The Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN) represents an unprecedented collaboration across diverse healthcare institutions including private, county, and state hospitals and health systems, a consortium of Federally Qualified Health Centers, and two Department of Veterans Affairs hospitals. CAPriCORN builds on the strengths of our institutions to develop a cross-cutting infrastructure for sustainable and patient-centered comparative effectiveness research in Chicago. Unique aspects include collaboration with the University HealthSystem Consortium to aggregate data across sites, a centralized communication center to integrate patient recruitment with the data infrastructure, and a centralized institutional review board to ensure a strong and efficient human subject protection program. With coordination by the Chicago Community Trust and the Illinois Medical District Commission, CAPriCORN will model how healthcare institutions can overcome barriers of data integration, marketplace competition, and care fragmentation to develop, test, and implement strategies to improve care for diverse populations and reduce health disparities.

Objective: Recent research demonstrates an increased need to understand the contribution of social determinants of health (SDHs) in shaping an individual's health status and outcomes. We studied patients with diabetes in safety-net centers and evaluated associations of their disease complexity, demographic characteristics, comorbidities, insurance status, and primary language with their HbA1c level over time.Methods: Adult patients with diabetes with at least 3 distinct primary care visits between January 1, 2006, and December 31, 2013, were identified in the CHARN data warehouse. These patients were categorized into 4 groups: those without a diagnosis of cardiovascular disease (CVD) or depression; those with CVD but not depression; those with depression but not CVD; and those with CVD and depression. Charlson score; demographic characteristics such as age, sex, and race/ethnicity; and SDHs such as primary language and insurance status were used as predictors. The outcome measure was HbA1c. Hypothesis testing was conducted using 3-level hierarchical linear models.Results: Baseline HbA1c differed significantly across the 4 diabetes groups and by race/ethnicity. The amount of HbA1c change over time differed by insurance status. Patients who were continuously insured tended to have lower baseline HbA1c and a smaller increase. Chinese-speaking patients tended to have lower baseline HbA1c but a larger increase over time compared with English speakers. There were various unexpected associations: compared with the diabetes-only group, mean HbA1c tended to be lower among the other more complex groups at baseline; women tended to have lower measures at baseline; older age and higher Charlson scores were associated with lower HbA1c.Conclusions: There is still unexplained variability relating to both baseline HbA1c values and change over time in the model. SDHs, such as insurance status and primary language, are associated with HbA1c, and results suggest that these relationships vary with disease status among patients with diabetes in safety-net centers. It is important to recognize that there are complex relationships among demographic and SDH measures in complex patients, and there is work to be done in correctly modeling and understanding these relationships. We also recommend prioritizing the collection of SDH and enabling services data for safety-net patients that would be instrumental in conducting a more comprehensive study. 3-7 Interest in studying and describing SDHs has increased over the past few decades, 8 -10 and it is clear that SDHs are associated with suboptimal health status or poor health-related outcomes, 11 such as higher rates of mental disorders and medical conditions. 12 In a meta-analysis conducted by Lorant and colleagues, 13 people with a low socioeconomic status were 1.8 times more likely to report being depressed than were people with higher status. Lett et al 14 also found that low social support was associated with a 1.5-to 2-times increased the risk of developing coronary heart disease or ex...

