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Oikonomou EK, Khera R. Designing medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility. Hellenic J Cardiol 2024:S1109-9666(24)00158-1. [PMID: 39025234 DOI: 10.1016/j.hjc.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 06/21/2024] [Accepted: 07/02/2024] [Indexed: 07/20/2024] Open
Abstract
Advances in artificial intelligence (AI) and machine learning systems promise faster, more efficient and more personalized care. While many of these models are built on the premise of improving access to the timely screening, diagnosis, and treatment of cardiovascular disease, their validity and accessibility across diverse and international cohorts remains unknown. In this mini-review article, we summarize key obstacles in the effort to design AI systems that will be scalable, accessible, and accurate across distinct geographical and temporal settings. We discuss representativeness, interoperability, quality assurance and the importance of vendor-agnostic data types that will be immediately available to end-users across the globe. These topics illustrate how the timely integration of these principles into AI development is crucial to maximizing the global benefits of AI in cardiology.
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Affiliation(s)
- Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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Khera R, Oikonomou EK, Nadkarni GN, Morley JR, Wiens J, Butte AJ, Topol EJ. Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice: JACC State-of-the-Art Review. J Am Coll Cardiol 2024; 84:97-114. [PMID: 38925729 DOI: 10.1016/j.jacc.2024.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 05/03/2024] [Accepted: 05/07/2024] [Indexed: 06/28/2024]
Abstract
Artificial intelligence (AI) has the potential to transform every facet of cardiovascular practice and research. The exponential rise in technology powered by AI is defining new frontiers in cardiovascular care, with innovations that span novel diagnostic modalities, new digital native biomarkers of disease, and high-performing tools evaluating care quality and prognosticating clinical outcomes. These digital innovations promise expanded access to cardiovascular screening and monitoring, especially among those without access to high-quality, specialized care historically. Moreover, AI is propelling biological and clinical discoveries that will make future cardiovascular care more personalized, precise, and effective. The review brings together these diverse AI innovations, highlighting developments in multimodal cardiovascular AI across clinical practice and biomedical discovery, and envisioning this new future backed by contemporary science and emerging discoveries. Finally, we define the critical path and the safeguards essential to realizing this AI-enabled future that helps achieve optimal cardiovascular health and outcomes for all.
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Affiliation(s)
- Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA; Center for Outcomes Research and Evaluation (CORE), New Haven, Connecticut, USA; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut, USA; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.
| | - Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Girish N Nadkarni
- The Samuel Bronfman Department of Medicine, Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jessica R Morley
- Digital Ethics Center, Yale University, New Haven, Connecticut, USA
| | - Jenna Wiens
- Electrical Engineering and Computer Science, Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Atul J Butte
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, USA; Center for Data-Driven Insights and Innovation, University of California Health, Oakland, California, USA
| | - Eric J Topol
- Molecular Medicine, Scripps Research Translational Institute, Scripps Research, La Jolla, California, USA
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Gottardelli B, Gatta R, Nucciarelli L, Tudor AM, Tavazzi E, Vallati M, Orini S, Di Giorgi N, Damiani A. GEN-RWD Sandbox: bridging the gap between hospital data privacy and external research insights with distributed analytics. BMC Med Inform Decis Mak 2024; 24:170. [PMID: 38886772 PMCID: PMC11184891 DOI: 10.1186/s12911-024-02549-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 05/21/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has become a pivotal tool in advancing contemporary personalised medicine, with the goal of tailoring treatments to individual patient conditions. This has heightened the demand for access to diverse data from clinical practice and daily life for research, posing challenges due to the sensitive nature of medical information, including genetics and health conditions. Regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. and the General Data Protection Regulation (GDPR) in Europe aim to strike a balance between data security, privacy, and the imperative for access. RESULTS We present the Gemelli Generator - Real World Data (GEN-RWD) Sandbox, a modular multi-agent platform designed for distributed analytics in healthcare. Its primary objective is to empower external researchers to leverage hospital data while upholding privacy and ownership, obviating the need for direct data sharing. Docker compatibility adds an extra layer of flexibility, and scalability is assured through modular design, facilitating combinations of Proxy and Processor modules with various graphical interfaces. Security and reliability are reinforced through components like Identity and Access Management (IAM) agent, and a Blockchain-based notarisation module. Certification processes verify the identities of information senders and receivers. CONCLUSIONS The GEN-RWD Sandbox architecture achieves a good level of usability while ensuring a blend of flexibility, scalability, and security. Featuring a user-friendly graphical interface catering to diverse technical expertise, its external accessibility enables personnel outside the hospital to use the platform. Overall, the GEN-RWD Sandbox emerges as a comprehensive solution for healthcare distributed analytics, maintaining a delicate equilibrium between accessibility, scalability, and security.
