Health Resources and Services Administration-Funded Health Centers Reduce Health Care Expenditures of California Medicaid Managed Care Beneficiaries with Complex Needs

Abstract

The authors aimed to investigate potential differences between health care use and related payments for patients with complex needs and high costs in Health Resources and Services Administration-funded health centers (HCs) and with other safety net primary care providers. The authors used data from the California Health Homes Program that was designed to improve health outcomes and reduce expenditures of such Medicaid managed care beneficiaries. The authors used 2018 data prior to program implementation and conducted propensity score-matched regressions. The authors then estimated predicted rates of use across seven service categories and payment values for each category and for overall payments. The authors found that 29% of the sample were HC patients and had lower estimated average total payment values ($21,220) than group provider patients ($23,180). HC patients also had lower values for hospitalizations and long-term facility stays and higher values for primary and mental health services than all other providers. Payment differences were generally consistent with differences in predicted rates of use. These findings suggest that HC approaches to managing patient care access and integrated mental health services may explain these differences in use and payment patterns.

Introduction

Recent efforts to reduce escalating health care costs in the United States have focused on patients with complex needs and high costs, who have multiple chronic conditions and nearly four times the spending of the average adult.¹ These individuals are more frequently found to be low income, on public insurance, and without a high school diploma; high users of outpatient care, emergency department (ED), and hospitals; and have three or more chronic conditions.¹ In addition, other evidence has indicated that these individuals often have a complex profile due to the presence of mental health conditions, substance use disorders, and experiences of homelessness.²

Evidence shows that managing the care of these patients requires significant effort due to their complex needs and challenges navigating a fragmented health care delivery system.^3,4 The challenges are magnified for Medicaid beneficiaries who experience barriers to access due to lower provider participation in Medicaid, particularly among specialists.^5,6 Evidence further indicates that managing the care of such patients is significantly supported by the use of care coordinators and case managers who can negotiate across siloed medical, behavioral, and social service delivery systems, build trust and rapport with patients, and advocate on the patient’s behalf with providers.⁷

With many Medicaid beneficiaries with complex needs and high costs, safety net providers can play an important role in addressing patient needs to improve health outcomes and reduce their expenditures. Among these providers, Health Resources and Services Administration (HRSA)-funded health centers (HCs) are unique because they are required to provide comprehensive primary care services under a bundled fee-for-service (FFS) rate, often integrate mental health and substance use services within their organization, and frequently employ care managers and coordinators to manage patient needs and link them to specialty and social service providers.^6,8 Furthermore, HCs are important because they provide care to nearly 31 million Americans, 90% of whom have incomes at or below 200% of the federal poverty level, and HCs receive technical assistance and funding from HRSA to promote care integration and quality.^8,9

Although the evidence is mixed, studies indicate that HCs’ infrastructure and approach to care delivery reduce the use of high-cost services.¹⁰ A recent study compared health care use of Medicaid beneficiaries with complex needs who were patients of HCs with that of patients of other providers. The analyses showed that HC patients had higher predicted rates of primary care evaluation and management visits and lower predicted rates of specialty evaluation and management visits, ED visits, and hospitalizations than patients of group and solo practices.¹¹

The authors are not aware of any studies that compare health care expenditures of Medicaid managed care enrollees with complex needs who were patients of HCs versus other types of primary care providers (PCPs). Beneficiaries enrolled in Medicaid managed care plans do not have some of the access barriers faced by their FFS counterparts, because managed care plans have established provider networks and comply with time and distance accessibility requirements in some states.¹² In addition, managed care plans are paid on a per member per month basis designed to pass the financial risk for patient care to the plan as a cost control mechanism and are therefore motivated to control expenditures.¹³

The authors aimed to understand the potential role of HCs relative to other providers in changing patterns of care delivery and reducing health care expenditures of Medicaid managed care enrollees with complex needs. This study’s findings are relevant to Medicaid’s efforts to reduce expenditures under managed care, given that 35 states reported managed care penetration rates ranging from 75% to 100% and three others reported rates between 40% and 66% as of July 2022.¹⁴ This study’s findings are also relevant to HRSA’s efforts to expand HC infrastructure to deliver care management and coordination and integration of behavioral health and other services.

