Consumer Governance May Harm Health Center Financial Performance

Abstract

Introduction

Federally Qualified Health Centers (FQHCs), which must be governed by a patient majority, have historically struggled to remain financially viable while caring for a disproportionately low-income and uninsured population. Consumer governance is credited with making FQHCs responsive to community needs, but to the extent that patient trustees resemble the typical low-income FQHC patient, patient trustees might lack the capacity to govern, harming financial performance as a result. Thus, this study sought to empirically evaluate the relationship between FQHC board composition and financial performance.

Methods

Using data from years 2002–2007 of the Uniform Data System (UDS) and the Area Resource File (ARF), and years 2003–2006 of FQHC grant applications, FQHC operating margin was modeled as a function of board and executive committee composition, the interaction between them, general time trends, other FQHC and county-level factors, and FQHC-level fixed effects. Trustees were classified as representative (i.e., low-income) consumers, non-representative (i.e., high-income) consumers, and non-consumers on the basis of their self-reported patient status and occupation.

Results

Each ten percentage point increase in the proportion of representative consumers on the board is associated with a 1.7 percentage point decrease in operating margin. This effect becomes insignificant if any consumers serve on the executive committee. There is no significant relationship between the proportion of non-representative consumers and operating margin.

Conclusions

If consumers are given leadership roles on the board, consumer governance does not harm financial performance and may be beneficial enough in other respects to justify its being required as a condition of federal FQHC funding. Without such strengthening of the provision, consumer governance appears to harm financial performance and it is unclear from this study whether it offers other benefits that are significant enough to justify this financial risk.

Introduction

Federally Qualified Health Centers (FQHCs) are a critical source of primary care for uninsured and low-income persons in both urban and rural areas. In 2010, approximately 19.5 million patients were treated at one of 8,147 delivery sites operated by 1,124 FQHC grantees.¹ By law, FQHCs must serve all without regard for their ability to pay. They also provide a variety of non-clinical enabling services which, though vital to increasing access to care, are seldom reimbursed. While FQHCs receive federal grants and enhanced Medicaid and Medicare payments, these funds often fail to fully offset the costs of the uncompensated care FQHCs provide.²

Consequently, many FQHCs are struggling financially, with “more than half of all [FQHCs reporting] operating deficits in 1997, 1998, and 1999.”³ According to the Government Accountability Office, 40 percent of centers are generally successful, 50 percent are “viable but…experiencing some operational problems” and the organizational viability of the other 10 percent is in question as they contend with financial struggles.⁴

FQHCs must also have a consumer majority governing board. This means that at least 51% of the trustees must be patients of the center, with at least one visit in the past two years. The board selects which services the CHC will provide, sets the hours of operation, approves the CHC’s budget, hires and oversees the executive director, and sets general CHC policies.⁵ While this requirement has been credited with making FQHCs more responsive to the needs of the communities and patients they serve,^6,7 no consensus regarding the value of consumer governance has been reached. For all of the examples of boards heading up successful centers, there are anecdotal cases of boards that have executed their fiduciary duty poorly, allowing centers to go deeply into debt, risking closure.⁸

Prior studies suggest that consumers lack professional skills relevant to governance,^9,10 may make programs less efficient,¹¹ and may ultimately be ineffective as trustees.^12–15 Given that most FQHC patients are low-income, uninsured, and likely to be poorly educated, consumer trustees may indeed lack the expertise needed to govern effectively, and might contribute to making some FQHCs operate inefficiently.

While the mission of caring for the underserved is a critical component of the FQHC model, it is equally important for FQHCs to remain solvent and competitive in today’s complex health care system. Thus, this study sought to empirically evaluate the relationship between FQHC board composition and financial performance.

Methods

The data for this study come from years 2002–2007 of the Uniform Data System (UDS) and the Area Resource File (ARF), and years 2003–2006 of FQHC grant applications. Both the UDS and the ARF are compiled annually by the Health Resources and Services Administration. The UDS contains data on FQHC patient demographics and health status, staffing, scope and volume of services, number of delivery sites, caseload, and finances. The ARF contains data on community demographics, health care workforce, and infrastructure. These datasets were merged into a single analytic file using UDS identifiers and Federal Information Processing Standard codes.