Background Homelessness is associated with substantial morbidity. Data linkages between homeless and health systems are important to understand unique needs across homeless populations, identify homeless individuals not registered in homeless databases, quantify the impact of housing services on health-system use, and motivate health systems and payers to contribute to housing solutions. Methods We performed a cross-sectional survey including six health systems and two Homeless Management Information Systems (HMIS) in Cook County, Illinois. We performed privacy-preserving record linkage to identify homelessness through HMIS or ICD-10 codes captured in electronic medical records. We measured the prevalence of health conditions and health-services use across the following typologies: housing-service utilizers stratified by service provided (stable, stable plus unstable, unstable) and non-utilizers (i.e., homelessness identified through diagnosis codes—without receipt of housing services). Results Among 11,447 homeless recipients of healthcare, nearly 1 in 5 were identified by ICD10 code alone without recorded homeless services (n = 2177; 19%). Almost half received homeless services that did not include stable housing (n = 5444; 48%), followed by stable housing (n = 3017; 26%), then receipt of both stable and unstable services (n = 809; 7%). Setting stable housing recipients as the referent group, we found a stepwise increase in behavioral-health conditions from stable housing to those known as homeless solely by health systems. Compared to those in stable housing, prevalence rate ratios (PRR) for those without homeless services were as follows: depression (PRR = 2.2; 95% CI 1.9 to 2.5), anxiety (PRR = 2.5; 95% CI 2.1 to 3.0), schizophrenia (PRR = 3.3; 95% CI 2.7 to 4.0), and alcohol-use disorder (PRR = 4.4; 95% CI 3.6 to 5.3). Homeless individuals who had not received housing services relied on emergency departments for healthcare—nearly 3 of 4 visited at least one and many (24%) visited multiple. Conclusions Differences in behavioral-health conditions and health-system use across homeless typologies highlight the particularly high burden among homeless who are disconnected from homeless services. Fragmented and high use of emergency departments for care should motivate health systems and payers to promote housing solutions, especially those that incorporate substance use and mental health treatment.

IntroductionMonitoring and understanding population health requires conducting health-related surveys and surveillance. The objective of our study was to assess whether data from self-administered surveys could be collected electronically from patients in urban, primary-care, safety-net clinics and subsequently linked and compared with the same patients’ electronic health records (EHRs).MethodsData from self-administered surveys were collected electronically from a convenience sample of 527 patients at 2 Chicago health centers from September through November, 2014. Survey data were linked to EHRs.ResultsA total of 251 (47.6%) patients who completed the survey consented to having their responses linked to their EHRs. Consenting participants were older, more likely to report fair or poor health, and took longer to complete the survey than those who did not consent. For 8 of 18 categorical variables, overall percentage of agreement between survey data and EHR data exceeded 80% (sex, race/ethnicity, pneumococcal vaccination, self-reported body mass index [BMI], diabetes, high blood pressure, medication for high blood pressure, and hyperlipidemia), and of these, the level of agreement was good or excellent (κ ≥0.64) except for pneumococcal vaccination (κ = 0.40) and hyperlipidemia (κ = 0.47). Of 7 continuous variables, agreement was substantial for age and weight (concordance coefficients ≥0.95); however, with the exception of calculated survey BMI and EHR–BMI (concordance coefficient = 0.88), all other continuous variables had poor agreement.ConclusionsSelf-administered and web-based surveys can be completed in urban, primary-care, safety-net clinics and linked to EHRs. Linking survey and EHR data can enhance public health surveillance by validating self-reported data, completing gaps in patient data, and extending sample sizes obtained through current methods. This approach will require promoting and sustaining patient involvement.

Purpose This study, conducted in five safety‐net practices, including two nurse‐managed health centers (NMHCs) and three federally qualified health centers (FQHCs), examined the impact of implementing a commercial electronic health records (EHRs) system on medication safety. Data source A mixed methods approach with two sources of data were used: (a) a query of prescription records captured by the EHR retrieving co‐prescribed medications with identified drug–drug interaction (DDI) risks, and (b) semistructured interviews with clinicians and leadership about the usability and benefits of EHR‐embedded clinical decision support in the form of DDI alerts. Conclusions We found an exceptionally low rate of DDI pairs in all five practices. Only 130 “true” DDI pairs were confirmed representing 149,087 visits and 62 providers. Among the 130, the largest categories were related to antihypertensive medications, which are in fact often prescribed together. There were no significant differences between physicians and nurse practitioners on the rate of DDI pairs nor between NMHCs and FQHCs. Implications for practice Implementation of an EHR in these five safety‐net settings had a positive impact on medication safety. The issue of missing end dates is noteworthy in terms of DDIs and unnecessary alerts that could lead to alert fatigue.