Abstract
Background:
Evidence is limited on insured patients’ use of safety net providers as vertically integrated health systems spread throughout the U.S.
Objectives:
To examine whether market-level health system penetration is associated with (1) switches in Medicare beneficiaries’ usual source of primary care from federally qualified health centers (FQHCs) to health systems and (2) FQHCs’ overall Medicare patient and visit volume.
Research Design:
Beneficiary-level discrete-time survival analysis and market-level linear regression analysis using Medicare fee-for-service claims data from 2013-2018.
Subjects:
659,652 Medicare fee-for-service beneficiaries aged 65 and older living in one of 27,386 empirically-derived primary care markets whose usual source of care in 2013 was an FQHC or a non-FQHC independent physician organization that predominantly served low-income patients.
Measures:
Beneficiary-year measure of the probability of switching to health system-affiliated physician organizations and market-year measures of number of FQHC visits by Medicare beneficiaries, number of beneficiaries attributed to FQHCs, and FQHC Medicare market shares.
Results:
During 2013-2018, 16.5% of beneficiaries who sought care from FQHCs switched to health systems. When health system penetration increases from the 25th to 75th percentile, the probability of Medicare FQHC patient switching increases by 4.6 percentage-points, with 22 fewer Medicare FQHC visits and 4 fewer beneficiaries attributed to FQHCs per market-year. Complex patients and patients who sought care from non-FQHC, independent physician organizations exhibited higher rates of switching to health systems.
Conclusions:
Health system expansion was associated with loss of Medicare patients by FQHCs, suggesting potential negative spillovers of vertical integration on independent safety net providers.
Keywords: federally qualified health centers, vertical integration, usual source of care, primary care
Introduction
Federally qualified health centers (FQHCs) are an important source of primary care for more than 30 million people annually in the US.1 As community-based and patient-directed organizations, FQHCs deliver affordable and accessible care to individuals and families in medically underserved areas. FQHCs had an average operating margin of only 0.8%,2 with nearly a quarter of FQHCs reporting operating losses exceeding 1.8%.3 Third-party payment provides a vital source of financial stability for FQHCs,2,4 and reductions in the use of FQHCs by insured patients could threaten FQHCs’ financial viability and their ability to serve vulnerable populations.
Medicare represents an important source of FQHC patient revenue, approximately 11% at the median FQHC with centers in certain states reaching nearly 40%.3,5 Unlike uninsured individuals or Medicaid beneficiaries, Medicare beneficiaries usually have a broader choice of providers because more physicians are willing to accept Medicare.6 Many Medicare beneficiaries might prefer receiving care from FQHCs because they might be a more convenient source of high-quality care than other types of physician organizations.7,8 However, an increasingly available source of primary care for Medicare beneficiaries is vertically integrated health systems–hospitals or health systems that employ or operate physician practices. As of January 2022, 52.1% of physicians were employed by hospitals or health systems.9 The expansion of vertically integrated health systems across the U.S. may affect FQHCs’ payer mix if Medicare beneficiaries switch to health systems as their usual source of care.
Health systems may be attractive for patients who need both primary care and specialty care. FQHC patients, including Medicare beneficiaries, often experience difficulty obtaining specialty care.10–13 In contrast, health systems enable patients to obtain both primary care and specialty care from the same organization, potentially facilitating access and reducing care fragmentation.14 On the other hand, FQHC users might have better experiences at FQHCs relative to other providers. By statute, FQHCs are required to have patient representatives on their governing boards, which can improve responsiveness to patient needs.15 By providing tailored clinical services and enabling services, FQHCs might offer a better medical home for disadvantaged patients.16 Therefore, although health systems may be attractive to Medicare beneficiaries in general, FQHC patients could be less willing to change their current provider than patients of other independent physician organizations.
Although substantial research has examined the direct impact of vertical integration on providers affiliated with health systems and patient outcomes, little is known about the spillover effects on remaining independent providers including FQHCs. Using 2013–2018 Medicare fee-for-service claims data, we constructed measures of health system penetration based on empirically-defined primary care markets and assessed the relationship between health system penetration and changes in the usual source of care for Medicare fee-for-service beneficiaries overall and by patient complexity. Additionally, we assessed the probability of switching to health systems from FQHCs relative to other independent non-FQHC physician organizations. We supplement these beneficiary-level analyses with market-level analyses that examine whether health system penetration is associated with changes in FQHCs’ Medicare patient and visit volume, which allows us to assess the net patient flow between FQHCs and other providers as health systems expand within primary care markets.