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Affiliation(s)
- Benedetta Gottardelli
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Roberto Gatta
- Department of Clinical and Experimental Sciences, Università degli Studi di Brescia, Brescia, Italy
| | - Leonardo Nucciarelli
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy.
| | - Andrada Mihaela Tudor
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Erica Tavazzi
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Mauro Vallati
- School of Computing and Engineering, University of Huddersfield, Huddersfield, UK
| | - Stefania Orini
- Department of Clinical and Experimental Sciences, Università degli Studi di Brescia, Brescia, Italy
- Alzheimer Operative Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Andrea Damiani
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
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Wirth FN, Abu Attieh H, Prasser F. OHDSI-compliance: a set of document templates facilitating the implementation and operation of a software stack for real-world evidence generation. Front Med (Lausanne) 2024; 11:1378866. [PMID: 38818399 PMCID: PMC11137233 DOI: 10.3389/fmed.2024.1378866] [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: 01/30/2024] [Accepted: 05/02/2024] [Indexed: 06/01/2024] Open
Abstract
Introduction The open-source software offered by the Observational Health Data Science and Informatics (OHDSI) collective, including the OMOP-CDM, serves as a major backbone for many real-world evidence networks and distributed health data analytics platforms. While container technology has significantly simplified deployments from a technical perspective, regulatory compliance can remain a major hurdle for the setup and operation of such platforms. In this paper, we present OHDSI-Compliance, a comprehensive set of document templates designed to streamline the data protection and information security-related documentation and coordination efforts required to establish OHDSI installations. Methods To decide on a set of relevant document templates, we first analyzed the legal requirements and associated guidelines with a focus on the General Data Protection Regulation (GDPR). Moreover, we analyzed the software architecture of a typical OHDSI stack and related its components to the different general types of concepts and documentation identified. Then, we created those documents for a prototypical OHDSI installation, based on the so-called Broadsea package, following relevant guidelines from Germany. Finally, we generalized the documents by introducing placeholders and options at places where individual institution-specific content will be needed. Results We present four documents: (1) a record of processing activities, (2) an information security concept, (3) an authorization concept, as well as (4) an operational concept covering the technical details of maintaining the stack. The documents are publicly available under a permissive license. Discussion To the best of our knowledge, there are no other publicly available sets of documents designed to simplify the compliance process for OHDSI deployments. While our documents provide a comprehensive starting point, local specifics need to be added, and, due to the heterogeneity of legal requirements in different countries, further adoptions might be necessary.
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Affiliation(s)
| | | | - Fabian Prasser
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Center of Health Data Science, Berlin, Germany
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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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Prasser F, Riedel N, Wolter S, Corr D, Ludwig M. [Artificial intelligence and secure use of health data in the KI-FDZ project: anonymization, synthetization, and secure processing of real-world data]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2024; 67:171-179. [PMID: 38175194 PMCID: PMC10834625 DOI: 10.1007/s00103-023-03823-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 12/08/2023] [Indexed: 01/05/2024]
Abstract
The increasing digitization of the healthcare system is leading to a growing volume of health data. Leveraging this data beyond its initial collection purpose for secondary use can provide valuable insights into diagnostics, treatment processes, and the quality of care. The Health Data Lab (HDL) will provide infrastructure for this purpose. Both the protection of patient privacy and optimal analytical capabilities are of central importance in this context, and artificial intelligence (AI) provides two opportunities. First, it enables the analysis of large volumes of data with flexible models, which means that hidden correlations and patterns can be discovered. Second, synthetic - that is, artificial - data generated by AI can protect privacy.This paper describes the KI-FDZ project, which aims to investigate innovative technologies that can support the secure provision of health data for secondary research purposes. A multi-layered approach is investigated in which data-level measures can be combined in different ways with processing in secure environments. To this end, anonymization and synthetization methods, among others, are evaluated based on two concrete application examples. Moreover, it is examined how the creation of machine learning pipelines and the execution of AI algorithms can be supported in secure processing environments. Preliminary results indicate that this approach can achieve a high level of protection while maintaining data validity. The approach investigated in the project can be an important building block in the secure secondary use of health data.
