22 research outputs found
Recommended from our members
An i2b2-based, generalizable, open source, self-scaling chronic disease registry
Objective: Registries are a well-established mechanism for obtaining high quality, disease-specific data, but are often highly project-specific in their design, implementation, and policies for data use. In contrast to the conventional model of centralized data contribution, warehousing, and control, we design a self-scaling registry technology for collaborative data sharing, based upon the widely adopted Integrating Biology & the Bedside (i2b2) data warehousing framework and the Shared Health Research Information Network (SHRINE) peer-to-peer networking software. Materials and methods Focusing our design around creation of a scalable solution for collaboration within multi-site disease registries, we leverage the i2b2 and SHRINE open source software to create a modular, ontology-based, federated infrastructure that provides research investigators full ownership and access to their contributed data while supporting permissioned yet robust data sharing. We accomplish these objectives via web services supporting peer-group overlays, group-aware data aggregation, and administrative functions. Results: The 56-site Childhood Arthritis & Rheumatology Research Alliance (CARRA) Registry and 3-site Harvard Inflammatory Bowel Diseases Longitudinal Data Repository now utilize i2b2 self-scaling registry technology (i2b2-SSR). This platform, extensible to federation of multiple projects within and between research networks, encompasses >6000 subjects at sites throughout the USA. Discussion We utilize the i2b2-SSR platform to minimize technical barriers to collaboration while enabling fine-grained control over data sharing. Conclusions: The implementation of i2b2-SSR for the multi-site, multi-stakeholder CARRA Registry has established a digital infrastructure for community-driven research data sharing in pediatric rheumatology in the USA. We envision i2b2-SSR as a scalable, reusable solution facilitating interdisciplinary research across diseases
Recommended from our members
Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS): Architecture
We describe the architecture of the Patient Centered Outcomes Research Institute (PCORI) funded Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS, http://www.SCILHS.org) clinical data research network, which leverages the $48 billion dollar federal investment in health information technology (IT) to enable a queryable semantic data model across 10 health systems covering more than 8 million patients, plugging universally into the point of care, generating evidence and discovery, and thereby enabling clinician and patient participation in research during the patient encounter. Central to the success of SCILHS is development of innovative âappsâ to improve PCOR research methods and capacitate point of care functions such as consent, enrollment, randomization, and outreach for patient-reported outcomes. SCILHS adapts and extends an existing national research network formed on an advanced IT infrastructure built with open source, free, modular components
Recommended from our members
C3-PRO: Connecting ResearchKit to the Health System Using i2b2 and FHIR
A renewed interest by consumer information technology giants in the healthcare domain is focused on transforming smartphones into personal health data storage devices. With the introduction of the open source ResearchKit, Apple provides a framework for researchers to inform and consent research subjects, and to readily collect personal health data and patient reported outcomes (PRO) from distributed populations. However, being research backend agnostic, ResearchKit does not provide data transmission facilities, leaving research apps disconnected from the health system. Personal health data and PROs are of the most value when presented in context along with health system data. Our aim was to build a toolchain that allows easy and secure integration of personal health and PRO data into an open source platform widely adopted across 140 academic medical centers. We present C3-PRO: the Consent, Contact, and Community framework for Patient Reported Outcomes. This open source toolchain connects, in a standards-compliant fashion, any ResearchKit app to the widely-used clinical research infrastructure Informatics for Integrating Biology and the Bedside (i2b2). C3-PRO leverages the emerging health data standard Fast Healthcare Interoperability Resources (FHIR)
C3-PRO data flow.
<p>Data flow from data capture on the phone through the C3-PRO receiver, consumer and i2b2 cell into the i2b2 database.</p
OAuth2 dynamic client registration flow, extended to use the app's App Store receipt.
<p>The receipt data is sent alongside the standard OAuth2 registration parameters and verified with Apple's iTunes servers. If the receipt is valid, standard dynamic client registration continues. Subsequently the app can request access tokens with the supplied client key and -secret through an OAuth2 "client credentialsâ flow.</p
Screenshots of the C Tracker app making use of C3-PRO.