Methods
Identifying FQHC users
Our beneficiary-level analyses focused on Medicare fee-for-service beneficiaries aged 65 years and older whose usual source of care was an FQHC in 2013. To identify beneficiaries’ usual source of care, we used 100% Medicare fee-for-service claims data, the Medicare enrollment database, and the Medicare Data on Provider Practice and Specialty (MD-PPAS) from 2013 to 2018. We first used MD-PPAS to assign physicians to physician organizations, identified by Taxpayer Identification Numbers (TINs). Medicare beneficiaries were then attributed to physician organizations based on the plurality of their ambulatory evaluation and management visits (E&M) to primary care physicians. Beneficiaries who did not have visits to primary care physicians were attributed based on the plurality of visits with internal medicine subspecialists (i.e., cardiology, endocrinology, gastroenterology, hematology/oncology, infectious disease, nephrology, pulmonology/critical care, rheumatology/immunology, and physical medicine/rehabilitation). If there was a tie, we then attributed the beneficiary based on spending. We identified physician organizations that were FQHCs by linking TINs to CMS Certification Numbers corresponding to FQHCs within the Medicare Provider Enrollment, Chain, and Ownership System (PECOS).
Identifying health system affiliated physician organizations
We used the PECOS and the Internal Revenue Service (IRS) Form 990 Schedule R to identify physician organization “affiliations” with health systems, which we defined as either ownership or management relationships.17 Using PECOS, physician organizations were identified as being affiliated with health systems if the system had: (1) a 5% or greater direct or indirect ownership interest or (2) operational or managerial control over the physician organization whether or not there is an ownership interest by the system. From IRS Form 990, which identifies physician organizations owned by non-profit hospitals or health systems, we linked physician organizations with hospitals based on their joint appearance on the same form. Physician organizations are listed on Form 990 when the filing organization operates the entity.
Identifying beneficiaries who use independent non-FQHC physician organizations
To explore potential differences in care-seeking behavior among FQHC users and non-FQHC users, we also included beneficiaries whose usual source of care in 2013 was an independent, non-FQHC physician organization that primarily served low-income beneficiaries, defined as a physician organization whose percentage of dual eligible beneficiaries was in the top quintile of the national distribution in 2013 (≥29.4%). We limited the sample of non-FQHC users to those who lived in ZIP codes that also included FQHC users, to enhance the comparability of the two groups. We excluded a small percentage of FQHC users and non-FQHC users who made “temporary” switches to an independent physician organization after an initial switch to a health system. In a sensitivity analysis, we included these beneficiaries to test whether our results were robust to temporary switches (i.e., switched out of health systems after the initial switch). Exhibit A1 in the Supplemental Digital Content shows sample inclusion and exclusion criteria in detail.
Deriving primary care markets
We examined the impact of health system penetration within empirically defined markets. For each unique ZIP code, we defined a ZIP code-centric primary care market for its residents as the most proximate set of ZIP codes that beneficiaries traveled to receive at least 70% of their E&M visits. We limited our sample to ZIP codes that had at least one Medicare beneficiary whose usual source of care is an FQHC in the study period and further excluded ZIP code-year observations with missing covariates (Exhibit A2, Supplemental Digital Content). In sensitivity analyses, we defined primary care markets as the zip codes capturing at least 60% of E&M visits of each index ZIP code, which results in smaller markets compared to the main analysis.
Exposure Variable
For each analysis, the main exposure was a continuous measure of market-level health system penetration, defined as the proportion of practice sites in a primary care market that were associated with health system-affiliated physician organizations, excluding practice sites associated with solo practitioners or physician organizations with no primary care physicians. Practice site locations for both FQHCs and non-FQHC physician organizations in each market were identified, by TIN, using PECOS.