As shown in Table 1, the total number of FQHCs in operation increased in each year of the study period, from 843 grantees in operation in 2002 to 1,067 grantees in operation in 2007. Together, this represents 5,668 potential FQHC-year observations, with most FQHCs being observed repeatedly across years. Using established criteria, the analysis was limited to fully operational federally-funded FQHCs by excluding centers without at least one full-time medical provider, at least one full-time administrative staff person, and fewer than 5,000 annual encounters.¹⁶ This resulted in the exclusion of 173 FQHCs. The 164 FQHCs located in the U.S. Territories and Commonwealths were also excluded. FQHCs whose funding comes solely from non-CHC sources (e.g., migrant health centers, health care for the homeless, etc.) are subject to a waiver of the consumer governance requirement and 592 such FQHCs were excluded from the study.¹⁷ Another 21 FQHCs were excluded because they reported receiving no federal funding. Two health centers reporting negative total costs were also dropped from the sample, because no fully operational center could accurately report negative total costs. This left a sample of 4,716 FQHC-years for FQHC-level analyses representing 907 unique health centers. However, as shown in Table 1, grant application data was only available for 71.4% of these FQHCs, further limiting the sample.

Table 1.

Annual Number of FQHCs in Operation, 2002 – 2007

Year	Total Number of FQHC Grantees	Number Excluded	Total FQHC Sample	Total Number of Grant Applications
2002	843	156	687	Not Requested
2003	890	154	736	397 (54%)
2004	914	146	768	297 (39%)
2005	952	155	797	767 (96%)
2006	1,002	160	842	784 (93%)
2007	1,067	181	886	Not Requested
Total	5,668	952	4,716	2,245

Using UDS data available for all FQHCs to predict the likelihood of an FQHC missing board data, indicated that the sample was nationally representative of location, caseload, patient demographics, acuity, and payer mix, but that FQHCs with missing data are likely to have a higher operating margin. Specifically, each ten percentage point increase in operating margin is associated with a 1.5 percentage point increase in the probability of having missing data. While statistically significant, the practical significance of this difference is small, as a very large change in operating margin is required to generate a small change in the probability of missing data.

Using these data, operating margin was modeled as a function of board composition, executive committee composition, the interaction between them, general time trends, and other FQHC and county-level factors in an ordinary least squares regression model with FQHC-level fixed effects. Because a delay between the composition of the board at any given time and the appearance of measurable outcomes resulting from the board’s decision-making is to be expected, the board composition variables are lagged by one year.

Operating margin was constructed by dividing total revenue minus total costs by total revenue. This measure is frequently used as an indicator of an organization’s financial health^1,18 and may be reduced by a focus on uncompensated care, enabling services, and other services patients need, but which are not well reimbursed.^19,20

Board composition is defined categorically as the percentage of trustees at an FQHC comprised of representative consumers (patients whose income is typical of FQHC patients), non-representative consumers (patients whose income is higher than most FQHC patients), and non-consumers. The methods for classifying individual trustees are based on their self-reported patient status in conjunction with the average annual income for their self-reported occupation and have been previously described.²¹

The model controls for a variety of other factors at both the county and FQHC level. County-level factors include: a binary indicator of metropolitan area, which has been both positively¹⁶ and negatively²² associated with financial performance; the per capita number of active non-federal office-based physicians, which has been negatively associated with operating margin;²³ the number of short-term general hospitals and the number of FQHCs, which may drive demand and need for care as well as represent competition for the FQHC; and several measures of county demographics (% male, % non-white, % Hispanic) and socioeconomic status (per capita income, % uninsured, % unemployed), which have been negatively associated with FQHC financial performance.²⁰

FQHC level factors include caseload, which has been positively associated with financial performance;¹⁶ aggregate case-mix by age, gender, % non-white, and income (relative to the poverty level), which are likely to have a direct effect on organizational outcomes; a measure of chronic disease burden (% of encounters for diabetes, asthma, and/or hypertension), which has been negatively associated with financial performance;¹⁶ the proportion of an FQHC’s caseload by insurance status, which has been both positively^16,18 and negatively associated with financial performance;²⁴ board size, which has been negatively associated with consumer influence;²⁵ the number of delivery sites an FQHC operates, which may have implications for organizational outcomes;²⁶ the number of full-time equivalent staff, which has been negatively associated with operating margin;²⁴ and the number of physicians as a percentage of total staff, which has been positively associated with financial performance.¹⁶ A binary variable is included to indicate the presence of at least one physician on the board, because boards with a physician presence may operate differently than boards without physicians.

The composition of the board’s executive committee (chair, vice chair, secretary and treasurer) was modeled using two ordinal variables to count the number of representative and non-representative consumers on the executive committee. These variables were interacted with the board composition variables to assess the moderating role of the executive committee on the relationship between board composition and operating margin.

