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

Demographic, clinical, and treatment characteristics of the juvenile primary fibromyalgia syndrome cohort enrolled in the Childhood Arthritis and Rheumatology Research Alliance Legacy Registry.

BackgroundTo describe the demographic, clinical, and treatment characteristics of youth diagnosed with juvenile primary fibromyalgia syndrome (JPFS) who are seen in pediatric rheumatology clinics.MethodsInformation on demographics, symptoms, functioning, and treatments recommended and tried were obtained on patients with JPFS as part of a multi-site patient registry (the Childhood Arthritis and Rheumatology Research Alliance Legacy Registry). Data were summarized using descriptive statistics. In a subset of patients completing registry follow-up visits, changes in symptoms, pain, and functioning were evaluated using growth modeling.ResultsOf the 201 patients with JPFS enrolled in the registry, most were Caucasian/White (85%), non-Hispanic (83%), and female (84%). Ages ranged from 9 to 20 years (M = 15.4 + 2.2). The most common symptoms reported were widespread musculoskeletal pain (91%), fatigue (84%), disordered sleep (82%), and headaches (68%). Pain intensity was rated as moderate to severe (M = 6.3 + 2.4/10). Scores on measures of functioning indicated mild to moderate impairment, with males observed to report significantly greater impairments. For the 37% of the initial cohort having follow-up data available, indicators of function and well-being were found to either worsen over time or remain relatively unchanged.ConclusionsThe symptoms of JPFS remained persistent and disabling for many patients treated by pediatric rheumatologists. Further study appears warranted to elucidate gender differences in the impact of JPFS symptoms. Work also is needed to identify accessible and effective outpatient treatment options for JPFS that can be routinely recommended or implemented by pediatric rheumatology providers

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

SHRINE: Enabling Nationally Scalable Multi-Site Disease Studies

Results of medical research studies are often contradictory or cannot be reproduced. One reason is that there may not be enough patient subjects available for observation for a long enough time period. Another reason is that patient populations may vary considerably with respect to geographic and demographic boundaries thus limiting how broadly the results apply. Even when similar patient populations are pooled together from multiple locations, differences in medical treatment and record systems can limit which outcome measures can be commonly analyzed. In total, these differences in medical research settings can lead to differing conclusions or can even prevent some studies from starting. We thus sought to create a patient research system that could aggregate as many patient observations as possible from a large number of hospitals in a uniform way. We call this system the ‘Shared Health Research Information Network’, with the following properties: (1) reuse electronic health data from everyday clinical care for research purposes, (2) respect patient privacy and hospital autonomy, (3) aggregate patient populations across many hospitals to achieve statistically significant sample sizes that can be validated independently of a single research setting, (4) harmonize the observation facts recorded at each institution such that queries can be made across many hospitals in parallel, (5) scale to regional and national collaborations. The purpose of this report is to provide open source software for multi-site clinical studies and to report on early uses of this application. At this time SHRINE implementations have been used for multi-site studies of autism co-morbidity, juvenile idiopathic arthritis, peripartum cardiomyopathy, colorectal cancer, diabetes, and others. The wide range of study objectives and growing adoption suggest that SHRINE may be applicable beyond the research uses and participating hospitals named in this report

BigMouth: a multi-institutional dental data repository

Few oral health databases are available for research and the advancement of evidence-based dentistry. In this work we developed a centralized data repository derived from electronic health records (EHRs) at four dental schools participating in the Consortium of Oral Health Research and Informatics. A multi-stakeholder committee developed a data governance framework that encouraged data sharing while allowing control of contributed data. We adopted the i2b2 data warehousing platform and mapped data from each institution to a common reference terminology. We realized that dental EHRs urgently need to adopt common terminologies. While all used the same treatment code set, only three of the four sites used a common diagnostic terminology, and there were wide discrepancies in how medical and dental histories were documented. BigMouth was successfully launched in August 2012 with data on 1.1 million patients, and made available to users at the contributing institutions