Outcomes
The primary outcome in the beneficiary-level analyses is a beneficiary-year-level binary indicator of a switch from the beneficiary’s usual source of care (either an FQHC or non-FQHC independent physician organization) to a health system-affiliated physician organization. The market-level analyses use four outcomes aggregated to the primary care market-year level: total number of visits to FQHCs, total number of Medicare beneficiaries who are attributed to FQHCs, percentage of beneficiary visits in the market that are to FQHCs, and percentage of Medicare beneficiaries in the market who are attributed to FQHCs. Examining market-level outcomes allowed us to account for net patient flow at FQHCs.
Statistical Analysis
The beneficiary-level analyses used discrete time survival analysis to estimate the probability that a beneficiary switches from an independent FQHC to a health system-affiliated physician organization for primary care and remains with the health system for the duration of the study period. We followed beneficiaries until their usual source of care switched to a health system-affiliated physician organization or until censoring because of disenrollment from traditional Medicare, death, or the end of the study period (2018). During the study period, 99 FQHCs in our data affiliated with health systems (e.g., academic or county-based health systems). Hence, we further excluded beneficiaries who were attributed to independent FQHCs that later affiliated with a health system during the study period in sensitivity analyses (N=13,000).
To assess heterogeneity in this relationship by patient complexity, the model included an interaction term between health system penetration and an indicator for complex patients, defined as those with HCC scores ≥2.0. To explore potential differences in switching behavior between FQHC users and non-FQHC users, we used a second model that included both FQHC and non-FQHC users and included an interaction term between health system penetration and an indicator for non-FQHC users.
All models controlled for time-varying beneficiary and market characteristics. Beneficiary characteristics included age, indicators for female gender, race/ethnicity, disability (defined according to the beneficiary’s original reason for Medicare entitlement), and dual eligibility. We used Medicare Hierarchical Condition Category (HCC) scores to measure comorbidity. We included the indicator for complex patients and the indicator for non-FQHC users at baseline. To control for market-level access to primary care providers, we included market-level number of practice sites associated with FQHCs per 1,000 beneficiaries, number of practice sites associated with primary care physician organizations, and the Euclidean distance to the nearest physician organization from the centroid of each beneficiary’s residential 9-digit ZIP code. We also included 5-digit ZIP code fixed effects and year fixed effects to account for unobserved time-invariant geographic heterogeneity and time trends that could affect a patient’s choice of health care provider. We used generalized linear models with a logit-link to estimate the association between model covariates and the probability of switching to a health system-affiliated physician organization (see Appendix A, Supplemental Digital Content).
In the market-level analyses, we used linear regressions to estimate the relationship between health system penetration and market-level outcomes, controlling for the aforementioned beneficiary characteristics (aggregated to the ZIP code level), market characteristics, 5-digit ZIP code fixed effects, and year fixed effects. By exploiting within-market variation over time, this approach allowed us to estimate how each outcome changed over time in markets with large growth in health system penetration versus markets with limited growth.
Standard errors were clustered at the 5-digit ZIP code level to account for correlation across observations within the same primary care market, as the exposure is measured at the market level.18 We used recycled predictions to calculate the predicted probability of switching to a health system-affiliated physician organization, at the 25th percentile, median, and 75th percentile of health system penetration (across all market-years) and by patient complexity among FQHC users, while holding other covariates at their observed values. We also estimated the probability of switching to a health system had these FQHC users been non-FQHC users in 2013 (i.e., the probability of switching from an independent, non-FQHC physician organization to a health system). We conducted all analyses using R, version 3.6.0.19
Results
Probability of switching to a health system
The analytic sample included 659,652 Medicare beneficiaries, of whom 287,243 used FQHCs in 2013 and 372,409 used independent, non-FQHC physician organizations that mainly served low-income patients. Among FQHC users, 47,270 (16.5%) switched to a health system-affiliated physician organization during the 5-year follow-up period. Compared with those who did not switch, FQHC users who switched to a health system were more likely to be disabled at baseline (17.3% versus 16.2%) and non-Hispanic White (73.7% versus 62.2%), but less likely to be dual eligible (35.7% versus 43.0%) (Table 1). At baseline (2013), FQHC users who eventually switched to a health system lived in markets with higher health system penetration (49.5% versus 43.5%), shorter distances to the nearest physician organization (4.5 miles versus 4.7 miles), and more physician practice sites (260 versus 279). A comparison of characteristics of FQHC users and non-users is shown in Exhibit A3, Supplemental Digital Content.
