. Thus the program did not immediately demonstrate significant savings.

Discussion

These analyses suggest that Race to Health! reduced Medicare expenditures, office visits, ED visits, and hospitalization rates compared with similar Medicare clients at other community mental health centers. Several key features of Race to Health! may have contributed to clients’ service utilization and expenditure reductions. Staff who provided treatment for mental and substance use disorders reported that their improved awareness of clients’ general medical needs, gained through training and the availability of data on overall health, enhanced their ability to discuss these needs with clients, work with PCPs on their clients’ behalf, and help connect clients to necessary medical care. An important component of this process may have been increased information on medications prescribed by the clients’ PCPs, which helped the agency’s psychiatrists make more informed decisions about prescribing psychiatric medications to avoid adverse reactions. In addition, staff reported that wellness activities, including health education and groups supporting chronic disease self-management, helped some clients adopt healthier behaviors, such as exercising or quitting smoking, that may ultimately result in better health.

The existing literature on similar programs is limited. Our results parallel those of Krupski et al. (12), who analyzed the impact of implementing an integrated care model at two mental health facilities. Krupski et al. (12) found that the facility with a more established integration program had significant reductions in hospital costs; the facility with less experience did not. Thus facilities implementing similar programs should be cognizant of the time needed to train staff and collaborating primary care and community health providers and for these individuals to increase their awareness of clients’ overall health needs and rethink their traditional roles and care approach. Programs are unlikely to see substantial impacts until providers have gained experience with the new care model.

Despite substantial preparation and planning, the Race to Health! program was still evolving two years after implementation. Few KMHS staff members had been exposed to integrated and coordinated care approaches prior to program implementation. Thus KMHS had to build a foundation for the program by providing training in health needs, support and guidance from program leadership, and time for staff to apply their training and gain experience in their daily work. For example, initially, program leaders and frontline staff were challenged to understand and effectively use clients’ general medical data; over time, they completed training on clients’ overall health needs and initiated protocols and criteria to use data to inform client interactions and treatment decision making. Given the gradual evolution of program implementation, it is not surprising that our findings suggest the program’s impacts on overall health expenditures were not significant until the second program year.

Despite the promising findings, these analyses had several limitations. Because of data availability, this study was limited to Medicare FFS enrollees—about 13% of all clients potentially affected by the implementation of the Race to Health! program. Individuals with other insurance types are likely to have different health care needs relative to Medicare FFS beneficiaries (17), and therefore the program may have had different effects on these beneficiaries compared with the analysis population. On average, KMHS clients participated for 23 months of the 30-month intervention period; some participants had shorter enrollment lengths. We did not assess impact by length of participation. Out-of-pocket expenditures and services not covered by Medicare may have been affected by the program but were not addressed in this study. Overall, our findings may not be generalizable to all KMHS clients and services. Metrics were limited to outcomes that were measurable in claims data because no data on health status or functioning were available for the comparison group. The study analyzed the Race to Health! program as a whole, and thus the findings did not identify whether some components of the program were more effective than others. Nonetheless, this analysis provides evidence that whole health care models can affect expenditures and service utilization, which has not been well understood in previous studies. Future research should examine utilization and costs of these models along with general medical and behavioral health outcomes.

Conclusions

Implementation of a whole health care model in a community mental health center reduced hospitalizations, ED utilization, office visits, and total Medicare expenditures for a Medicare FFS population. These results may be due in part to staff training in general medical conditions and substance use disorders and the availability of general health data, which enhanced the staff’s ability to address clients’ overall medical needs. Our findings suggest that the program’s impact on Medicare expenditures were not significant until the second program year. This lag may be attributed to the substantial transformation and time needed for staff to adapt to the program’s expectations.