Changing the research landscape: the New York City Clinical Data Research Network

Kaushal, Rainu; Hripcsak, George; Ascheim, Deborah D.; Bloom, Toby; Campion, Thomas R.; Caplan, Arthur L.; Currie, Brian P.; Check, Thomas; Deland, Emme L.; Gourevitch, Marc N.; Hart, Raffaella; Horowitz, Carol R.; Kastenbaum, Isaac; Levin, Arthur A.; Low, Alexander F. H.; Meißner, Paul; Mirhaji, Parsa; Pincus, Harold Alan; Scaglione, Charles; Shelley, Donna; Tobin, Jonathan N.

doi:10.1136/amiajnl-2014-002764

Cited by 83 publications

(68 citation statements)

References 10 publications

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“…In the future, HIE organizations may provide clinical data directly to researchers. Alternatively, HIE organizations may share data with research repositories, such as the clinical data research networks funded by the Patient Centered Outcomes Research Institute (PCORI) in the United States . Internationally, HIE projects are also underway throughout North America, Europe, Asia, and Oceania .…”

Section: Discussionmentioning

confidence: 99%

“…Alternatively, HIE organizations may share data with research repositories, such as the clinical data research networks funded by the Patient Centered Outcomes Research Institute (PCORI) in the United States. 38 Internationally, HIE projects are also underway throughout North America, Europe, Asia, and Oceania. 39 Taken together, these efforts suggest that HIE data may become increasingly available to the epilepsy research community.…”

Section: Hie As Emerging Data Sourcementioning

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%

Section: Hie As Emerging Data Sourcementioning

confidence: 99%

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

et al. 2015

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“…5-9 There are, however, few reports of the impact of these registries on timely enrollment and study completion, and so the cost/benefit is uncertain. Other approaches to speedy recruitment include aggregation of data from electronic health records based on consent waivers or opt-out mechanisms; two major initiatives in 2014 are the PCOR-NET [14][15][16] and CTSA-based data network. 17 It remains to be established which approach(es) are most effective.…”

Section: Discussionmentioning

confidence: 99%

A Data‐Rich Recruitment Core to Support Translational Clinical Research

Kost

Corregano

Rainer

et al. 2014

Clinical Translational Sci

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Background Under-enrollment of clinical studies wastes resources and delays assessment of research discoveries. We describe the organization and impact of a centralized recruitment core delivering comprehensive recruitment support to investigators. Methods The Rockefeller University Center for Clinical and Translational Science supports a centralized recruitment core, call center, Research Volunteer Repository, data infrastructure, and staff who provide expert recruitment services to investigators. During protocol development, consultations aim to optimize enrollment feasibility, develop recruitment strategy, budget, and advertising. Services during study conduct include advertising placement, repository queries, call management, prescreening, referral, and visit scheduling. Utilization and recruitment outcomes are tracked using dedicated software. Results For protocols receiving recruitment services during 2009–2013: median time from initiation of recruitment to the first enrolled participant was 10 days; of 4,047 first-time callers to the call center, 92% (n=3722) enrolled in the Research Volunteer Repository, with 99% retention; 23% of Repository enrollees subsequently enrolled in ≥1 research studies, with 89% retention. Of volunteers referred by repository queries, 49% (280/537) enrolled into the study, with 92% retained. Conclusions Provision of robust recruitment infrastructure including expertise, a volunteer repository, data capture and real-time analysis accelerates protocol accrual. Application of recruitment science improves the quality of clinical investigation.

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“…Its collaborative governance model leverages members’ expertise and experience to inform patient-centered, comparative effectiveness research. 20 The NYC-CDRN focuses on socioeconomically, racially and ethnically diverse patient populations and clinicians in a geographically defined urban area. Like other members of Big Data initiatives, our seasoned researchers, clinicians, advocates, technology, privacy and security experts rapidly recognized the lack of an established framework for patient and clinician engagement in Big Data research.…”

Section: Introductionmentioning

confidence: 99%

A Tale of 2 Constituencies

et al. 2018

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Background:Patient and clinician stakeholders are inadequately engaged in key aspects of research, particularly regarding use of Big Data to study and improve patient-centered outcomes. Little is known about the attitudes, interests, and concerns of stakeholders regarding such data.Research Design:The New York City Clinical Data Research Network (NYC-CDRN), a collaboration of research, clinical, and community leaders built a deidentified dataset containing electronic health records from millions of New Yorkers. Guided by a patient-clinician advisory board, we developed a question guide to explore patient and clinician experiences and ideas about research using large datasets. Trained facilitators led discussions during preexisting patient, community, and clinician group meetings. The research team coded meeting notes and identified themes.Results:Fully 272 individuals participated in 19 listening sessions (139 patients/advocates, 133 clinicians) at 6 medical centers with diverse NYC communities: 76% were female and 63% were nonwhite. Clinicians and patients agreed on all major themes including the central role of clinicians in introducing patients to research and the need for public campaigns to inform stakeholders about Big Data. Stakeholders were interested in using granular data to compare the care and clinical outcomes of their neighborhoods with others across NYC, but were also concerned that data could not truly be deidentified.Conclusions:Clinicians and patients agree on potential benefits of stakeholder-engaged Big Data research and provided suggestions for further research and building stakeholder research capacity. This evaluation demonstrated the potential of brief meetings with existing patient and clinical groups to explore barriers and facilitators to patient and clinician engagement.

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Changing the research landscape: the New York City Clinical Data Research Network

Cited by 83 publications

References 10 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

A Data‐Rich Recruitment Core to Support Translational Clinical Research

A Tale of 2 Constituencies

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