Table 1.
Baseline characteristics of Medicare beneficiaries whose usual source of care in 2013 was an FQHC
| Did not switch to health system(N=239,973) | Switched to health systema(N=47,270) | |
|---|---|---|
| Exposure variable | ||
| Health system penetration (%), Mean (SD) | 43.5 (20.6) | 49.5 (17.9) |
| Beneficiary characteristics | ||
| Disabled, % | 16.2 | 17.3 |
| Dual eligible, % | 43.0 | 35.7 |
| Female, % | 60.1 | 61.4 |
| Race/ethnicityb | ||
| White, % | 62.2 | 73.7 |
| Black, % | 14.8 | 11.0 |
| Hispanic, % | 15.8 | 8.8 |
| Asian/Pacific Islander, % | 4.4 | 4.0 |
| American Indian/Alaska Native, % | 1.1 | 1.1 |
| Other or unknown race/ethnicity, % | 1.6 | 1.3 |
| Complex patient, % | 10.6 | 12.5 |
| HCC score, Mean (SD) | 1.1 (0.9) | 1.1 (0.9) |
| Age, Mean (SD) | 73.6 (7.2) | 73.5 (7.0) |
| Market characteristics | ||
| Distance to the nearest physician organization in miles, Mean (SD) | 4.7 (7.0) | 4.5 (6.2) |
| Number of FQHC sites per 1,000 beneficiaries, Mean (SD) | 0.1 (0.2) | 0.1 (2.7) |
| Number of practice sites (in 10), Mean (SD) | 26.0 (43.5) | 27.9 (48.1) |
All characteristics of FQHC users who switched to health systems were significantly different from those of FQHC users who did not switch to health systems at 0.99 confidence level based on t-tests or chi-squared tests.
Percentages do not sum up to 100 due to rounding.
FQHC = Federally qualified health center; SD = Standard deviation; HCC = Hierarchical Condition Category.
After adjusting for all aforementioned covariates, the 5-year probability of switching from FQHCs to health system-affiliated physician organizations was 25.5% in markets with health system penetration at the 25th percentile of the national distribution, 28.0% in the median market, and 30.2% in the 75th percentile market (Figure 1). Exhibit A4, Model 1 in the Supplemental Digital Content reports full results of the regression model.
Figure 1.
Adjusted cumulative probability of Medicare beneficiary switching from FQHCs to health systems, by percentile of market-level health system penetration
Notes. Predicted cumulative probability of switching from an FQHC to a health system-affiliated physician organization, by year, among Medicare beneficiaries whose usual source of care in 2013 was an FQHC. Full regression results are shown in Exhibit A4, Supplemental Digital Content.
* Probability is significantly different from the 25th percentile at 0.95 confidence level.
FQHC = Federally qualified health center.
Figure 2 decomposes FQHC users’ adjusted probability of switching to health systems by patient complexity. Compared to low-complexity FQHC patients, high-complexity patients had a higher probability of switching to health system-affiliated physician organizations in markets with health system penetration at the 25th percentile, median, and 75th percentile. In each of these markets, switching probabilities were 25.2%, 27.6%, and 29.6%, respectively, for low-complexity patients, compared with 28.4%, 32.0%, and 35.0% for high-complexity patients (p<0.001 for all differences).
Figure 2.
Adjusted 5-year probability of Medicare beneficiary switching from FQHCs to health systems, by patient complexity and percentile of market-level health system penetration
Notes. Predicted 5-year probability of switching from an FQHC to a health system-affiliated physician organization by patient complexity, among Medicare beneficiaries whose usual source of care in 2013 was an FQHC. Full regression results are shown in Exhibit A4, Supplemental Digital Content. Within-market change in health system penetration was −3.4 percentage-points at the 25th percentile, 2.83 percentage-points at the median, and 10.0 percentage-points at the 75th percentile.
* Probability for patients with high complexity is significantly different from patients with low complexity at 0.95 confidence level.
FQHC = Federally qualified health center.