Methods

Data and sample

The authors used Medicaid enrollment and claims data for managed care beneficiaries who were eligible for California’s Health Homes Program (HHP). HHP was created under Section 2703 of the 2010 Patient Protection and Affordable Care Act, which allows states to create Medicaid “health homes” to coordinate the full range of physical health, behavioral health, and community-based long-term services and supports needed by Medicaid enrollees with medical and behavioral health conditions who are high users of EDs, hospital services, or are experiencing homelessness.¹⁵

The California Department of Health Care Services identified all Medicaid beneficiaries in California prior to the program’s start in 2018 who were likely to be eligible for HHP services (N = 773,232). The authors excluded beneficiaries who did not have at least one primary care visit during 2018 to a PCP (432,279) from the sample. The authors further restricted the sample to beneficiaries with a claim from several categories of billing PCPs (see the Supplementary Appendix for further detail), excluding claims from providers such as chiropractic, dental, behavioral health, and podiatry (25,038). The authors further restricted the sample to beneficiaries with complete data for the calculation of payments, which led to the exclusion of another 83,568 beneficiaries. The study’s final analytic sample included n = 232,347 Medicaid beneficiaries with complex needs who used services in 2018.

Dependent variables

The authors examined estimated payments overall and across various service categories, including primary care, specialty care, mental health care, substance use disorder treatment, ED visits, hospitalizations, and long-term care facility stays. The authors used estimated payment per Medicaid claim calculated under the HHP evaluation, because the charges reported in Medicaid claims by managed care plans were unreliable and inconsistent with FFS claims and Medicaid fee schedules (see the Supplementary Appendix for further detail).¹⁵ In addition, the authors examined categories of service, including the number of primary care and specialty care visits, mental health care and substance use disorder treatment services, ED visits, hospitalizations, and long-term care stays using variables constructed under the HHP evaluation.¹⁵

Independent variables

The study’s main independent variable of interest was PCP type. The authors followed a previously developed methodology to categorize providers and identify their billing and rendering national provider identification (NPI) numbers to attribute beneficiaries to each PCP category in the claims data.¹¹ The authors first classified providers as HRSA-funded HC, group practice provider, solo practice provider, or other clinic not funded by HRSA (see the Supplementary Appendix for further detail).

The authors then assigned patients to a specific PCP in each category by counting the number of visits in 2018 to the same provider. Patients who exclusively used one provider (97,277) or had more than 50% of their claims with one rendering PCP (127,388) were assigned to that PCP. The authors randomly assigned patients with an equal number of claims from more than one PCP to a PCP type (7682).

The authors included patient demographics (e.g., age, sex, race/ethnicity), health status (e.g., physical health conditions such as diabetes and mental health conditions such as depression), and risk for high spending (e.g., total estimated payment in prior year) to control for differences in underlying costs and service use of patients across PCP types unrelated to differences in patterns of care. See Supplementary Appendix for additional details on these variables.

Statistical analyses

To address potential selection bias in PCP types, the authors estimated propensity scores to be used as weights in subsequent models estimating expenditures and use by PCP type. The authors estimated propensity scores using a multinomial regression model with PCP type as the outcome and including all independent variables. Payment outcomes with zeroes were assumed to have a mixture of zero-only and nonzero distributions. Therefore, the authors used two-part models, which consisted of a logistic regression to model the probability of zero versus nonzero payment and a gamma regression with a log link to model variation in payment amounts, conditional on being nonzero. To model the number of services used per category of service, the authors used negative binomial regression. All models included all independent variables. Standard errors were adjusted for patient clustering within NPIs. The authors used the user-written program “twopm” for the two-part models¹⁶ and the glm command for all other regression models within STATA version 16 and subsequently used the margins command to calculate predicted payment values and the number of services used.

