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Baumgartner M, Kreiner K, Lauschensky A, Jammerbund B, Donsa K, Hayn D, Wiesmüller F, Demelius L, Modre-Osprian R, Neururer S, Slamanig G, Prantl S, Brunelli L, Pfeifer B, Pölzl G, Schreier G. Health data space nodes for privacy-preserving linkage of medical data to support collaborative secondary analyses. Front Med (Lausanne) 2024; 11:1301660. [PMID: 38660421 PMCID: PMC11039786 DOI: 10.3389/fmed.2024.1301660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 03/21/2024] [Indexed: 04/26/2024] Open
Abstract
Introduction The potential for secondary use of health data to improve healthcare is currently not fully exploited. Health data is largely kept in isolated data silos and key infrastructure to aggregate these silos into standardized bodies of knowledge is underdeveloped. We describe the development, implementation, and evaluation of a federated infrastructure to facilitate versatile secondary use of health data based on Health Data Space nodes. Materials and methods Our proposed nodes are self-contained units that digest data through an extract-transform-load framework that pseudonymizes and links data with privacy-preserving record linkage and harmonizes into a common data model (OMOP CDM). To support collaborative analyses a multi-level feature store is also implemented. A feasibility experiment was conducted to test the infrastructures potential for machine learning operations and deployment of other apps (e.g., visualization). Nodes can be operated in a network at different levels of sharing according to the level of trust within the network. Results In a proof-of-concept study, a privacy-preserving registry for heart failure patients has been implemented as a real-world showcase for Health Data Space nodes at the highest trust level, linking multiple data sources including (a) electronical medical records from hospitals, (b) patient data from a telemonitoring system, and (c) data from Austria's national register of deaths. The registry is deployed at the tirol kliniken, a hospital carrier in the Austrian state of Tyrol, and currently includes 5,004 patients, with over 2.9 million measurements, over 574,000 observations, more than 63,000 clinical free text notes, and in total over 5.2 million data points. Data curation and harmonization processes are executed semi-automatically at each individual node according to data sharing policies to ensure data sovereignty, scalability, and privacy. As a feasibility test, a natural language processing model for classification of clinical notes was deployed and tested. Discussion The presented Health Data Space node infrastructure has proven to be practicable in a real-world implementation in a live and productive registry for heart failure. The present work was inspired by the European Health Data Space initiative and its spirit to interconnect health data silos for versatile secondary use of health data.
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Affiliation(s)
- Martin Baumgartner
- Center for Health and Bioresources, AIT Austrian Institute of Technology, Vienna, Austria
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Karl Kreiner
- Center for Health and Bioresources, AIT Austrian Institute of Technology, Vienna, Austria
| | - Aaron Lauschensky
- Center for Health and Bioresources, AIT Austrian Institute of Technology, Vienna, Austria
| | - Bernhard Jammerbund
- Center for Health and Bioresources, AIT Austrian Institute of Technology, Vienna, Austria
| | - Klaus Donsa
- Center for Health and Bioresources, AIT Austrian Institute of Technology, Vienna, Austria
| | - Dieter Hayn
- Center for Health and Bioresources, AIT Austrian Institute of Technology, Vienna, Austria
- Ludwig Boltzmann Institute for Digital Health and Prevention, Salzburg, Austria
| | - Fabian Wiesmüller
- Center for Health and Bioresources, AIT Austrian Institute of Technology, Vienna, Austria
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
- Ludwig Boltzmann Institute for Digital Health and Prevention, Salzburg, Austria
| | - Lea Demelius
- Institute of Interactive Systems and Data Science, Graz University of Technology, Graz, Austria
- Know-Center GmbH, Graz, Austria
| | | | - Sabrina Neururer
- Department of Clinical Epidemiology, Tyrolean Federal Institute for Integrated Care, Tirol Kliniken GmbH, Innsbruck, Austria
- Division for Digital Health and Telemedicine, UMIT TIROL—Private University for Health Sciences and Technology, Hall in Tyrol, Austria
| | | | | | - Luca Brunelli
- Department of Internal Medicine III, Cardiology and Angiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Bernhard Pfeifer
- Division for Digital Health and Telemedicine, UMIT TIROL—Private University for Health Sciences and Technology, Hall in Tyrol, Austria
- Tyrolean Federal Institute for Integrated Care, Tirol Kliniken GmbH, Innsbruck, Austria
| | - Gerhard Pölzl
- Department of Internal Medicine III, Cardiology and Angiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Günter Schreier
- Center for Health and Bioresources, AIT Austrian Institute of Technology, Vienna, Austria
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
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Technical Design and Development of A Self-Sovereign Identity Management Platform for Patient-Centric Health Care Using Blockchain Technology. BLOCKCHAIN IN HEALTHCARE TODAY 2022; 5:196. [PMID: 36779027 PMCID: PMC9907400 DOI: 10.30953/bhty.v5.196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 01/06/2022] [Accepted: 01/06/2022] [Indexed: 11/17/2022]