Figure 3 displays estimates of the probability of switching to a health system FQHC users and non-users. Compared from the probability of switching from FQHCs, the probability of switching from independent non-FQHC physician organizations was 1.4 percentage-points (p>0.05), 8.8 percentage-points (p<0.001), and 16.3 percentage-points (p<0.001) higher in markets with health system penetration at the 25th percentile, median, and 75th percentiles, respectively. Exhibit A4, Model 2 in the Supplemental Digital Content displays the detailed regression results.
Figure 3.
Adjusted 5-year probability of Medicare beneficiary switching from usual source of care to health systems, by type of physician organization and percentile of market-level health system penetration
Notes. Predicted 5-year probability of switching to a health system-affiliated physician organization among Medicare beneficiaries whose usual source of care in 2013 was an FQHC or an independent, non-FQHC physician organization. Full regression results are shown in Exhibit A4, Supplemental Digital Content.
* Probability for patients using non-FQHC independent physician organizations is significantly different from patients using FQHCs at 0.95 confidence level.
FQHC = Federally qualified health center.
In sensitivity analyses (Exhibit A5, Supplemental Digital Content), we obtained qualitatively similar results when including beneficiaries who had temporary switches (Model 1), using smaller primary care markets (Model 2), and excluding beneficiaries who were attributed to independent FQHCs that later affiliated with health systems (Model 3).
Change in market-level FQHC volumes and market shares
The average primary care market included in the analysis had 549 Medicare beneficiaries who made an average of 3,371 primary care visits. On average, a market had 85 Medicare FQHC visits per year, and 20 FQHC users. Expressed in terms of market shares, FQHCs in the average market captured 5.2% of all visits and were the usual source of care for 7.0% of Medicare beneficiaries (Exhibit A6, Supplemental Digital Content).
Figure 4 depicts the relationship between health system penetration and market-level outcomes. Predicted market-level Medicare FQHC visits were 112.1, 99.6, and 89.8 in markets with health system penetration at the 25th percentile, median, and 75th percentile, respectively. Predicted market-level Medicare FQHC users were 25.4, 23.2, and 21.6 in markets with health system penetration at the 25th percentile, median, and 75th percentile respectively. Exhibit A7 in the Supplemental Digital Content reports the full regression results. Specifically, every 1-percentage point-increase in health system penetration was associated with a decrease of 0.76 Medicare FQHC visits (95% CI, −1.07, −0.46) and 0.13 Medicare FQHC users (95% CI, −0.19, −0.07), with a reduction of 0.02 percentage-points in FQHCs’ market share of Medicare visits (95% CI, -0.02, -0.01) and 0.02 percentage-points in FQHC’s market share of Medicare beneficiaries (95% CI, −0.03, −0.02).
Figure 4.
Predicted market-level outcomes, by percentile of market-level health system penetration
Notes. Predicted market-level FQHC visits by Medicare beneficiaries and predicted number of Medicare beneficiaries whose usual source of care was an FQHC. Full regression results are shown in Exhibit A7, Supplemental Digital Content.
* Probability is significantly different from the 25th percentile at 0.95 confidence level.
FQHC = Federally qualified health center.
Discussion
There is little evidence on how the rapid growth in vertical integration has affected independent physicians, especially safety net providers and their patients. While Medicare serves as a significant source of revenue for FQHCs, over 16% of Medicare fee-for-service beneficiaries changed their usual source of care from FQHCs to vertically integrated health systems between 2013 and 2018. Moreover, a higher penetration of health systems in primary care markets was associated with a higher probability of switching from FQHCs to health systems. Our market-level analyses, which account for patient switching between FQHCs and health systems in both directions, indicate that FQHCs experienced net reductions in both Medicare beneficiaries as well as total visits by Medicare patients, as health system penetration increased within primary care markets.
