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

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 ‘apps’ 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

Outcomes of SARS-CoV-2 infection among children and young people with pre-existing rheumatic and musculoskeletal diseases

OBJECTIVES: Some adults with rheumatic and musculoskeletal diseases (RMDs) are at increased risk of COVID-19-related death. Excluding post-COVID-19 multisystem inflammatory syndrome of children, children and young people (CYP) are overall less prone to severe COVID-19 and most experience a mild or asymptomatic course. However, it is unknown if CYP with RMDs are more likely to have more severe COVID-19. This analysis aims to describe outcomes among CYP with underlying RMDs with COVID-19. METHODS: Using the European Alliance of Associations for Rheumatology COVID-19 Registry, the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry, and the CARRA-sponsored COVID-19 Global Paediatric Rheumatology Database, we obtained data on CYP with RMDs who reported SARS-CoV-2 infection (presumptive or confirmed). Patient characteristics and illness severity were described, and factors associated with COVID-19 hospitalisation were investigated. RESULTS: 607 CYP with RMDs <19 years old from 25 different countries with SARS-CoV-2 infection were included, the majority with juvenile idiopathic arthritis (JIA; n=378; 62%). Forty-three (7%) patients were hospitalised; three of these patients died. Compared with JIA, diagnosis of systemic lupus erythematosus, mixed connective tissue disease, vasculitis, or other RMD (OR 4.3; 95% CI 1.7 to 11) or autoinflammatory syndrome (OR 3.0; 95% CI 1.1 to 8.6) was associated with hospitalisation, as was obesity (OR 4.0; 95% CI 1.3 to 12). CONCLUSIONS: This is the most significant investigation to date of COVID-19 in CYP with RMDs. It is important to note that the majority of CYP were not hospitalised, although those with severe systemic RMDs and obesity were more likely to be hospitalised

Outcomes of SARS-CoV-2 infection among children and young people with pre-existing rheumatic and musculoskeletal diseases.

OBJECTIVES: Some adults with rheumatic and musculoskeletal diseases (RMDs) are at increased risk of COVID-19-related death. Excluding post-COVID-19 multisystem inflammatory syndrome of children, children and young people (CYP) are overall less prone to severe COVID-19 and most experience a mild or asymptomatic course. However, it is unknown if CYP with RMDs are more likely to have more severe COVID-19. This analysis aims to describe outcomes among CYP with underlying RMDs with COVID-19. METHODS: Using the European Alliance of Associations for Rheumatology COVID-19 Registry, the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry, and the CARRA-sponsored COVID-19 Global Paediatric Rheumatology Database, we obtained data on CYP with RMDs who reported SARS-CoV-2 infection (presumptive or confirmed). Patient characteristics and illness severity were described, and factors associated with COVID-19 hospitalisation were investigated. RESULTS: 607 CYP with RMDs <19 years old from 25 different countries with SARS-CoV-2 infection were included, the majority with juvenile idiopathic arthritis (JIA; n=378; 62%). Forty-three (7%) patients were hospitalised; three of these patients died. Compared with JIA, diagnosis of systemic lupus erythematosus, mixed connective tissue disease, vasculitis, or other RMD (OR 4.3; 95% CI 1.7 to 11) or autoinflammatory syndrome (OR 3.0; 95% CI 1.1 to 8.6) was associated with hospitalisation, as was obesity (OR 4.0; 95% CI 1.3 to 12). CONCLUSIONS: This is the most significant investigation to date of COVID-19 in CYP with RMDs. It is important to note that the majority of CYP were not hospitalised, although those with severe systemic RMDs and obesity were more likely to be hospitalised

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 credentials” flow.</p

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, PCORnet’s 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