Abstract
Objective Clinical data in the United States are highly fragmented, stored in numerous different databases, and are defined by service providers or clinical specialties rather than by individuals or their families. As a result, linking or aggregating a complete record for a patient is a major technological, legal, and operational challenge. One of the factors that has made clinical data integration so difficult to achieve is the lack of a universal ID for everyone. This leads to other related problems of having to prove identity at each interaction with the health system and repeatedly providing basic information on demographics, insurance, payment, and medical conditions. Traditional solutions that require complex governance, expensive technology, and risks to privacy and security of the data have failed adequately to solve this interoperability problem. We describe the technical design decisions of a patient-centric decentralized health identity management system using the blockchain technology, called MediLinker, to address some of these challenges. Design Our multidisciplinary research group developed and implemented an identity wallet, which uses the blockchain technology to manage verifiable credentials issued by healthcare clinics, banks, and insurance companies. To manage patient's self-sovereign identity, we leveraged the Hyperledger Indy blockchain framework to store patient's decentralized identifiers (DIDs) and the schemas or format for each credential type. In contrast, the credentials containing patient data are stored 'off-ledger' in each person's wallet and accessible via a computer or smartphone. We used Hyperledger Aries as a middleware layer (API: Application Programming Interface) to connect Hyperledger Indy with the front-end, which was developed using a JavaScript framework, ReactJS (Web Application) and React Native (iOS Application). Results MediLinker allows users to store their personal data on digital wallets, which they control. It uses a decentralized trusted identity using Hyperledger Indy and Hyperledger Aries. Patients use MediLinker to register and share their information securely and in a trusted system with healthcare and other service providers. Each MediLinker wallet can have six credential types: health ID with patient demographics, insurance, medication list including COVID-19 vaccination status, credit card, medical power of attorney (MPOA) for guardians of pediatric or geriatric patients, and research consent. The system allows for in-person and remote granting and revoking of such permissions for care, research, or other purposes without repeatedly requiring physical identity documents or enrollment information. Conclusion We successfully developed and tested a blockchain-based technical architecture, described in this article, as an identity management system that may be operationalized and scaled for future implementation to improve patient experience and control over their personal information.
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Wu AC, Graif C, Mitchell SG, Meurer J, Mandl KD. Creative Approaches for Assessing Long-term Outcomes in Children. Pediatrics 2021; 148:s25-s32. [PMID: 34210844 PMCID: PMC8287841 DOI: 10.1542/peds.2021-050693f] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/31/2021] [Indexed: 11/24/2022] Open
Abstract
Advances in new technologies, when incorporated into routine health screening, have tremendous promise to benefit children. The number of health screening tests, many of which have been developed with machine learning or genomics, has exploded. To assess efficacy of health screening, ideally, randomized trials of screening in youth would be conducted; however, these can take years to conduct and may not be feasible. Thus, innovative methods to evaluate the long-term outcomes of screening are needed to help clinicians and policymakers make informed decisions. These methods include using longitudinal and linked-data systems to evaluate screening in clinical and community settings, school data, simulation modeling approaches, and methods that take advantage of data available in the digital and genomic age. Future research is needed to evaluate how longitudinal and linked-data systems drawing on community and clinical settings can enable robust evaluations of the effects of screening on changes in health status. Additionally, future studies are needed to benchmark participating individuals and communities against similar counterparts and to link big data with natural experiments related to variation in screening policies. These novel approaches have great potential for identifying and addressing differences in access to screening and effectiveness of screening across population groups and communities.
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Affiliation(s)
- Ann Chen Wu
- Center for Healthcare Research in Pediatrics, Department of Population Medicine, Harvard Medical School, Harvard University and Harvard Pilgrim Health Care, Boston, Massachusetts
| | - Corina Graif
- Department of Sociology and Criminology, Population Research Institute, Pennsylvania State University, University Park, Pennsylvania
| | | | - John Meurer
- Division of Community Health, Medical College of Wisconsin, Milwaukie, Wisconsin
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts
- Departments of Biomedical Informatics and Pediatrics, Harvard Medical School, Harvard University, Boston, Massachusetts
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