We found that the likelihood of switching from FQHCs to health systems was greater among complex patients, the differences increased with the level of health system penetration. Complex patients tend to require intensive outpatient management from numerous providers and caregivers. These patients historically experience higher utilization and poorer outcomes, in part due to a fragmented delivery and financing system.20,21 Although FQHCs are able to provide comprehensive primary care services and, in many cases, dental care and behavioral health services, access to specialty care remains a challenge for FQHC patients.10,11 Medicare beneficiaries who visit FQHCs for primary care generally have lower utilization of specialty care compared to similar beneficiaries who seek primary care elsewhere,13 indicating access barriers. Additionally, according to a recent survey of medical directors at FQHCs in 9 states and the District of Columbia, nearly 24% of FQHCs reported difficulty obtaining new patient specialty visits for Medicare beneficiaries and 35% reported difficulty for dual eligible beneficiaries.12 Hence, perceived comprehensive and coordinated care at health systems may motivate Medicare beneficiaries to leave FQHCs, which is consistent with a prior study showing that vertical integration led to significant reductions in care fragmentation.14
Comparing FQHCs with other independent non-FQHC safety-net physician organizations that serve low-income populations, we found that FQHC patients were less likely to switch to health systems in markets with high health system penetration. On average, our data showed that FQHCs were larger than non-FQHC safety-net organizations in terms of number of beneficiaries served. Hence, FQHCs might be more able to offer a broad range of health services, extend operating hours, and provide high-value care tailored to the specific needs of local communities than small practices, engendering greater patient loyalty.8,22 Prior studies have shown that FQHCs provide more preventive services and use quality-improvement strategies more frequently than other primary care providers.7,23,24 Additionally, FQHCs could retain their patients through race, ethnicity, or language concordance between providers and patients, as well as enabling services that promote access to care and patient experiences.16 Future studies may investigate drivers of the pattern that FQHCs outperformed other providers in terms of retaining Medicare beneficiaries despite health system expansion in their markets.
Our study does not indicate whether switching to health systems led to changes in patient experience or quality of care. Prior studies have shown health system affiliation can improve care management infrastructure and use of technology among affiliated physician practices, which potentially benefits patients.25,26 However, a recent systematic review of vertical integration reported mixed findings and mostly null effects on patient-centered outcomes.27 Similarly, a study found no evidence that health systems confer quality advantages to Medicare beneficiaries with high needs.28 Future research is needed to assess the implications of switching usual source of care on clinical coordination and patient experiences.
Our findings raise potential concerns that increasing vertical integration could affect Medicare revenue of FQHCs and independent physician organizations serving low-income patients, an overlooked unintended consequence. This could raise uncertainty by FQHCs about their payer mix and revenue streams. On the one hand, if loss of Medicare patients adversely changed patient revenue, it could hurt FQHCs’ financial performance. These potential implications could be more salient for FQHCs with a significant proportion of Medicare patients (e.g., centers in rural areas or states that did not expand Medicaid under the Affordable Care Act), because they are less likely to be able to offset reductions in Medicare revenue by increasing Medicaid or commercial insurance revenue. On the other hand, for FQHCs experiencing capacity constraints, reductions in Medicare patients might free up appointments for Medicaid patients, minimizing revenue impacts and potentially expanding access to care. Hence, a comprehensive assessment of vertical integration requires not only investigating its implications on system-affiliated providers and their patients, but also accounting for any potential spillover effects on providers that remain independent. Our findings can also inform policies to support safety net providers to remain financially viable, especially those with a high proportion of Medicare revenue, in markets with high health system penetration, and caring for sicker patients.
Our study has several limitations. First, although we used survival methods that accounted for a range of time-varying beneficiary- and market-level characteristics, the associations we estimate are not causal. Second, health system penetration and FQHC penetration were measured based on counts of practice sites, and the size or market share of each site was not considered due to limited availability of practice site-level information. Finally, our results are generalizable only to low-income fee-for-service Medicare beneficiaries. The care-seeking behavior of patients with other types of insurance or without insurance could differ. Future studies might examine the impact of health system expansion on care-seeking behavior of Medicaid FQHC users and overall financial performance at FQHCs.
In conclusion, we found higher market-level health system penetration was associated with greater likelihood of Medicare beneficiaries switching their usual source of care from FQHCs to health systems, with stronger associations observed among complex patients. Even after accounting for possible in-flow of Medicare patients from health systems to FQHCs, we found that FQHCs experienced net losses in both attributed Medicare beneficiaries and visits, which may affect FQHCs’ payer mix and financial health. Our findings suggest potential spillovers of vertical integration on independent safety-net providers, which could inform policies that strengthen oversight of provider consolidations. Additionally, quantifying the revenue impact of health system expansion on the safety net could be useful when examining the magnitude of community benefits provided by tax-exempt health systems.