Given available data for each FQHC grantee over multiple years, all of the characteristics of an FQHC that do not change over time are controlled for by including fixed effects dummy variables at the FQHC level. Such characteristics are strong predictors of FQHC outcomes.²⁴ After running the fixed effects model, an F-test (F(816, 1354)=3.50, p<0.0001) indicated that the fixed effects variables were jointly significant, meaning that fixed effects is preferred over ordinary least squares. A Hausman test was also performed, which confirmed that the fixed effects model is preferred over random effects (Chi2(42)=64.00, p=0.0159).

Heteroskedasticity and autocorrelation were identified with a White test (Chi2(45)=90.44, p=0.00007) and a Wooldridge test for serial correlation in panel data (F(1, 272)=10.954, p=0.0011), respectively.^27,28 Both are controlled for using robust clustered standard errors at the FQHC level.

To investigate the alternative that organizational performance may determine board composition,^29–31 an auxiliary regression was estimated in which board composition in year two is modeled as a function of organizational outcomes in year one.^30–32 The results strongly suggest that organizational outcomes in one time period do not predict board composition one year later.

Results

The descriptive statistics for the sample appear in Table 2. During the study period, an average FQHC with a staff of just over 100 employees working at one of six delivery sites, saw almost 16,000 patients and nearly 62,000 encounters annually. Of these, 70% were either uninsured or enrolled in Medicaid, almost half (48%) had asthma, diabetes, or hypertension and nearly half (49%) had incomes below poverty. From these data, it appears that there is almost no change in the average operating margin over the study period. Across all years, values of this variable range from −140.2% to 56.7% with a mean of −4.5%. An FQHC that “breaks even” would have a value of zero, while positive values indicate revenues in excess of costs, and negative values indicate costs in excess of revenues.

Table 2.

Sample Specific Mean Descriptive Statistics for Select Variables

Variable	2003	2004	2005	2006	Overall
Operating Margin (%)	−3.96	−4.93	−4.43	−4.58	−4.47
Delivery Sites per FQHC	5.89	4.79	5.77	5.98	5.73
Unique Patients	16,707	12,590	16,258	16,214	15,837
Unique Encounters	65,234	49,150	63,472	63,487	61,894
Total FTEs	106.42	80.27	103.88	105.74	101.85
Total FQHC Grantees in County	6.72	6.85	8.25	8.81	7.99
Board Size	12.60	12.40	12.57	12.36	12.48
% Representative Consumers on Board	27.77	27.46	26.66	25.42	26.53
% Non-Representative Consumers on Board	40.80	40.98	42.73	43.94	42.58
% of Boards with at least one Physician	33.50	30.30	32.59	30.48	31.71
Representative Consumers on Executive. Committee	0.74	0.75	0.74	0.74	0.74
Non-Representative Consumers on Executive Committee	1.73	1.75	1.80	1.86	1.80
% of Patients ≤ 100% FPL	49.10	49.24	49.02	47.67	48.59
% of Patients 101 – 150% FPL	12.22	11.16	11.54	11.87	11.73
% of Patients 151 – 200% FPL	5.34	5.59	5.53	5.54	5.51
% of Patients ≥ 201% FPL	9.21	8.03	7.24	6.61	7.47
% of Patients FPL Unknown	24.13	25.98	26.66	28.31	26.70
% of Patients, Male	40.72	40.20	40.64	40.40	40.51
% of Patients, Non-White	54.58	55.06	56.00	55.40	55.42
% of Patients, Uninsured	37.84	38.21	39.16	39.09	38.77
% of Patients, Medicaid	31.64	33.71	31.85	31.31	31.87
% of Patients, Medicare	8.81	8.83	9.03	9.17	9.01
% of Patients, Other Public Insurance	2.20	1.69	1.80	1.75	1.84
% of Patients, Private Insurance	19.52	17.57	18.17	18.68	18.51
% of Patients, Chronic Illness	45.32	48.05	48.92	48.55	48.04
Observations (N)	397	297	767	784	2245

The results of the model to predict operating margin appear in Table 3. The proportion of representative consumers on the board is negatively associated with FQHC operating margin, while there is no significant relationship between the proportion of non-representative consumers and operating margin. Specifically, for an FQHC with no consumers—either representative or non-representative—on the executive committee, each ten percentage point increase in the proportion of representative consumers on the board is associated with a 1.7 percentage point decrease in operating margin. Given an average operating margin of approximately −4%, an effect of this size could make the difference between an FQHC surviving or closing.

Table 3.