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Affiliation(s)
- Fabian Prasser
- Center für Health Data Science, Berlin Institute of Health der Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Deutschland.
| | - Nico Riedel
- Forschungsdatenzentrum Gesundheit, Bundesinstitut für Arzneimittel und Medizinprodukte (BfArM), Bonn, Deutschland
| | - Steven Wolter
- Forschungsdatenzentrum Gesundheit, Bundesinstitut für Arzneimittel und Medizinprodukte (BfArM), Bonn, Deutschland
| | - Dörte Corr
- Fraunhofer-Institut für Digitale Medizin MEVIS, Bremen, Deutschland
| | - Marion Ludwig
- InGef - Institut für angewandte Gesundheitsforschung Berlin GmbH, Berlin, Deutschland
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Putzier M, Khakzad T, Dreischarf M, Thun S, Trautwein F, Taheri N. Implementation of cloud computing in the German healthcare system. NPJ Digit Med 2024; 7:12. [PMID: 38218892 PMCID: PMC10787755 DOI: 10.1038/s41746-024-01000-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 01/03/2024] [Indexed: 01/15/2024] Open
Abstract
With the advent of artificial intelligence and Big Data - projects, the necessity for a transition from analog medicine to modern-day solutions such as cloud computing becomes unavoidable. Even though this need is now common knowledge, the process is not always easy to start. Legislative changes, for example at the level of the European Union, are helping the respective healthcare systems to take the necessary steps. This article provides an overview of how a German university hospital is dealing with European data protection laws on the integration of cloud computing into everyday clinical practice. By describing our model approach, we aim to identify opportunities and possible pitfalls to sustainably influence digitization in Germany.
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Affiliation(s)
- M Putzier
- Center for Musculoskeletal Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - T Khakzad
- Center for Musculoskeletal Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - M Dreischarf
- RAYLYTIC GmBH, Petersstraße 32 - 34, 04109, Leipzig, Germany
| | - S Thun
- Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - F Trautwein
- RAYLYTIC GmBH, Petersstraße 32 - 34, 04109, Leipzig, Germany
| | - N Taheri
- Center for Musculoskeletal Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany.
- Berlin Institute of Health, Julius Wolff Institute for Biomechanics and Musculoskeletal Regeneration, Charité-Universitätsmedizin Berlin, Augustenburger Pl. 1, 13353, Berlin, Germany.
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Zhang K, Jiang X. Sensitive Data Detection with High-Throughput Machine Learning Models in Electrical Health Records. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2024; 2023:814-823. [PMID: 38222389 PMCID: PMC10785837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
In the era of big data, there is an increasing need for healthcare providers, communities, and researchers to share data and collaborate to improve health outcomes, generate valuable insights, and advance research. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) is a federal law designed to protect sensitive health information by defining regulations for protected health information (PHI). However, it does not provide efficient tools for detecting or removing PHI before data sharing. One of the challenges in this area of research is the heterogeneous nature of PHI fields in data across different parties. This variability makes rule-based sensitive variable identification systems that work on one database fail on another. To address this issue, our paper explores the use of machine learning algorithms to identify sensitive variables in structured data, thus facilitating the de-identification process. We made a key observation that the distributions of metadata of PHI fields and non-PHI fields are very different. Based on this novel finding, we engineered over 30 features from the metadata of the original features and used machine learning to build classification models to automatically identify PHI fields in structured Electronic Health Record (EHR) data. We trained the model on a variety of large EHR databases from different data sources and found that our algorithm achieves 99% accuracy when detecting PHI-related fields for unseen datasets. The implications of our study are significant and can benefit industries that handle sensitive data.
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Affiliation(s)
- Kai Zhang
- University of Texas Health Science Center, Houston, TX, USA
| | - Xiaoqian Jiang
- University of Texas Health Science Center, Houston, TX, USA
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Li W, Kim M, Zhang K, Chen H, Jiang X, Harmanci A. COLLAGENE enables privacy-aware federated and collaborative genomic data analysis. Genome Biol 2023; 24:204. [PMID: 37697426 PMCID: PMC10496350 DOI: 10.1186/s13059-023-03039-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 08/16/2023] [Indexed: 09/13/2023] Open
Abstract
Growing regulatory requirements set barriers around genetic data sharing and collaborations. Moreover, existing privacy-aware paradigms are challenging to deploy in collaborative settings. We present COLLAGENE, a tool base for building secure collaborative genomic data analysis methods. COLLAGENE protects data using shared-key homomorphic encryption and combines encryption with multiparty strategies for efficient privacy-aware collaborative method development. COLLAGENE provides ready-to-run tools for encryption/decryption, matrix processing, and network transfers, which can be immediately integrated into existing pipelines. We demonstrate the usage of COLLAGENE by building a practical federated GWAS protocol for binary phenotypes and a secure meta-analysis protocol. COLLAGENE is available at https://zenodo.org/record/8125935 .