Supplementary Material
Acknowledgements:
This work was supported by the Agency for Healthcare Research and Quality (grant number R01 HS027782 and U19 HS024067). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality. The funding source had no involvement in study design; collection, analysis, and interpretation of data; the writing of the report; nor in the decision to submit the article for publication. Early versions of this paper were presented at the AcademyHealth Annual Research Meeting in June 2023 and June 2024.
Funding source:
This research was supported by the Agency for Healthcare Research and Quality (grant number R01 HS027782 and U19 HS024067).
Footnotes
Conflict of interest: None.
References
- 1.National Health Center Program Uniform Data System (UDS) Awardee Data. Health Resources and Services Administration Data Warehouse. Accessed March 23, 2023. https://data.hrsa.gov/tools/data-reporting/program-data/national
- 2.Luo Q, Moghtaderi A, Markus A, Dor A. Financial impacts of the Medicaid expansion on community health centers. Health Services Research. 2022;57(3):634–643. doi: 10.1111/1475-6773.13897 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Federally Qualified Health Centers Financial and Operational Performance Analysis 2019-2022. 2024. Accessed 7/21/2024. https://www.caplink.org/fqhc-trends-2024
- 4.Rosenbaum S, Shin P, Sharac J, Bedenbaugh C. Community Health Centers And Medicaid: A Deeper Dive Into FQHC Alternative Payment Reform. Health Affairs Forefront blog. 2023. [Google Scholar]
- 5.Community Health Center Revenues by Payer Source. Kaiser Family Foundation. Accessed 7/21,2024. https://www.kff.org/other/state-indicator/community-health-center-revenues-by-payer-source/
- 6.Boccuti C, Fields C, Casillas G, Hamel L, . Primary Care Physicians Accepting Medicare: A Snapshot. Kaiser Family Foundation; 2015. October 30. Accessed March 23, 2023. https://www.kff.org/medicare/issue-brief/primary-care-physicians-accepting-medicare-a-snapshot/ [Google Scholar]
- 7.Shi L, Lebrun LA, Tsai J, Zhu J. Characteristics of Ambulatory Care Patients and Services: A Comparison of Community Health Centers and Physicians’ Offices. Journal of Health Care for the Poor and Underserved. 2010;21:1169–1183. [DOI] [PubMed] [Google Scholar]
- 8.Shi L, Tsai J, Higgins PC, Lebrun LA. Racial/ethnic and socioeconomic disparities in access to care and quality of care for US health center patients compared with non-health center patients. J Ambul Care Manage. Oct-Dec 2009;32(4):342–50. doi: 10.1097/JAC.0b013e3181ba6fd8 [DOI] [PubMed] [Google Scholar]
- 9.COVID-19’s Impact on Acquisitions of Physician Practices and Physician Employment 2019-2021. 2022. https://www.physiciansadvocacyinstitute.org/Portals/0/assets/docs/PAI-Research/PAI%20Avalere%20Physician%20Employment%20Trends%20Study%202019-21%20Final.pdf
- 10.Zuckerman KE, Perrin JM, Hobrecker K, Donelan K. Barriers to Specialty Care and Specialty Referral Completion in the Community Health Center Setting. The Journal of Pediatrics. 2013;162(2):409–414.e1. doi: 10.1016/j.jpeds.2012.07.022 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Kranz A, Ryan J, Mahmud A, Setodji C, Damberg C, Timbie J. Association of Primary and Specialty Care Integration on Physician Communication and Cancer Screening in Safety-Net Clinics. Preventing Chronic Disease. 2020;17:200025. doi: 10.5888/pcd17.200025 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Timbie JW, Kranz AM, Mahmud A, Damberg CL. Specialty Care Access for Medicaid Enrollees in Expansion States. The American Journal of Managed Care. 2019;25(3):e83–e87. [PMC free article] [PubMed] [Google Scholar]
