OBJECTIVE: We aimed to develop a distributed, immutable, and highly available cross-cloud blockchain system to facilitate federated data analysis activities among multiple institutions. MATERIALS AND METHODS: We preprocessed 9166 COVID-19 Structured Query Language (SQL) code, summary statistics, and user activity logs, from the GitHub repository of the Reliable Response Data Discovery for COVID-19 (R2D2) Consortium. The repository collected local summary statistics from participating institutions and aggregated the global result to a COVID-19-related clinical query, previously posted by clinicians on a website. We developed both on-chain and off-chain components to store/query these activity logs and their associated queries/results on a blockchain for immutability, transparency, and high availability of research communication. We measured run-time efficiency of contract deployment, network transactions, and confirmed the accuracy of recorded logs compared to a centralized baseline solution. RESULTS: The smart contract deployment took 4.5 s on an average. The time to record an activity log on blockchain was slightly over 2 s, versus 5-9 s for baseline. For querying, each query took on an average less than 0.4 s on blockchain, versus around 2.1 s for baseline. DISCUSSION: The low deployment, recording, and querying times confirm the feasibility of our cross-cloud, blockchain-based federated data analysis system. We have yet to evaluate the system on a larger network with multiple nodes per cloud, to consider how to accommodate a surge in activities, and to investigate methods to lower querying time as the blockchain grows. CONCLUSION: Blockchain technology can be used to support federated data analysis among multiple institutions.

While much is known about governance models for research informatics programs in academic medical centers and similarly situated cancer centers, community and public health systems have been less well-characterized. As part of implementing an enterprise research governance framework, leaders in the Los Angeles County Department of Health Services established a research informatics program, including research data warehousing. The strategy is focused on high-priority, patient-centered research that leverages the investment in health IT and an efficient, sustained contribution from 2 affiliated Clinical Translational Sciences Institutes. This case study describes the foundational governance framework and policies that were developed. We share the results of several years of planning, implementation, and operations of an academically funded research informatics service core embedded in a large, multicenter county health system. We include herein a Supplementary Appendix of governance documents that may serve as pragmatic models for similar initiatives.

OBJECTIVE: To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online. MATERIALS AND METHODS: We developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data. RESULTS: Our public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems. DISCUSSION AND CONCLUSIONS: We present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems.

OBJECTIVE: Little evidence exists about effective and scalable methods for meaningful stakeholder engagement in research. We explored patient/caregiver experiences with a high-tech online engagement approach for patient-centered research prioritization, compared their experiences with those of professional stakeholders, and identified factors associated with favorable participant experiences. METHODS: We conducted 8 online modified-Delphi (OMD) panels. Panelists participated in 2 rating rounds with a statistical feedback/online discussion round in between. Panels focused on weight management/obesity, heart failure, and Kawasaki disease. We recruited a convenience sample of adults with any of the 3 conditions (or parents/guardians of Kawasaki disease patients), clinicians, and researchers. Measures included self-reported willingness to use OMD again, the panelists' study participation and online discussion experiences, the system's perceived ease of use, and active engagement metrics. RESULTS: Out of 349 panelists, 292 (84%) completed the study. Of those, 46% were patients, 36% were clinicians, and 19% were researchers. In multivariate models, patients were not significantly more actively engaged (Odds ratio (OR) = 1.69, 95% confidence interval (CI), 0.94-3.05) but had more favorable study participation (β = 0.49; P ≤ .05) and online discussion (β = 0.18; P ≤ .05) experiences and were more willing to use OMD again (β = 0.36; P ≤ .05), compared to professional stakeholders. Positive perceptions of the OMD system's ease of use (β = 0.16; P ≤ .05) and favorable study participation (β = 0.26; P ≤ .05) and online discussion (β = 0.57; P ≤ .05) experiences were also associated with increased willingness to use OMD in the future. Active engagement was not associated with online experience indices or willingness to use OMD again. CONCLUSION: Online approaches to engaging large numbers of stakeholders are a promising and efficient adjunct to in-person meetings.

BACKGROUND: Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner. OBJECTIVE: The objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies. MATERIALS AND METHODS: Based on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network. RESULTS: The authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws. DISCUSSION AND CONCLUSION: Federated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient-level data transport, allowing institutions with strict local data storage requirements to participate in sophisticated analyses based on federated research networks.

This article describes the patient-centered Scalable National Network for Effectiveness Research (pSCANNER), which is part of the recently formed PCORnet, a national network composed of learning healthcare systems and patient-powered research networks funded by the Patient Centered Outcomes Research Institute (PCORI). It is designed to be a stakeholder-governed federated network that uses a distributed architecture to integrate data from three existing networks covering over 21 million patients in all 50 states: (1) VA Informatics and Computing Infrastructure (VINCI), with data from Veteran Health Administration's 151 inpatient and 909 ambulatory care and community-based outpatient clinics; (2) the University of California Research exchange (UC-ReX) network, with data from UC Davis, Irvine, Los Angeles, San Francisco, and San Diego; and (3) SCANNER, a consortium of UCSD, Tennessee VA, and three federally qualified health systems in the Los Angeles area supplemented with claims and health information exchange data, led by the University of Southern California. Initial use cases will focus on three conditions: (1) congestive heart failure; (2) Kawasaki disease; (3) obesity. Stakeholders, such as patients, clinicians, and health service researchers, will be engaged to prioritize research questions to be answered through the network. We will use a privacy-preserving distributed computation model with synchronous and asynchronous modes. The distributed system will be based on a common data model that allows the construction and evaluation of distributed multivariate models for a variety of statistical analyses.