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Affiliation(s)
- Wentao Li
- Center for Secure Artificial Intelligence For hEalthcare (SAFE), D. Bradley McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Miran Kim
- Department of Mathematics, Department of Computer Science, Hanyang University, Seoul, 04763, Republic of Korea
- Research Institute for Convergence of Basic Science, Hanyang University, Seoul, 04763, Republic of Korea
- Bio-BigData Center, Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Seoul, 04763, Republic of Korea
| | - Kai Zhang
- Center for Secure Artificial Intelligence For hEalthcare (SAFE), D. Bradley McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
- Center for Precision Health, D. Bradley McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Xiaoqian Jiang
- Center for Secure Artificial Intelligence For hEalthcare (SAFE), D. Bradley McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Arif Harmanci
- Center for Secure Artificial Intelligence For hEalthcare (SAFE), D. Bradley McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA.
- Center for Precision Health, D. Bradley McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
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Moulaei K, Moulaei R, Bahaadinbeigy K. Barriers and facilitators of using health information technologies by women: a scoping review. BMC Med Inform Decis Mak 2023; 23:176. [PMID: 37670281 PMCID: PMC10478440 DOI: 10.1186/s12911-023-02280-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 08/31/2023] [Indexed: 09/07/2023] Open
Abstract
BACKGROUND AND AIM Health information technologies play a vital role in addressing diverse health needs among women, offering a wide array of services tailored to their specific requirements. Despite the potential benefits, the widespread utilization of these technologies by women faces numerous barriers and challenges. These barriers can cause women to either reduce their usage of health technologies or refrain from using them altogether. Therefore, this review was done with the aim of identifying and classifying barriers and facilitators. METHODS Some databases, including PubMed, Web of Sciences, and Scopus were searched using related keywords. Then, according to the inclusion and exclusion criteria, the articles were evaluated and selected. Finally, the barriers and facilitators were identified and classified. RESULTS Out of 14,399 articles, finally 35 articles were included in the review. In general, 375 barriers (232 items) and facilitators (143 items) were extracted from the studies. After merging similar items, 121 barriers (51 items) and facilitators (70 items) identified were organized into five main themes (management, technological, legal and regulatory, personal, and data and information management). The most important barriers were "privacy, confidentiality, and security concerns" (n = 24), "deficiencies and limitations of infrastructure, software, hardware, and network" (n = 19), "sociocultural challenges" (n = 15), and "poor economic status" (n = 15). Moreover, the most important facilitators were "increasing awareness, skills and continuous education of women" (n = 17, in personal theme), "providing training services" (n = 14, in management theme), "simple, usable, and user-friendly design of technologies" (n = 14, in technological theme), and "providing financial or non-financial incentives (motivation) for women" (n = 14, in personal theme). CONCLUSION This review showed that in order to use technologies, women face many barriers, either specific to women (such as gender inequality) or general (such as lack of technical skills). To overcome these barriers, policymakers, managers of organizations and medical centers, and designers of health systems can consider the facilitators identified in this review.