Results of a Fixed Effect OLS Model to Predict Operating Margin

	Coefficient
FQHC-Level Design Factors
Board Composition (Lagged One Year)
% Representative Consumers	−0.169*
	(0.0765)
% Non-Representative Consumers	−0.0253
	(0.0662)
Board Size	−0.0353
	(0.158)
Physician on Board	2.151
	(1.272)
# Represent. Consumers on Exec. Cmte.	−1.780
	(2.190)
# Non-Represent. Consumers on Exec. Cmte.	−2.023
	(1.776)
(% Represent. Consumers) × (# Represent. Consumers on Exec. Cmte.)	0.0637
	(0.0366)
(% Represent. Consumers) × (# Non-Represent. Consumers on Exec. Cmte.)	0.0725*
	(0.0322)
(% Non-Represent. Consumers) × (# Represent. Consumers on Exec. Cmte.)	0.00958
	(0.0319)
(% Non-Represent. Consumers) × (# Non-Represent. Consumers on Exec. Cmte.)	0.0184
	(0.0248)
FQHC Staffing
Total FTEs	−0.0884***
	(0.0240)
Physicians as % of Staff	0.0772
	(0.295)
Funding Source
Migrant Grantee	0.928
	(4.688)
Homeless Grantee	0.628
	(1.626)
Public Housing Grantee	8.373**
	(3.010)
# Delivery Sites	0.0822
	(0.182)
FQHC-Level Context Factors
# Annual Patient Encounters	5.72e-05
	(4.71e-05)
Metro Area	4.952
	(12.18)
Patients by Age (19 – 64 Omitted)
% Age < 5	−0.188
	(0.228)
% Age 5 – 18	0.0270
	(0.172)
% Age ≥ 65	0.504
	(0.392)
Patients by Other Characteristics
% Male	−0.108
	(0.250)
% Non-White	0.0946*
	(0.0482)
% with Chronic Illness	−0.0798
	(0.0407)
Patients by Poverty Status (% Unknown Omitted)
% with Income ≤ 100% FPL	−0.0505
	(0.0329)
% with Income 101 – 150% FPL	0.0411
	(0.0753)
% with Income 151 – 200% FPL	−0.0126
	(0.118)
% with Income ≥ 201% FPL	0.0969
	(0.0684)
Patients by Insurance Status (% Private Omitted)
% Uninsured	−0.0260
	(0.120)
% Medicaid	0.142
	(0.154)
% Medicare	0.173
	(0.307)
% Other Public Insurance	0.220
	(0.283)
County-Level Context Factors
Health Care Supply
# Hospitals	1.140
	(0.928)
Physicians per capita	−1.488
	(2.143)
# FQHCs	0.0829
	(0.148)
Population Characteristics
% Male	−2.193
	(1.188)
% Non-White	0.507
	(0.545)
% Hispanic	1.746
	(0.900)
Per Capita Income	−3.53e-05
	(0.000247)
% Uninsured	2.457
	(2.641)
% Unemployed	0.415
	(0.403)
Time Trends (Year 2004 Omitted)
Year 2005	−1.672
	(1.208)
Year 2006	−0.905
	(1.266)
Year 2007	−0.151
	(1.456)
Constant	25.91
	(67.69)
Fixed-Effects	819
R2	0.052
Observations	2230

Robust standard errors in parentheses

^***p<0.001,

^**p<0.01,

^*p<0.05

The effect of the proportion of representative consumers on the board is not so straightforward, however, if there are also consumers on the executive committee. Specifically, the effect of the proportion of representative consumers on the board on operating margin depends on the number of non-representative consumers on the executive committee. The marginal effect is given by:

$$\text{Marginal}\text{Effect}=-0.169+0.0637(\#\text{Representative}\text{Consumers}\text{on}\text{Executive}\text{Committee})+0.0725(\#\text{Non-Representative}\text{Consumers}\text{on}\text{Executive}\text{Committee})$$

Therefore, at an FQHC with at least three consumers on the executive committee, regardless of whether they are representative or non-representative in nature, the marginal effect of the proportion of representative consumers on operating margin will become slightly positive. However, the results of an F-test indicate that for boards with at least one consumer—either representative or non-representative—on the executive committee, the effect of the proportion of representative consumers on operating margin is no longer significant (F(3, 818)=1.90, p = 0.128).

Additionally, three other variables were significant predictors of operating margin. These were the size of FQHC staff, whether an FQHC received public housing grant funds, and the proportion of non-white patients. In particular, each 10 additional full-time staff persons hired was associated with an 0.88 percentage point decrease in operating margin. This likely reflects the increased cost of hiring additional staff. Operating margin was 8.4 percentage points higher among FQHCs that received public housing grant funds as compared to FQHCs that received only CHC grant funds. It is not clear from these data whether this reflects an increase in revenue or a decrease in costs. Lastly, each 10 percentage point increase in the proportion of non-white patients was associated with a 0.95 percentage point increase in operating margin.

