Payment Reform Needed to Address Health Disparities of Undiagnosed Diabetic Retinopathy in the City of Chicago.

INTRODUCTION: The Affordable Care Act (ACA) has expanded health coverage for thousands of Illinois residents. Expanded coverage, however, does not guarantee appropriate health care. Diabetes and its ocular complications serve as an example of how providers in underserved urban areas may not be able to keep up with new demand for labor- and technology-intensive health care unless changes in reimbursement policies are instituted. METHODS: A retrospective cohort study was conducted using medical encounter information from the Chicago HealthLNK Data Repository (HDR), an assembly of non-duplicated and de-identified patient medical records. We used a method of estimating the geographic distribution of undiagnosed diabetic retinopathy in the city of Chicago to illustrate the magnitude of potentially preventable eye disease. All rates were calculated for all ZIP Codes within Chicago (Cook County), and statistical differences between observed and geographically adjusted expected rates (p < 0.10, p < 0.05, p < 0.01) were highlighted as underserved areas. RESULTS: This analysis included 150,661 patients with diabetes identified from a total of nearly two million patients in Chicago. High rates of undetected diabetic retinopathy were found in low-income and minority areas. Within these areas, 37% of the identified diabetics were uninsured, with rates ranging widely from 20% to 68.6%. Among those with insurance, 32.8% were covered by Medicare and only 10% by Medicaid. Most patients with untreated diabetic retinopathy were found to reside in areas where primary health care is provided through Federally Qualified Health Centers. CONCLUSIONS: With 150,661 diabetics identified in the city of Chicago, and this number continuing to rise each year, a manpower approach with ophthalmologist screening for diabetic retinopathy is not realistic. The ability to identify the growing number of diabetic patients with retinopathy in low-income areas will likely require the adoption of cost-effective screening technologies that are currently not funded by Medicare and Medicaid.

An Evaluation of Recurrent Diabetic Ketoacidosis, Fragmentation of Care, and Mortality Across Chicago, Illinois.

OBJECTIVE: A portion of patients with diabetes are repeatedly hospitalized for diabetic ketoacidosis (DKA), termed recurrent DKA, which is associated with poorer clinical outcomes. This study evaluated recurrent DKA, fragmentation of care, and mortality throughout six institutions in the Chicago area. RESEARCH DESIGN AND METHODS: A deidentified Health Insurance Portability and Accountability Act-compliant data set from six institutions (HealthLNK) was used to identify 3,615 patients with DKA (ICD-9 250.1x) from 2006 to 2012, representing 5,591 inpatient admissions for DKA. Demographic and clinical data were queried. Recurrence was defined as more than one DKA episode, and fragmentation of health care was defined as admission at more than one site. RESULTS: Of the 3,615 patients, 780 (21.6%) had recurrent DKA. Patients with four or more DKAs (n = 211) represented 5.8% of the total DKA group but accounted for 26.3% (n = 1,470) of the encounters. Of the 780 recurrent patients, 125 (16%) were hospitalized at more than one hospital. These patients were more likely to recur (odds ratio [OR] 2.96; 95% CI 1.99, 4.39; P < 0.0001) and had an average of 1.88-times the encounters than nonfragmented patients. Although only 13.6% of patients died of any cause during the study period, odds of death increased with age (OR 1.06; 95% CI 1.05, 1.07; P < 0.001) and number of DKA encounters (OR 1.28; 95% CI 1.04, 1.58; P = 0.02) after adjustment for age, sex, insurance, race, fragmentation, and DKA visit count. This study was limited by lack of medical record-level data, including comorbidities without ICD-9 codes. CONCLUSIONS: Recurrent DKA was common and associated with increased fragmentation of health care and increased mortality. Further research is needed on potential interventions in this unique population.

Using Monte Carlo/Gaussian Based Small Area Estimates to Predict Where Medicaid Patients Reside.

Electronic Health Records (EHR) are rapidly becoming accepted as tools for planning and population health1,2. With the national dialogue around Medicaid expansion12, the role of EHR data has become even more important. For their potential to be fully realized and contribute to these discussions, techniques for creating accurate small area estimates is vital. As such, we examined the efficacy of developing small area estimates for Medicaid patients in two locations, Albuquerque and Chicago, by using a Monte Carlo/Gaussian technique that has worked in accurately locating registered voters in North Carolina11. The Albuquerque data, which includes patient address, will first be used to assess the accuracy of the methodology. Subsequently, it will be combined with the EHR data from Chicago to develop a regression that predicts Medicaid patients by US Block Group. We seek to create a tool that is effective in translating EHR data's potential for population health studies.