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Affiliation(s)
- Khadijeh Moulaei
- Department of Health Information Technology, Faculty of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Reza Moulaei
- School of medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Kambiz Bahaadinbeigy
- Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
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11
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Mese I. Addressing Concerns and Pitfalls in ChatGPT-Driven Radiology Reporting. AJR Am J Roentgenol 2023; 221:403. [PMID: 37406203 DOI: 10.2214/ajr.23.29504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/07/2023]
Affiliation(s)
- Ismail Mese
- Health Sciences University Erenkoy Mental Health and Neurology, Training and Research Hospital, Istanbul, Turkey,
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12
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Li J, Cairns BJ, Li J, Zhu T. Generating synthetic mixed-type longitudinal electronic health records for artificial intelligent applications. NPJ Digit Med 2023; 6:98. [PMID: 37244963 DOI: 10.1038/s41746-023-00834-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 05/05/2023] [Indexed: 05/29/2023] Open
Abstract
The recent availability of electronic health records (EHRs) have provided enormous opportunities to develop artificial intelligence (AI) algorithms. However, patient privacy has become a major concern that limits data sharing across hospital settings and subsequently hinders the advances in AI. Synthetic data, which benefits from the development and proliferation of generative models, has served as a promising substitute for real patient EHR data. However, the current generative models are limited as they only generate single type of clinical data for a synthetic patient, i.e., either continuous-valued or discrete-valued. To mimic the nature of clinical decision-making which encompasses various data types/sources, in this study, we propose a generative adversarial network (GAN) entitled EHR-M-GAN that simultaneously synthesizes mixed-type timeseries EHR data. EHR-M-GAN is capable of capturing the multidimensional, heterogeneous, and correlated temporal dynamics in patient trajectories. We have validated EHR-M-GAN on three publicly-available intensive care unit databases with records from a total of 141,488 unique patients, and performed privacy risk evaluation of the proposed model. EHR-M-GAN has demonstrated its superiority over state-of-the-art benchmarks for synthesizing clinical timeseries with high fidelity, while addressing the limitations regarding data types and dimensionality in the current generative models. Notably, prediction models for outcomes of intensive care performed significantly better when training data was augmented with the addition of EHR-M-GAN-generated timeseries. EHR-M-GAN may have use in developing AI algorithms in resource-limited settings, lowering the barrier for data acquisition while preserving patient privacy.
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Affiliation(s)
- Jin Li
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Benjamin J Cairns
- Clinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jingsong Li
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China.
| | - Tingting Zhu
- Department of Engineering Science, University of Oxford, Oxford, UK.
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Implementing systems thinking and data science in the training of the regenerative medicine workforce. NPJ Regen Med 2022; 7:76. [PMID: 36566283 PMCID: PMC9790008 DOI: 10.1038/s41536-022-00271-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 12/05/2022] [Indexed: 12/25/2022] Open
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Wirth FN, Kussel T, Müller A, Hamacher K, Prasser F. EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation. BMC Bioinformatics 2022; 23:531. [PMID: 36494612 PMCID: PMC9733077 DOI: 10.1186/s12859-022-05044-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 11/08/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Modern biomedical research is data-driven and relies heavily on the re-use and sharing of data. Biomedical data, however, is subject to strict data protection requirements. Due to the complexity of the data required and the scale of data use, obtaining informed consent is often infeasible. Other methods, such as anonymization or federation, in turn have their own limitations. Secure multi-party computation (SMPC) is a cryptographic technology for distributed calculations, which brings formally provable security and privacy guarantees and can be used to implement a wide-range of analytical approaches. As a relatively new technology, SMPC is still rarely used in real-world biomedical data sharing activities due to several barriers, including its technical complexity and lack of usability. RESULTS To overcome these barriers, we have developed the tool EasySMPC, which is implemented in Java as a cross-platform, stand-alone desktop application provided as open-source software. The tool makes use of the SMPC method Arithmetic Secret Sharing, which allows to securely sum up pre-defined sets of variables among different parties in two rounds of communication (input sharing and output reconstruction) and integrates this method into a graphical user interface. No additional software services need to be set up or configured, as EasySMPC uses the most widespread digital communication channel available: e-mails. No cryptographic keys need to be exchanged between the parties and e-mails are exchanged automatically by the software. To demonstrate the practicability of our solution, we evaluated its performance in a wide range of data sharing scenarios. The results of our evaluation show that our approach is scalable (summing up 10,000 variables between 20 parties takes less than 300 s) and that the number of participants is the essential factor. CONCLUSIONS We have developed an easy-to-use "no-code solution" for performing secure joint calculations on biomedical data using SMPC protocols, which is suitable for use by scientists without IT expertise and which has no special infrastructure requirements. We believe that innovative approaches to data sharing with SMPC are needed to foster the translation of complex protocols into practice.