The authors then estimated differences in predicted values between HRSA-funded HCs and other providers and reported findings that were statistically significant at the 0.05 level. The authors evaluated the robustness of how the authors estimated the propensity scores using the multinomial logistic model. See the Supplementary Appendix for additional details.

Results

Table 1 shows the characteristics of the sample overall and by PCP type. A large proportion of the sample were patients of group practices (43%), followed by HCs (29%) and other non-HC providers (15%). The patients were most often ages 50–64 (47%); female (60%); Hispanic/Latino (34%); preferred English as a communication language (66%); urban residents (97%); residents of Southern California counties other than Los Angeles (28%); and had diabetes (52%) and hypertension (63%). Descriptive statistics further showed that patients of HCs were less likely than other providers’ patients to be 65 years of age and older, residents of Los Angeles County, or have chronic obstructive pulmonary disease, coronary artery disease, or high health care expenditure in the past year. However, HC patients were more likely than other providers’ patients to be male, Hispanic/Latino, experiencing homelessness, having obesity, depression, serious mental illness, or substance use disorder. Sample characteristics before and after propensity score matching by provider type are shown in Supplementary Table S1.

Table 1.

Sample Characteristics by Provider Type, 2018

	Total	HRSA-funded health centers	Group practice providers	Solo practice providers	Other clinics not funded by HRSA	P
Sample size	232,347	66,561 (29%)	99,049 (43%)	32,761 (14%)	33,976 (15%)
Patient characteristics
Age						0.000
18–34 years	13%	14%	12%	10%	15%
35–49 years	19%	20%	18%	16%	22%
50–64 years	47%	51%	44%	48%	48%
65 years and older	21%	15%	26%	25%	15%
Female (vs. male)	60%	59%	60%	59%	62%	0.000
Race						0.000
NH White	28%	28%	28%	24%	34%
Hispanic/Latino	34%	36%	33%	34%	34%
NH Black/African American	11%	11%	12%	10%	8%
NH Asian and Pacific Islander	14%	11%	15%	20%	11%
NH other race	13%	14%	13%	12%	13%
Preferred language is English (vs. not)	66%	67%	65%	59%	72%	0.000
Experiencing homelessness (vs. not)	10%	13%	9%	9%	11%	0.000
Urban (vs. rural)	97%	95%	99%	99%	91%	0.000
Region						0.000
Central	23%	21%	20%	10%	45%
Los Angeles	24%	14%	31%	35%	16%
North	25%	37%	21%	11%	30%
Other Southern California	28%	28%	29%	45%	9%
Number of California Medicaid enrolled months	11.7	11.7	11.7	11.8	11.8	0.005
Patient health status
Physical chronic conditions in 2017
Has diabetes	52%	50%	53%	55%	48%	0.000
Has COPD	13%	11%	15%	14%	13%	0.000
Has hypertension	63%	59%	65%	66%	59%	0.000
Has obesity	19%	21%	18%	15%	20%	0.000
Has asthma	19%	18%	18%	17%	21%	0.000
Has coronary artery disease	15%	11%	17%	16%	13%	0.000
Has congestive heart failure	10%	8%	12%	11%	8%	0.000
Behavioral health chronic conditions in 2017
Has depression	24%	27%	22%	21%	26%	0.000
Has anxiety	19%	22%	18%	15%	23%	0.000
Has serious mental illness	30%	33%	28%	27%	31%	0.000
Has substance use disorder	15%	21%	13%	12%	15%	0.000
Health care use in 2017
CDPS score	1.39	1.25	1.51	1.42	1.31	0.000
Total health care expenditures	$19,609	$17,625	$21,211	$20,128	$18,327	0.000

Non-Hispanic other race included race coded as American Indian/Alaska Native, other, and unreported.

CDPS, Chronic Illness and Disability Payment System for Medicaid; COPD, chronic obstructive pulmonary disease; HRSA, Health Resources and Services Administration; NH, non-Hispanic.