<p><b>A</b>) App dashboard for participation overview. <b>B</b>) Viewing the generated consent PDF file, including signature and date, on device. <b>C</b>) Filling out a short survey, showing one of the questions rendered by ResearchKit.</p
Recommended from our members
The Ad-Hoc Uncertainty Principle of Patient Privacy
The Health Information Portability and Accountability Act (HIPAA) allows for the exchange of de-identified patient data, but its definition of de-identification is essentially open-ended, thus leaving the onus on dataset providers to ensure patient privacy. The Patient Centered Outcomes Research Network (PCORnet) builds a de-identification approach into queries, but we have noticed various subtle problems with this approach. We censor aggregate counts below a threshold (i.e. <11) to protect patient privacy. However, we have found that thresholded numbers can at times be inferred, and some key numbers are not thresholded at all. Furthermore, PCORnetâs approach of thresholding low counts introduces a selection bias which slants the data towards larger health care sites and their corresponding demographics. We propose a solution: instead of censoring low counts, introduce Gaussian noise to all aggregate counts. We describe this approach and the freely available tools we created for this purpose
Learning a Comorbidity-Driven Taxonomy of Pediatric Pulmonary Hypertension
Rationale: Pediatric pulmonary hypertension (PH) is a heterogeneous condition with varying natural history and therapeutic response. Precise classification of PH subtypes is, therefore, crucial for individualizing care. However, gaps remain in our understanding of the spectrum of PH in children. Objective: We seek to study the manifestations of PH in children and to assess the feasibility of applying a network-based approach to discern disease subtypes from comorbidity data recorded in longitudinal data sets. Methods and Results: A retrospective cohort study comprising 6 943 263 children (<18 years of age) enrolled in a commercial health insurance plan in the United States, between January 2010 and May 2013. A total of 1583 (0.02%) children met the criteria for PH. We identified comorbidities significantly associated with PH compared with the general population of children without PH. A Bayesian comorbidity network was constructed to model the interdependencies of these comorbidities, and network-clustering analysis was applied to derive disease subtypes comprising subgraphs of highly connected comorbid conditions. A total of 186 comorbidities were found to be significantly associated with PH. Network analysis of comorbidity patterns captured most of the major PH subtypes with known pathological basis defined by the World Health Organization and Panama classifications. The analysis further identified many subtypes documented in only a few case studies, including rare subtypes associated with several well-described genetic syndromes. Conclusions: Application of network science to model comorbidity patterns recorded in longitudinal data sets can facilitate the discovery of disease subtypes. Our analysis relearned established subtypes, thus validating the approach, and identified rare subtypes that are difficult to discern through clinical observations, providing impetus for deeper investigation of the disease subtypes that will enrich current disease classifications. Keywords: cluster analysis; hypertension, pulmonary; comorbidity; pediatrics; connective tissue diseaseNational Institutes of Health (U.S.) (Award U01HL121518
Recommended from our members
Adding patient-reported outcomes to a multisite registry to quantify quality of life and experiences of disease and treatment for youth with juvenile idiopathic arthritis.
Children with Juvenile Idiopathic Arthritis (JIA) often have poor health-related quality of life (HRQOL) despite advances in treatment. Patient-centered research may shed light on how patient experiences of treatment and disease contribute to HRQOL, pinpointing directions for improving care and enhancing outcomes. Parent proxies of youth enrolled in the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry shared patient-reported outcomes about their child's HRQOL and experiences of disease and treatment burden (pain interference, morning stiffness, history of medication side effects and methotrexate intolerance). Contributions of these measures to HRQOL were estimated using generalized estimating equations accounting for site and patient demographics. Patients (N = 180) were 81.1% white non-Hispanic and 76.7% female. Mean age was 11.8 (SD = 3.6) years, mean disease duration was 7.7 years (SD = 3.5). Mean Total Pediatric Quality of Life was 76.7 (SD = 18.2). Mean pain interference score was 50.1 (SD = 11.1). Nearly one-in-five (17.8%) youth experienced >15 min of morning stiffness on a typical day, more than one quarter (26.7%) reported â„1 serious medication side effect and among 90 methotrexate users, 42.2% met criteria for methotrexate intolerance. Measures of disease and treatment burden were independently negatively associated with HRQOL (all p-values <0.01). Negative associations among measures of treatment burden and HRQOL were attenuated after controlling for disease burden and clinical characteristics but remained significant. For youth with JIA, HRQOL is multidimensional, reflecting disease as well as treatment factors. Adverse treatment experiences undermine HRQOL even after accounting for disease symptoms and disease activity and should be assessed routinely to improve wellbeing
Recommended from our members
Adding patient-reported outcomes to a multisite registry to quantify quality of life and experiences of disease and treatment for youth with juvenile idiopathic arthritis
Background: Children with Juvenile Idiopathic Arthritis (JIA) often have poor health-related quality of life (HRQOL) despite advances in treatment. Patient-centered research may shed light on how patient experiences of treatment and disease contribute to HRQOL, pinpointing directions for improving care and enhancing outcomes. Methods: Parent proxies of youth enrolled in the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry shared patient-reported outcomes about their childâs HRQOL and experiences of disease and treatment burden (pain interference, morning stiffness, history of medication side effects and methotrexate intolerance). Contributions of these measures to HRQOL were estimated using generalized estimating equations accounting for site and patient demographics. Results: Patients (N = 180) were 81.1% white non-Hispanic and 76.7% female. Mean age was 11.8 (SD = 3.6) years, mean disease duration was 7.7 years (SD = 3.5). Mean Total Pediatric Quality of Life was 76.7 (SD = 18.2). Mean pain interference score was 50.1 (SD = 11.1). Nearly one-in-five (17.8%) youth experienced >15 min of morning stiffness on a typical day, more than one quarter (26.7%) reported â„1 serious medication side effect and among 90 methotrexate users, 42.2% met criteria for methotrexate intolerance. Measures of disease and treatment burden were independently negatively associated with HRQOL (all p-values <0.01). Negative associations among measures of treatment burden and HRQOL were attenuated after controlling for disease burden and clinical characteristics but remained significant. Conclusions: For youth with JIA, HRQOL is multidimensional, reflecting disease as well as treatment factors. Adverse treatment experiences undermine HRQOL even after accounting for disease symptoms and disease activity and should be assessed routinely to improve wellbeing. Electronic supplementary material The online version of this article (10.1186/s41687-017-0025-2) contains supplementary material, which is available to authorized users