- 13.Lavelle TA, Rose AJ, Timbie JW, et al. Utilization of health care services among Medicare beneficiaries who visit federally qualified health centers. BMC Health Services Research. 2018/January/25 2018;18(1):41. doi: 10.1186/s12913-018-2847-x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Encinosa W, Dor A, Nguyen P. Does Physician-hospital vertical integration signal care-coordination? Evidence from mover-stayer analyses of commercially insured enrollees. National Bureau of Economic Research; 2021;NBER Working Paper NO. 29599. https://www.nber.org/system/files/working_papers/w29599/w29599.pdf [DOI] [PubMed] [Google Scholar]
- 15.Sharma AE, Huang B, Knox M, Willard-Grace R, Potter MB. Patient Engagement in Community Health Center Leadership: How Does it Happen? Journal of Community Health. 2018/December/01 2018;43(6):1069–1074. doi: 10.1007/s10900-018-0523-z [DOI] [PubMed] [Google Scholar]
- 16.Yue D, Pourat N, Chen X, et al. Enabling Services Improve Access To Care, Preventive Services, And Satisfaction Among Health Center Patients. Health Affairs. 2019;38(9):1468–1474. doi: 10.1377/hlthaff.2018.05228 [DOI] [PubMed] [Google Scholar]
- 17.Timbie JW, Kranz AM, DeYoreo M, et al. Racial and ethnic disparities in care for health system-affiliated physician organizations and non-affiliated physician organizations. Health Services Research. 2020;55(S3):1107–1117. doi: 10.1111/1475-6773.13581 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Abadie A, Athey S, Imbens GW, Wooldridge JM. When Should You Adjust Standard Errors for Clustering? The Quarterly Journal of Economics. 2022;138(1):1–35. doi: 10.1093/qje/qjac038 [DOI] [Google Scholar]
- 19._R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing . R Core Team. Accessed June 7, 2023. https://www.R-project.org/
- 20.Drzayich Antol D, Schwartz R, Caplan A, et al. Comparison of Health Care Utilization by Medicare Advantage and Traditional Medicare Beneficiaries With Complex Care Needs. JAMA Health Forum. 2022;3(10):e223451-e223451. doi: 10.1001/jamahealthforum.2022.3451 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Figueroa JF, Zhuo X, Jha AK. Characteristics And Spending Patterns Of Persistently High-Cost Medicare Patients. Health Affairs. 2019;38(1):107–114. doi: 10.1377/hlthaff.2018.05160 [DOI] [PubMed] [Google Scholar]
- 22.Pourat N, Chen X, Lee C, et al. HRSA-funded Health Centers Are an Important Source of Care and Reduce Unmet Needs in Primary Care Services. Med Care. Dec 2019;57(12):996–1001. doi: 10.1097/mlr.0000000000001206 [DOI] [PubMed] [Google Scholar]
- 23.Hing E, Hooker RS, Ashman JJ. Primary Health Care in Community Health Centers and Comparison with Office-Based Practice. Journal of Community Health. 2011/June/01 2011;36(3):406–413. doi: 10.1007/s10900-010-9322-x [DOI] [PubMed] [Google Scholar]
- 24.Lindner S, Solberg LI, Miller WL, et al. Does Ownership Make a Difference in Primary Care Practice? The Journal of the American Board of Family Medicine. 2019;32(3):398–407. doi: 10.3122/jabfm.2019.03.180271 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Bishop TF, Shortell SM, Ramsay PP, Copeland KR, Casalino LP. Trends in hospital ownership of physician practices and the effect on processes to improve quality. Am J Manag Care. Mar 2016;22(3):172–6. [PMC free article] [PubMed] [Google Scholar]
- 26.Johnston KJ, Wiemken TL, Hockenberry JM, Figueroa JF, Joynt Maddox KE. Association of Clinician Health System Affiliation With Outpatient Performance Ratings in the Medicare Merit-based Incentive Payment System. JAMA. 2020;324(10):984–992. doi: 10.1001/jama.2020.13136 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Machta RM, Maurer KA, Jones DJ, Furukawa MF, Rich EC. A systematic review of vertical integration and quality of care, efficiency, and patient-centered outcomes. Health Care Manage Rev. Apr/Jun 2019;44(2):159–173. doi: 10.1097/hmr.0000000000000197 [DOI] [PubMed] [Google Scholar]
- 28.Kranz AM, DeYoreo M, Eshete-Roesler B, et al. Health system affiliation of physician organizations and quality of care for Medicare beneficiaries who have high needs. Health Services Research. 2020;55(S3):1118–1128. doi: 10.1111/1475-6773.13570 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.