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Affiliation(s)
- Felix Nikolaus Wirth
- grid.484013.a0000 0004 6879 971XBerlin Institute of Health at Charité – Universitätsmedizin Berlin, Medical Informatics Group, Charitéplatz 1, 10117 Berlin, Germany
| | - Tobias Kussel
- grid.6546.10000 0001 0940 1669Computational Biology and Simulation, TU Darmstadt, Darmstadt, Germany
| | - Armin Müller
- grid.484013.a0000 0004 6879 971XBerlin Institute of Health at Charité – Universitätsmedizin Berlin, Medical Informatics Group, Charitéplatz 1, 10117 Berlin, Germany
| | - Kay Hamacher
- grid.6546.10000 0001 0940 1669Computational Biology and Simulation, TU Darmstadt, Darmstadt, Germany
| | - Fabian Prasser
- grid.484013.a0000 0004 6879 971XBerlin Institute of Health at Charité – Universitätsmedizin Berlin, Medical Informatics Group, Charitéplatz 1, 10117 Berlin, Germany
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Yan C, Yan Y, Wan Z, Zhang Z, Omberg L, Guinney J, Mooney SD, Malin BA. A Multifaceted benchmarking of synthetic electronic health record generation models. Nat Commun 2022; 13:7609. [PMID: 36494374 PMCID: PMC9734113 DOI: 10.1038/s41467-022-35295-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 11/28/2022] [Indexed: 12/13/2022] Open
Abstract
Synthetic health data have the potential to mitigate privacy concerns in supporting biomedical research and healthcare applications. Modern approaches for data generation continue to evolve and demonstrate remarkable potential. Yet there is a lack of a systematic assessment framework to benchmark methods as they emerge and determine which methods are most appropriate for which use cases. In this work, we introduce a systematic benchmarking framework to appraise key characteristics with respect to utility and privacy metrics. We apply the framework to evaluate synthetic data generation methods for electronic health records data from two large academic medical centers with respect to several use cases. The results illustrate that there is a utility-privacy tradeoff for sharing synthetic health data and further indicate that no method is unequivocally the best on all criteria in each use case, which makes it evident why synthetic data generation methods need to be assessed in context.
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Affiliation(s)
- Chao Yan
- grid.412807.80000 0004 1936 9916Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN USA
| | - Yao Yan
- grid.430406.50000 0004 6023 5303Sage Bionetworks, Seattle, WA USA
| | - Zhiyu Wan
- grid.412807.80000 0004 1936 9916Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN USA
| | - Ziqi Zhang
- grid.152326.10000 0001 2264 7217Department of Computer Science, Vanderbilt University, Nashville, TN USA
| | - Larsson Omberg
- grid.430406.50000 0004 6023 5303Sage Bionetworks, Seattle, WA USA
| | - Justin Guinney
- grid.34477.330000000122986657Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA USA ,grid.511425.60000 0004 9346 3636Tempus Labs, Chicago, IL USA
| | - Sean D. Mooney
- grid.34477.330000000122986657Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA USA
| | - Bradley A. Malin
- grid.412807.80000 0004 1936 9916Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN USA ,grid.152326.10000 0001 2264 7217Department of Computer Science, Vanderbilt University, Nashville, TN USA ,grid.412807.80000 0004 1936 9916Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN USA
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Big Data in Laboratory Medicine—FAIR Quality for AI? Diagnostics (Basel) 2022; 12:diagnostics12081923. [PMID: 36010273 PMCID: PMC9406962 DOI: 10.3390/diagnostics12081923] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/05/2022] [Accepted: 08/06/2022] [Indexed: 12/22/2022] Open
Abstract
Laboratory medicine is a digital science. Every large hospital produces a wealth of data each day—from simple numerical results from, e.g., sodium measurements to highly complex output of “-omics” analyses, as well as quality control results and metadata. Processing, connecting, storing, and ordering extensive parts of these individual data requires Big Data techniques. Whereas novel technologies such as artificial intelligence and machine learning have exciting application for the augmentation of laboratory medicine, the Big Data concept remains fundamental for any sophisticated data analysis in large databases. To make laboratory medicine data optimally usable for clinical and research purposes, they need to be FAIR: findable, accessible, interoperable, and reusable. This can be achieved, for example, by automated recording, connection of devices, efficient ETL (Extract, Transform, Load) processes, careful data governance, and modern data security solutions. Enriched with clinical data, laboratory medicine data allow a gain in pathophysiological insights, can improve patient care, or can be used to develop reference intervals for diagnostic purposes. Nevertheless, Big Data in laboratory medicine do not come without challenges: the growing number of analyses and data derived from them is a demanding task to be taken care of. Laboratory medicine experts are and will be needed to drive this development, take an active role in the ongoing digitalization, and provide guidance for their clinical colleagues engaging with the laboratory data in research.