Table 2 shows the predicted values for payments and health care use rates for each PCP type, as well as absolute differences in outcomes between patients of HCs and each of the other provider groups. The authors found that the overall predicted payments per patient in 2018 were significantly lower by $1,960 for patients of HCs ($21,220) versus group practices ($23,180). A similar pattern was observed for ED payments. Payments for hospitalization were significantly lower by $1,530 for patients of HCs ($4,530) versus group practice ($6,060), $710 less than solo practice ($5,240), and $1,280 less than other non-HCs ($5,810). A similar pattern was observed for long-term facility payments. Payments for specialty care were significantly lower for HC patients ($1,900) than for group practices only ($2,340). However, payments were higher for primary care and mental health services for HC patients than patients of the group ($540 and $200 higher, respectively), solo ($540 and $190 higher, respectively), and other non-HC ($480 higher, only primary care) practices. Payment differences were generally consistent with differences in predicted rate of use. Unadjusted payments and use data prior to propensity score matching are shown in Supplementary Table S2. Results of complete payment models are presented in Supplementary Table S3.

Table 2.

Predicted Values and Rates for Payment and Service Utilization for High-Need High-Cost Medicaid Managed Care Beneficiaries in California, 2018

	Estimated payment				Difference by provider type
	HRSA-funded health centers	Group practice providers	Solo practice providers	Other clinics not funded by HRSA	HRSA-funded health centers vs. group practice providers	HRSA-funded health centers vs. solo practice providers	HRSA-funded health centers vs. other clinics not funded by HRSA
Sample size	66,561	99,049	32,761	33,976
Payments per member
Total	$21,220	$23,180	$21,920	$21,800	−$1960***	−$700	−$580
Emergency department	$750	$830	$780	$810	−$80***	−$30	−$60*
Hospitalization	$4530	$6060	$5240	$5810	−$1530***	−$710***	−$1280**
Primary care	$1340	$800	$800	$860	$540***	$540***	$480***
Specialty care	$1900	$2340	$1910	$1770	−$440***	−$10	$130
Prescription medications	$4550	$4390	$4320	$4390	$160	$230	$160
SUD treatment	$110	$70	$70	$100	$40***	$40***	$10
Mental health care	$880	$680	$690	$760	$200***	$190***	$120
Long-term facility stay	$380	$1970	$2460	$1430	−$1590***	−$2080***	−$1050*
Number of services per member
Emergency department	1.6	1.8	1.7	1.7	−0.15***	−0.01	−0.05
Hospitalizations	0.4	0.5	0.5	0.4	−0.11***	−0.07***	−0.06*
Primary care	7.2	6.5	6.5	6.7	0.71***	0.71***	0.45
Specialty care	4.9	6.4	5.5	5.3	−1.48***	−0.61***	−0.33
Mental health care	4.1	3.9	3.5	4.2	0.17	0.62**	−0.13
SUD treatment	5.7	6.1	6.2	7.0	−0.40	−0.51	−1.34**
Long-term facility stay	0.1	0.5	0.8	0.3	−0.40***	−0.67***	−0.18

All payment values are rounded to nearest 10th. Differences were calculated based on original values.

Statistically significant compared with HRSA-funded health centers at *P < 0.05; **P < 0.01; ***P < 0.001.

Predicted values and rates are obtained from regression models that account for demographics, health status, and past service use.

HRSA, Health Resources and Services Administration; SUD, substance use disorder.

In 2018, the overall estimated payments for beneficiaries with complex needs in the analytic sample were calculated to be $5.167 billion, generated from multiplication of the predicted values and the number of beneficiaries in the sample, as shown in Table 3. The authors further examined the residual payments for services not included in the reported categories and found that most were for durable medical equipment and supplies, dialysis and related injections, lab and imaging, and home health and community-based services. In addition, approximately 2% of the residual payments were for chiropractic and acupuncture services provided by HCs (data not shown).

Table 3.

