Data interchange using i2b2

Klann, Jeffrey G.; Abend, Aaron; Raghavan, V.; Mandl, Kenneth D.; Murphy, Shawn N.

doi:10.1093/jamia/ocv188

Cited by 79 publications

(67 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the purposes of this study, only patients recruited to the PPHNet registry at Boston Children’s Hospital were included. The second data source was the local Informatics for Integrating Biology and the Bedside (i2b2) 14 data warehouse at Boston Children’s Hospital, which is part of the Scalable Collaborative for a Learning Health System (SCILHS) 15 network. i2b2 contains clinical data such as medication prescriptions, billing data including diagnostic codes, demographic data, and clinical notes.…”

Section: Methodsmentioning

confidence: 99%

A Computable Phenotype Improves Cohort Ascertainment in a Pediatric Pulmonary Hypertension Registry

Geva

Gronsbell

Cai

et al. 2017

The Journal of Pediatrics

View full text Add to dashboard Cite

Objectives To compare registry and EHR data mining approaches for cohort ascertainment in patients with pediatric pulmonary hypertension (PH) in an effort to overcome some of the limitations of registry enrollment alone in identifying patients with disease phenotypes. Study design This study was a single-center retrospective analysis of EHR and registry data at Boston Children’s Hospital. The local Informatics for Integrating Biology and the Bedside (i2b2) data warehouse was queried for billing codes, prescriptions, and narrative data related to pediatric PH. Computable phenotype algorithms were developed by fitting penalized logistic regression models to a physician-annotated training set. Algorithms were applied to a candidate patient cohort and performance was evaluated using a separate set of 136 records and 179 registry patients. We compared clinical and demographic characteristics of patients identified by computable phenotype and the registry. Results The computable phenotype had an area under the ROC curve of 90% (95% CI 85% – 95%), positive predictive value of 85% (95% CI 77% – 93%), and identified 413 patients (an additional 231%) with pediatric PH not enrolled in the registry. Patients identified by the computable phenotype were clinically distinct from registry patients, with greater prevalence of diagnoses related to perinatal distress and left heart disease. Conclusions Mining of EHRs using computable phenotypes identified a large cohort of patients not recruited using a classic registry. Fusion of EHR and registry data can improve cohort ascertainment for the study of rare diseases. Trial Registration ClinicalTrials.gov: NCT02249923

show abstract

Section: Methodsmentioning

confidence: 99%

A Computable Phenotype Improves Cohort Ascertainment in a Pediatric Pulmonary Hypertension Registry

Geva

Gronsbell

Cai

et al. 2017

The Journal of Pediatrics

View full text Add to dashboard Cite

show abstract

“…Future work that involves other collaborative health data initiatives (eg, Informatics for Integrating Biology & the Bedside [i2b2], Observational Health Data Sciences and Informatics [OHDSI], Electronic Health Data in a European Network [EHDEN], Canadian Network for Observational Drug Effect Studies [CNODES], and Collaborative Informatics Environment for Learning on Health Outcomes [CIELO]) will provide important benefits. Technology‐wise, CNDS was built with standard communication mechanisms (eg, APIs), enabling future integration possibilities Health research community engagement .…”

Section: Discussionmentioning

confidence: 99%

Cross‐Network Directory Service: Infrastructure to enable collaborations across distributed research networks

Malenfant

Hochstadt

Nolan

et al. 2019

Learning Health Systems

View full text Add to dashboard Cite

Introduction Existing large‐scale distributed health data networks are disconnected even as they address related questions of healthcare research and public policy. This paper describes the design and implementation of a fully functional prototype open‐source tool, the Cross‐Network Directory Service (CNDS), which addresses much of what keeps distributed networks disconnected from each other. Methods The set of services needed to implement a Cross‐Directory Service was identified through engagement with stakeholders and workgroup members. CNDS was implemented using PCORnet and Sentinel network instances and tested by participating data partners. Results Web services that enable the four major functional features of the service (registration, discovery, communication, and governance) were developed and placed into an open‐source repository. The services include a robust metadata model that is extensible to accommodate a virtually unlimited inventory of metadata fields, without requiring any further software development. The user interfaces are programmatically generated based on the contents of the metadata model. Conclusion The CNDS pilot project gathered functional requirements from stakeholders and collaborating partners to build a software application to enable cross‐network data and resource sharing. The two partners—one from Sentinel and one from PCORnet—tested the software. They successfully entered metadata about their organizations and data sources and then used the Discovery and Communication functionality to find data sources of interest and send a cross‐network query. The CNDS software can help integrate disparate health data networks by providing a mechanism for data partners to participate in multiple networks, share resources, and seamlessly send queries across those networks.

show abstract

“…Providing efficient tools for clinicians to support their own clinical research, to build cooperative and collaborative networks of clinical researchers beyond the border of academic settings, and to do research in real settings, are major goals to be achieved. There are many initiatives that try to address these challenges, such as i2b2 in the U.S. [199] or EHR4CR in Europe [200]. Clinicians have been early adopters of EHRs to support their own clinical research, including in clinical practices [201].…”

Section: H Clinical Practice and Research Integrationmentioning

confidence: 99%

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Meystre¹,

Lovis²,

Bürkle³

et al. 2017

Yearb Med Inform

View full text Add to dashboard Cite

SummaryObjective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research.

show abstract

Data interchange using i2b2

Cited by 79 publications

References 9 publications

A Computable Phenotype Improves Cohort Ascertainment in a Pediatric Pulmonary Hypertension Registry

A Computable Phenotype Improves Cohort Ascertainment in a Pediatric Pulmonary Hypertension Registry

Cross‐Network Directory Service: Infrastructure to enable collaborations across distributed research networks

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Contact Info

Product

Resources

About