Query Health: standards-based, cross-platform population health surveillance

Objective: Understanding population-level health trends is essential to effectively monitor and improve public health. The Office of the National Coordinator for Health Information Technology (ONC) Query Health initiative is a collaboration to develop a national architecture for distributed, population-level health queries across diverse clinical systems with disparate data models. Here we review Query Health activities, including a standards-based methodology, an open-source reference implementation, and three pilot projects. Materials and methods Query Health defined a standards-based approach for distributed population health queries, using an ontology based on the Quality Data Model and Consolidated Clinical Document Architecture, Health Quality Measures Format (HQMF) as the query language, the Query Envelope as the secure transport layer, and the Quality Reporting Document Architecture as the result language. Results: We implemented this approach using Informatics for Integrating Biology and the Bedside (i2b2) and hQuery for data analytics and PopMedNet for access control, secure query distribution, and response. We deployed the reference implementation at three pilot sites: two public health departments (New York City and Massachusetts) and one pilot designed to support Food and Drug Administration post-market safety surveillance activities. The pilots were successful, although improved cross-platform data normalization is needed. Discussions This initiative resulted in a standards-based methodology for population health queries, a reference implementation, and revision of the HQMF standard. It also informed future directions regarding interoperability and data access for ONC's Data Access Framework initiative. Conclusions: Query Health was a test of the learning health system that supplied a functional methodology and reference implementation for distributed population health queries that has been validated at three sites

Designing the Arriving Refugee Informatics Surveillance and Epidemiology (ARIVE) System: A Web-based Electronic Database for Epidemiological Surveillance

Objectives: We design and implement the Arriving Refugee Informatics surVeillance and Epidemiology (ARIVE) system to improve the health of refugees undergoing resettlement and enhance existing health surveillance networks. Materials and Methods: Using the REDCap electronic data capture software as a basis we create a refugee health database incorporating data from the Center for Disease Control and Prevention’s Electronic Disease Notification (EDN) system and domestic screening data from refugee health care providers. Results: Domestic screening and EDN refugee health data have been integrated for 13,824 refugees resettled from 35 different countries into the state of Kentucky from the years 2013-2016. Discussion: A flexible software solution like REDCap provides a way to implement the core of a health surveillance network in a way that is sustainable and cost-effective and REDCap’s data dictionary standard provides an easy way to share and improve the database structure of a health surveillance network

Applying probabilistic temporal and multi-site data quality control methods to a public health mortality registry in Spain: A systematic approach to quality control of repositories

OBJECTIVE: To assess the variability in data distributions among data sources and over time through a case study of a large multisite repository as a systematic approach to data quality (DQ). MATERIALS AND METHODS: Novel probabilistic DQ control methods based on information theory and geometry are applied to the Public Health Mortality Registry of the Region of Valencia, Spain, with 512 143 entries from 2000 to 2012, disaggregated into 24 health departments. The methods provide DQ metrics and exploratory visualizations for (1) assessing the variability among multiple sources and (2) monitoring and exploring changes with time. The methods are suited to big data and multitype, multivariate, and multimodal data. RESULTS: The repository was partitioned into 2 probabilistically separated temporal subgroups following a change in the Spanish National Death Certificate in 2009. Punctual temporal anomalies were noticed due to a punctual increment in the missing data, along with outlying and clustered health departments due to differences in populations or in practices. DISCUSSION: Changes in protocols, differences in populations, biased practices, or other systematic DQ problems affected data variability. Even if semantic and integration aspects are addressed in data sharing infrastructures, probabilistic variability may still be present. Solutions include fixing or excluding data and analyzing different sites or time periods separately. A systematic approach to assessing temporal and multisite variability is proposed. CONCLUSION: Multisite and temporal variability in data distributions affects DQ, hindering data reuse, and an assessment of such variability should be a part of systematic DQ procedures.This work was supported by the Spanish Ministry of Economy and Competitiveness grant numbers RTC-2014-1530-1 and TIN-2013-43457-R, and by the Universitat Politecnica de Valencia grant number SP20141432.Sáez Silvestre, C.; Zurriaga, O.; Pérez-Panadés, J.; Melchor, I.; Robles Viejo, M.; García Gómez, JM. (2016). Applying probabilistic temporal and multi-site data quality control methods to a public health mortality registry in Spain: A systematic approach to quality control of repositories. Journal of the American Medical Informatics Association. 23(6):1085-1095. https://doi.org/10.1093/jamia/ocw010S1085109523

An iOS Framework for the Indivo X Personally Controlled Health Record

The Indivo X personally controlled health record creates a channel between researchers and the patient/subject in several large scale projects. Indivo enables patients to access their health data through a web interface and, as an “apps platform”, can be extended in functionality. Patient-facing apps, such as a medication list, may improve the data flow between researcher and patient, in both directions, and as such provide better data for the researcher and immediate benefit for the patient. However, research projects in general do not allocate large funds to patient facing apps, let alone a mobile interface. Thus we have created a framework that greatly simplifies connecting an iOS app to an Indivo X server. Our open-source framework enables novel as well as experienced iOS developers to build mobile interfaces for their research subjects, taking advantage of Indivo X