In our study population, those with consistent health insurance were more likely to have received diabetes preventive health care in the FQHC network, compared to the uninsured or the partially insured. These results confirm that FQHCs provide crucial health care access for vulnerable patients, and that continuous health insurance makes a difference for this population.24,25,30,31
Our analyses suggest the need to control for point-in-time health insurance status, and for fluctuations in health insurance over time, in evaluating delivery of care in FQHCs and quality-improvement interventions.
Our findings illustrate that health insurance plays a key role in optimizing care for FQHC patients, and that continuity matters. Compared to uninsured patients, those with partial coverage were worse off in receipt of HbA1c and LDL screening; those covered for three quarters had no better rates than those covered for one quarter.
The fact that uninsured patients were not far behind, and sometimes fared better than partially insured patients in receipt of services, deserves further discussion. FQHCs have been able to narrow the access gap in the delivery of most services to their uninsured patients. These are the patients who urgently need a “medical home,” a place where patients maintain ongoing relationships with their physician, and with a team of caregivers who work together to provide continuous, comprehensive, coordinated patient care.46
It is also important to remember that our uninsured subgroup is not necessarily representative of the general uninsured population, but is composed of individuals highly motivated to manage their diabetes and obtain primary care; many are uninsured only because they cannot qualify for public insurance programs. In contrast, the partially insured population is more likely to be people who qualify for full coverage, but have not been able to maintain such coverage due to instability in employment and other aspects of their lives; discontinuous insurance may thus compound their vulnerability.
Despite the unique nature of the uninsured patients in this population and the continuous access to care being provided by the FQHCs, we found no examples where the uninsured or partially insured fared better than the continuously insured. While the FQHCs’ dedication to providing care for their patients mitigated some of the negative effects of being uninsured, continuous coverage was associated with the best rates of preventive services. It was beyond the scope of these analyses to evaluate why underinsured patients were less likely to receive recommended services in FQHC settings. However, anecdotally, providers and patients report that uninsured patients at the FQHCs often decline diagnostic tests and referrals due to fears about costs. They may go to other sites – an Indian Health Service clinic, a Veterans Administration Hospital, a community health fair – seeking free testing. More often, they delay care hoping to obtain insurance coverage in the future.
In addition to contributing to the existing literature on the importance of insurance continuity for patient care and outcomes, this study highlights the central role played by FQHCs in reducing racial/ethnic disparities.47
National reports have shown that racial/ethnic minorities receive lower rates of recommended preventive care.9–16,20,36,48–50
In our comparisons, however, minorities were more likely than whites to receive recommended care. In interpreting this finding, we note that the racial/ethnic groups in our study are heterogeneous; for example, the NH white patients include many recent immigrants from Eastern Europe who have diverse cultural and linguistic backgrounds. Another consideration is that NH whites may be more likely to obtain private insurance over the course of a given year,37,40,41
and thus may receive preventive services elsewhere. So, while the “reverse disparities” described here account for age, socioeconomic status, and insurance patterns, many other life circumstances likely differ between minority and non-minority patients at these clinics and may have influenced our findings.
The significance of this study goes beyond insurance patterns. Previous work has shown the relevance of reviewing medical records from FQHCs to inform public policy discussions about how to improve healthcare delivery and outcomes for underserved patients.22,23,29–32
Our study is one of the first to demonstrate how administrative FQHC data can be used to gauge the state of healthcare delivery. The methods used here will help future studies measure the impact of interventions designed to improve care for vulnerable populations.
The National Institutes of Health has stated that translating evidence-based research into practice and policies should be a major focus of efforts to improve health care.51
Our research models how the OCHIN database – and others like it, when they become available – can be used in translational research, especially when partnered with data from programs such as the HRSA Health Disparities Collaboratives. The evolving OCHIN database is unique in design and in scale, with enormous potential to be a leader in creating information technology networks among safety net providers. This type of network not only serves clinicians and patients by improving day-to-day operations, but also provides a powerful data warehouse for studies on improving care for vulnerable populations on a much larger scale.
While the OCHIN database allows us an unprecedented view into care received by patients with partial or no insurance coverage, limitations are inherent. First, our data is limited to a finite number of FQHCs, and does not include every FQHC in the area. This lack of a population-based denominator meant it was only feasible to conduct a user-rate analysis. Although no comparable national database exists to obtain more generalizable data, further studies should be conducted in other states as databases comparable to OCHIN’s become available. We showed reasonable correlation between rates of receipt of diabetes preventive services compared to available estimates from nationally representative data.22
However, directly comparing rates of receipt of the care measures with other populations was not feasible, as previous assessments varied from ours in how receipt of care was measured and how populations were defined.22,24,29–31,36
We chose to define patients as diabetic if they had two or more visits associated with a diabetes diagnostic code. We used this method to avoid incorrectly considering patients as diabetic based on a single visit’s ‘rule-out’ diabetes diagnostic code. However, through this method we likely missed some diabetics who had only one or no visits associated with a diabetes diagnosis during the study period; therefore, our results give conservative estimates of the diabetic patient population in these clinics and their receipt of preventive care.
Third, we may have missed the occurrence of screenings if they were billed using procedure codes other than those we used. Also, some of the patients we identified may have received services elsewhere. In this initial study, we had no information about diabetes care received outside of the OCHIN FQHC network. For example, patients aged over 65 probably had low rates of influenza vaccinations because this Medicare-covered group received vaccinations outside the FQHC system. Similarly, NH American Indians may access these services through the Indian Health Service.
It is possible that patients more likely to seek care outside the OCHIN clinic network are disproportionately distributed among our three insurance groups, which would explain some of the differences we found. The OCHIN database allows us to track patients across over 100 FQHC clinic sites, and patients utilizing safety set services usually have few other options, making it unlikely that a large number of patients sought care outside of the network. Even if this group does represent a large percentage of our subjects, this finding would be significant in itself, and supports the need for policy efforts to focus on designing comprehensive medical homes. If discontinuous insurance or lack of insurance is more likely to force patients to “shop around” to find recommended services, mechanisms must be created within medical homes to minimize this fragmentation of care. Future studies should measure this important issue and its implications, and should take into account community factors such as the availability of other sources of care.
Fourth, we estimated insurance coverage based on each patient’s assigned coverage on the first day of the quarter. While this method created a categorical variable to enable simpler analyses, we were not able to account for the true fluidity of health insurance coverage. We also determined public insurance coverage based on state administrative files, but do not know whether patients themselves were aware of what coverage they were assigned, or the services to which they may have been entitled. We also estimated household income and FPL percentage, which are recorded at every visit, based on the highest income recorded in 2005; thus, results associated with these variables are conservative.
Finally, although data errors as described above may have occurred, we believe that OCHIN’s dataset far surpasses what has been previously available of a similar nature. The analyses we conducted for this study would not have been possible using older methods of data-sifting and less comprehensive datasets.
Future studies should determine more precise measurements of insurance continuity and type of coverage, control for comorbidities, and assess the impact of churning between coverage types on continuity of care. The ongoing implementation of an electronic medical record system at many OCHIN member FQHCs, linking OCHIN’s PM data with more detailed clinical data including care received outside of the FQHC system, will support future efforts to conduct this important research.
Our study supports the importance for diabetic patients from underserved communities of having both a FQHC medical home and continuous health insurance in receiving optimal chronic disease management. It also demonstrates how FQHCs can collaborate within information technology networks and effectively partner with researchers to study their own care delivery, to impact the translation of evidence into practice, and to inform policies that will make a difference to their communities.