Estimated Overall Payments and Percentage of Overall Payment per Service Category by Provider Type, 2018

	Overall payments in millions	Percentage of payments in each category of service by provider type
	Overall payments in millions	HRSA-funded health centers (%)	Group practice providers (%)	Solo practice providers (%)	Other clinics not funded by HRSA (%)
Total	$5167	27	44	14	14
Emergency department	$185	27	44	14	15
Hospitalization	$1271	24	47	14	16
Primary care	$224	40	35	12	13
Specialty care	$481	26	48	13	13
Prescription medications	$1028	29	42	14	15
SUD treatment	$20	37	35	11	17
Mental health care	$174	34	39	13	15
Long-term facility stay	$350	7	56	23	14

Overall payments are calculated by multiplying predicted values and total sample.

HRSA, Health Resources and Services Administration; SUD, substance use disorder.

The authors found that HC patients generated the largest proportion of primary care payments among provider categories. However, the proportion of payments for HC patients within all other service categories was lower than for patients of group practices. HC patients generated more payments than patients of solo practices and other non-HCs in all categories of service other than long-term facility stays.

For additional details on sensitivity analyses and findings, please see Supplementary Appendix for further details.

Discussion

The current study’s analyses demonstrated the complexity of health and social determinants of Medicaid managed care beneficiaries with complex needs in California, which corroborated existing evidence indicating a more complex profile, greater barriers to timely care, and higher health care use levels than patients of other payers.^1,17 The current study’s analyses further showed that the beneficiaries included in the sample generated an estimated $5.2 billion in payments in 2018, which is roughly 5% of total California Medicaid expenditures of over $102 billion that year.^18,19

The current study’s findings of lower payments overall in primary care, substance use disorder, and mental health care treatment than other services are in part due to lower fee schedules for these services by Medicaid compared with other insurers.²⁰ Combined with the predicted use rates, however, the current study’s findings suggest a continued misalignment in the delivery of low-cost but high-value outpatient services that may improve health outcomes for patients with complex needs and reduce the need for urgent or institutional care.²¹

The study’s findings also confirmed that the complexity of the beneficiary profile was frequently greater for patients of HCs than for other safety net providers.²² Despite this complexity, total payments for the patients of HCs in the current study’s sample were lower than for those of group practices, and payments for hospitalizations and long-term facility stays were lower than for patients of all other providers. The explanation for lower overall payments for some categories of service likely lies in differences in patterns of health care use by PCP type. For example, findings suggested that HCs likely provided more comprehensive primary and behavioral health care services on-site or within their organizations than other providers. This is likely to have been enabled by HRSA’s financial and nonfinancial support for promoting delivery of a broad range of primary care services including imaging, lab, and pharmacy; recognition of HCs as patient-centered medical homes based on compliance with principles of patient-centered and high-quality care; expansion of mental health and substance use disorder staffing; and expansion of staff to provide nonreimbursable services such as care management and coordination, transportation, translation, and linkages to social service providers to help patients obtain public assistance, housing, and food.^6,23
–25

The study’s findings further suggested that HCs’ approach to the delivery of primary care is effective in managing patients with complex needs.²⁶ This suggestion is supported by evidence that 78% of HCs are recognized as patient-centered medical homes and performed well in the management of common conditions such as diabetes, overweight status, mental health conditions, and substance use disorders, as well as enhanced services such as after-hours availability and provision of urgent appointments.^27

–30 These suggestions are further supported by the findings of other studies that indicate care management and coordination and integration of behavioral health services can reduce ED visits and hospitalizations.^31,32