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Real world data and data science in medical research: present and future. JAPANESE JOURNAL OF STATISTICS AND DATA SCIENCE 2022; 5:769-781. [PMID: 35437515 PMCID: PMC9007054 DOI: 10.1007/s42081-022-00156-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/18/2022] [Accepted: 03/20/2022] [Indexed: 10/24/2022]
Abstract
AbstractReal world data (RWD) are generating greater interest in recent times despite being not new. There are various purposes of the RWD analytics in medical research as follows: effectiveness and safety of medical treatment, epidemiology such as incidence and prevalence of disease, burden of disease, quality of life and activity of daily living, medical costs, etc. The RWD research in medicine is a mixture of digital transformation, statistics or data science, public health, and regulatory science. Most of the articles describing the RWD or real-world evidence (RWE) in medical research cover only a portion of these specializations, which might lead to an incomplete understanding of the RWD. This article summarizes the overview and challenges of the RWD analysis in medical fields from methodological perspectives. As the first step for the RWD analysis, data source of the RWD should be comprehended. The progress of the RWD is closely related to the digitization, especially of medical administrative data and medical records. Second, the selection of appropriate statistical and epidemiological methods is highly critical for an RWD analysis than those for randomized clinical trials. This is because it contains greater varieties of bias, which should be controlled by balancing the underlying risk between treatment groups. Last, the future of the RWD is discussed in terms of overcoming limited data by proxy confounders, using unstructured text data, linking of multiple databases, using the RWD or RWE for a regulatory purpose, and evaluating values and new aspects in medical research brought by the RWD.
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Cullen MR, Baiocchi M, Chamberlain L, Chu I, Horwitz RI, Mello M, O'Hara A, Roosz S. Population health science as a unifying foundation for translational clinical and public health research. SSM Popul Health 2022; 18:101047. [PMID: 35252530 PMCID: PMC8885441 DOI: 10.1016/j.ssmph.2022.101047] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 01/31/2022] [Accepted: 02/13/2022] [Indexed: 12/04/2022] Open
Abstract
Separated both in academics and practice since the Rockefeller Foundation effort to “liberate” public health from perceived subservience to clinical medicine a century ago, research in public health and clinical medicine have evolved separately. Today, translational research in population health science offers a means of fostering their convergence, with potentially great benefit to both domains. Although evidence that the two fields need not and should not be entirely distinct in their methods and goals has been accumulating for over a decade, the prodigious efforts of biomedical and social sciences over the past year to address the COVID-19 pandemic has placed this unifying approach to translational research in both fields in a new light. Specifically, the coalescence of clinical and population-level strategies to control disease and novel uses of population-level data and tools in research relating to the pandemic have illuminated a promising future for translational research. We exploit this unique window to re-examine how translational research is conducted and where it may be going. We first discuss the transformation that has transpired in the research firmament over the past two decades and the opportunities these changes afford. Next, we present some of the challenges—technical, cultural, legal, and ethical— that need attention if these opportunities are to be successfully exploited. Finally, we present some recommendations for addressing these challenges. Population datasets with extensive biologic, medical and social information augur a new approach for translational research. Important questions about health throughout the life course could be answered were relevant person-level data accessible. Technical, cultural, and legal constraints presently limit the ability to actualize this vision. Analysis of these limitations in the context of the recent pandemic reveals each could be overcome with proper attention.
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Affiliation(s)
- Mark R. Cullen
- Center for Population Health Sciences, Stanford School of Medicine, Stanford, CA, USA
- Retired, USA
- Corresponding author. 2776 Stirrup Way, Los Altos Hills, CA, 94022, USA.
| | - Michael Baiocchi
- Department of Epidemiology and Population Health, Stanford School of Medicine, Stanford, CA, USA
| | - Lisa Chamberlain
- Department of Pediatrics, Stanford School of Medicine, Stanford, CA, USA
| | - Isabella Chu
- Center for Population Health Sciences, Stanford School of Medicine, Stanford, CA, USA
| | - Ralph I. Horwitz
- Department of Medicine, Lewis Katz School of Medicine, Temple University, USA
| | - Michelle Mello
- Stanford Health Policy and the Department of Medicine, Stanford University School of Medicine, and Stanford Law School, and the Freeman Spogli Institute for International Studies, all in Stanford, CA, USA
| | - Amy O'Hara
- McCourt School of Public Policy, Georgetown University Washington, DC, USA
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