Discussion

For the last five decades, FQHCs have exemplified what it means to be core safety net providers, rising to the challenge of providing primary care to some of the most vulnerable populations in the most underserved areas of the United States, while relying on extremely limited resources. It is tempting to attribute this success to consumer governance. However, the results suggest that—absent any consumers on the executive committee—a greater proportion of representative consumers on the board is associated with poorer financial performance as measured by operating margin.

In light of this, the consumer governance requirement should either be completely eliminated or considerably strengthened. There is evidence that consumer governance can be harmful to margin, and the deciding factor appears to be whether or not a board has consumers on its executive committee. It may be that FQHCs whose boards are dominated by non-consumers are somehow less amenable to consumer involvement, and this may create a tense dynamic that makes the board and the organization less effective financially.

If efforts are made to strengthen the provision, such that consumers are given leadership roles on the board, then it seems that consumer governance does not harm financial performance and may be beneficial enough in other respects to justify its being required as a condition of federal FQHC funding. Without such strengthening of the provision, consumer governance appears to harm financial performance and it is unclear from this study whether it offers other benefits that are significant enough to justify this financial risk. At the very least, these results underscore the need for greater education and training of trustees to improve their competence with finances.

Strengthening the provision seems favorable, as the financial struggles faced by many FQHCs would be exacerbated if the consumer governance provision were eliminated and limited grant funds were spread over a greater number of organizations. Rather than reallocating current funds, serious consideration should be given to increasing the total amount of funding provided to all safety net institutions. Fortunately, recently enacted health reform legislation will expand the Medicaid program up to 138% of FPL, which would provide coverage (and reimbursement) for roughly 2 million currently uninsured FQHC patients.³³

Furthermore, the consumer governance provision is not the sole distinction between FQHCs and other safety net providers. For example, while FQHCs have a legally mandated option to treat all regardless of ability to pay, hospitals with ambulatory clinics face no such mandate, in many cases shielding them from the brunt of uncompensated care, even as they enjoy the advantage of tax-exempt non-profit status. Likewise, because of the exceedingly high number of uninsured patients they serve, FQHCs have far less of an ability to cost-shift than providers that enjoy a more diverse payer mix. In the wake of the Affordable Care Act, FQHCs will continue to play a vital and expanded role; many newly insured individuals will face non-financial barriers to access and will depend on the unique services that FQHCs provide.

Limitations

The limitations of this study must be acknowledged. First, the UDS data are self-reported and unaudited. However, no other comprehensive source of FQHC data exists. Second, financial data in the UDS are complicated by the fact that revenues are reported on a cash accounting basis while costs are reported on an accrual basis.⁴ This is likely to bias operating margins downward slightly, as costs will tend to be accurate, but revenues will not reflect pending charges not yet collected. However, there is no reason to suspect that this will differ systematically across centers.

Third, the requirement of a consumer majority limits the possibility of examining the effect of lower levels of consumer governance. As such, future studies should consider comparing FQHCs to other safety net providers that are not subject to a consumer governance requirement.

Finally, while a number of factors are controlled for at the county level using ARF data, it is important to note that the county and the community are not necessarily synonymous. For smaller FQHCs with perhaps a single delivery site, the community service area may actually be only a portion of the county. For large, multi-site FQHCs, however, the service area may span multiple counties, and even cross state lines. Consequently, some relevant county-level factors affecting delivery sites lying outside the central county may not be completely controlled for in the study. However, to the extent that those factors are time-invariant, the fixed effects models will control for them. Future studies should consider alternative ways to account for the diversity of settings in which large FQHCs with multiple delivery sites operate.

Conclusion

When FQHCs were first established in 1965 as “neighborhood health centers,” the rationale was that a confluence of poverty and racism had kept many Americans from accessing basic primary care. Ten years after their creation, the consumer governance requirement was implemented. Today, the problems of poverty and the lack of access to health care that motivated the creation of the first FQHCs remain, but the health care system has become increasingly complex, creating a challenging fiscal environment requiring fiscal expertise for effective governance.

Giving people from underserved communities a seat at the table will continue to be important as the country moves toward new models of care in an attempt to control costs, improve quality, and confront the social determinants of health. The issue is how such democratic notions are to be effectively implemented. Under the right conditions, there is no doubt that consumer trustees can have a positive impact on the organizations they govern. However, this study suggests that including consumers on the board may harm the FQHCs’ operating margins, which is not a sound strategy for long-term organizational sustainability. Consequently, FQHC governing boards must find the right balance between the valuable input of consumers and the skills and expertise of professionals.