Migration of Patients Between Five Urban Teaching Hospitals in Chicago

Galanter, William L.; Applebaum, Andrew; Boddipalli, Viveka; Kho, Abel N.; Lin, Michael; Meltzer, David O.; Roberts, A. Michelle; Trick, Bill; Walton, Surrey M.; Lambert, Bruce L.

doi:10.1007/s10916-013-9930-y

Cited by 13 publications

(15 citation statements)

References 21 publications

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“…Fourth, the generalizability of our findings may be limited given the unique geographic location of these hospitals: Manhattan is the most densely populated county in the United States. However, crossover is common in studies of other locations, including 23–31% of acute care patients in Massachusetts and Indiana, and at least 6% of inpatient admissions in five Chicago hospitals …”

Section: Discussionmentioning

confidence: 99%

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Hospital crossover increases utilization for people with epilepsy: A retrospective cohort study

et al. 2015

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Objectives “Hospital crossover” occurs when people visit multiple hospitals for care, which may cause gaps in electronic health records. Although crossover is common among people with epilepsy, the effect on subsequent use of health services is unknown. Understanding this effect will help prioritize healthcare delivery innovations targeted for this population. Methods We collected de-identified information from a health information exchange network describing 7836 people with epilepsy who visited any of seven hospitals in New York, NY from 2009–2012. Data included demographics, comorbidities, and two years of visit information from ambulatory, inpatient, emergency department (ED), and radiology settings. We performed two complementary retrospective cohort analyses, in order to (1) illustrate the effect on a carefully selected subgroup, and (2) confirm the effect across the study population. First, we performed a matched cohort analysis on 410 pairs of individuals with and without hospital crossover in the baseline year. Second, we performed a propensity score odds weighted ordinal logistic regression analysis to estimate the effect across all 7836 individuals. The outcomes were the use of six health services in the follow-up year. Results In the matched pair analysis, baseline hospital crossover increased the odds of more visits in the ED (odds ratio [95%CI] 1.42 [1.05,1.95]) and radiology settings (1.7 [1.22,2.38]). The regression analysis confirmed the ED and radiology findings, and also suggested crossover lead to more inpatient admissions (1.35 [1.11,1.63]), head CTs (1.44 [1.04,2]), and brain MRIs (2.32 [1.59,3.37]). Significance Baseline hospital crossover is an independent marker for subsequent increased health service use in multiple settings among people with epilepsy. Healthcare delivery innovations targeted for people with epilepsy who engage in hospital crossover should prioritize (1) sharing radiology images and reports (to reduce unnecessary radiology use, particularly head CTs), and (2) improving coordination of care (to reduce unnecessary ED and inpatient use).

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Section: Discussionmentioning

confidence: 99%

“…Crossover is common: statewide studies in Massachusetts and Indiana have found that 23–31% of acute care patients visit more than one acute care setting over 3–5 years . A study of five Chicago hospitals found 6% of inpatient admissions were of patients who were admitted to multiple hospitals over 2 years …”

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confidence: 99%

Hospital crossover increases utilization for people with epilepsy: A retrospective cohort study

et al. 2015

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“…11 These systems comprise approximately 42% of the total inpatient beds in Chicago. Analysts at each institution extracted data from their data warehouse based on a specified data model.…”

Section: Methodsmentioning

confidence: 99%

“…To better understand the strengths and weaknesses of electronic data research networks and traditional cohorts for the ascertainment of demographics, CVD risk factors, and CVD events, we directly compared individual-level data from HealthLNK 11 —a research database of approximately 2.95 million Chicago area residents with extracted electronic data merged from six, large health systems—to data from the Multi-Ethnic Study of Atherosclerosis (MESA), a community-based, cardiovascular disease (CVD) cohort. We hypothesized there would be a large amount of discordance for demographics, blood pressure (BP), body mass index (BMI), CVD risk factors, and CVD events between HealthLNK and MESA.…”

Section: Introductionmentioning

confidence: 99%

Validity of Cardiovascular Data From Electronic Sources

Ahmad¹,

Chan²,

Rosenman³

et al. 2017

Circulation

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Background Understanding the validity of data from electronic data research networks is critical to national research initiatives and learning healthcare systems for cardiovascular care. Our goal was to evaluate the degree of agreement of electronic data research networks compared with data collected by standardized research approaches in a cohort study. Methods We linked individual-level data from The Multi-Ethnic Study of Atherosclerosis (MESA), a community-based cohort, with HealthLNK, a 2006–2012 database of electronic health records (EHRs) from six, Chicago health systems. To evaluate the correlation and agreement of blood pressure (BP) in HealthLNK as compared with in-person MESA examinations, and BMI in HealthLNK compared with MESA, we used Pearson Correlation Coefficients and Bland-Altman plots. Using diagnoses in MESA as the criterion standard, we calculated the performance of HealthLNK for hypertension (HTN), obesity, and diabetes diagnosis using ICD-9 codes and clinical data. We also identified potential myocardial infarctions (MIs), strokes, and heart failure events in HealthLNK and compared them with adjudicated events in MESA. Results Of the 1,164 MESA participants enrolled at the Chicago Field Center, 802 (68.9%) participants had data in HealthLNK. The correlation was low for systolic BP (0.39; P<0.0001). Compared with MESA, HealthLNK overestimated systolic BP by 6.5 mmHg (95%CI: 4.2, 7.8). There was a high correlation between BMI in MESA and HealthLNK (0.94; P<0.0001). HealthLNK underestimated BMI by 0.3 kg/m2 (95%CI: −0.4, −0.1). Using ICD-9 codes and clinical data, the sensitivity and specificity of HealthLNK queries for HTN were 82.4% and 59.4%, for obesity were 73.0% and 89.8%, and for diabetes were 79.8% and 93.3%. Compared with adjudicated CVD events in MESA, the concordance rates for MI, stroke, and heart failure were, respectively, 41.7% (5/12), 61.5% (8/13), and 62.5% (10/16). Conclusions These findings illustrate the limitations and strengths of electronic data repositories compared with information collected by traditional standardized epidemiologic approaches for the ascertainment of CVD risk factors and events.

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“…In addition, all institutions have policies, procedures, and practices designed to safeguard the use of clinical information for research purposes. As the CAPriCORN CDRN evolves, we will expand development of software and processes to support production of secure hash-based deidentified clinical datasets to allow patient disambiguation across disparate healthcare sites,7 while ensuring data security for patient data in CAPriCORN.…”

Section: Security and Privacymentioning

confidence: 99%

CAPriCORN: Chicago Area Patient-Centered Outcomes Research Network

Kho

Hynes

Goel

et al. 2014

Journal of the American Medical Informatics Association

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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.

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Migration of Patients Between Five Urban Teaching Hospitals in Chicago

Cited by 13 publications

References 21 publications

Hospital crossover increases utilization for people with epilepsy: A retrospective cohort study

Hospital crossover increases utilization for people with epilepsy: A retrospective cohort study

Validity of Cardiovascular Data From Electronic Sources

CAPriCORN: Chicago Area Patient-Centered Outcomes Research Network

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