These findings are limited by a lack of data on the characteristics of group and individual providers, particularly because of significant underlying variation in the structure of group practices. This category of providers included hospital-based and free-standing practices, single or multispecialty groups, and large and small groups. Large groups are more likely to have resources to employ care managers and coordinators. Multispecialty groups are better able to address specialty needs of their patients due to ease of referrals within the group.³³ The authors also do not have data on potential self-selection of group practices in Medicaid and whether participating group practices are more or less effective or efficient in delivering care and managing outcomes. Similarly, the authors lacked data on solo providers’ delivery of care management and coordination, self-selection as a Medicaid provider, and their effectiveness and efficiency. Finally, the non-HRSA-funded clinics in the current study’s data and participating in California Medicaid or elsewhere include a variety of organizations, ranging from HC look-alikes that are similar to HCs in many respects but do not get the bundled FFS payment and other nonprofit or community-based organizations that are less likely to have ready access to capital or extensive resources to deliver a broad array of services.^34,35

The current study’s estimated payments are also subject to error since lags in claims data could lead to missing claims or claims that were not fully adjudicated at the time of the HHP evaluation. Furthermore, the study’s estimated payments and rates of use were somewhat sensitive to the selected methodology for estimating the propensity scores. Nevertheless, the methods of attributing payments to provider categories were consistent and therefore comparable. The data for this study were from a unique Medicaid program in 2018 in California and may not be generalizable to all states. Furthermore, the context of care delivery and Medicaid patient characteristics may have changed. However, data from a single state are less confounded by variations in care delivery and enrolled population characteristics in other states. Similarly, California continues to focus on the provision of care coordination and support services to HNHC patients with similar characteristics, and the findings can inform current efforts.

Conclusions

This study’s findings have implications for policy, practice, and research associated with improving care and subsequently reducing expenditures for Medicaid managed care beneficiaries with complex needs in California and elsewhere. The current study confirms the importance of better alignment of care for Medicaid beneficiaries with complex needs to reduce their reliance on and use of acute services and lower the use of outpatient services. An approach to improving such alignment includes the delivery of comprehensive and integrated care by PCP types similar to HCs.

The structural advantages of HCs and the prospective payment system of reimbursement combined with the payment and use differences between HCs and other PCP types highlight the types of care management services required by other providers. However, the implementation of such approaches by group or solo practices requires further wraparound services. An example of such efforts is the provision of a new Medicaid benefit in California called enhanced care management, under which Medicaid managed care plans are responsible for contracting with community-based organizations to provide comprehensive care management and coordination for patients with complex needs. California has further added a new benefit called community supports using the same mechanism to address health-related social needs by providing housing supports and medically tailored meals.³⁶ Data on the impact of these new benefits are not yet available to assess their likely effect on California Medicaid beneficiaries. However, the current study’s findings provide insight into expected outcomes of such approaches.

The current study’s findings further highlight the value of past investments by HRSA to promote patient-centered and high-value care to improve outcomes for patients with complex needs at HCs beyond those covered by Medicaid managed care. Future efforts could include continued support of integrated delivery of mental health and substance use disorder treatments, intensive care management and coordination to ensure receipt of outpatient services not available within HC organizations, and meaningful linkages of patients to appropriate social services. Replication of this study with newer and national data is needed to assess whether HNHC patients of HCs have similar utilization and expenditure patterns everywhere and by other private sector providers of care coordination.

Footnotes

Authors’ Contributions

N.P.: Conceptualization (lead), methodology (lead), and writing—original draft (lead). W.Z.: Data curation (lead) and formal analysis (equal). L.A.H.: Project administration (lead), formal analysis (supporting), writing—original draft (supporting), and writing—review and editing (lead). J.R.: Writing—review and editing (equal). A.S.: Writing—review and editing (equal).

Disclaimer

The views expressed in this publication are solely the opinions of the authors and do not necessarily reflect the official policies of the U.S. Department of Health and Human Services or the HRSA, nor does mention of the department or agency names imply endorsement by the U.S. Government.

Author Disclosure Statement

The authors have no conflicts of interest to report.

Funding Information

This article was funded by the U.S. Department of Health and Human Services, Health Resources and Services Administration (HRSA) under contract number 75R60219D00014.

Supplementary Material

Supplementary Appendix

Supplementary Table S1

Supplementary Table S2

Supplementary Table S3

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