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Palaniappan K, Lin EYT, Vogel S, Lim JCW. Gaps in the Global Regulatory Frameworks for the Use of Artificial Intelligence (AI) in the Healthcare Services Sector and Key Recommendations. Healthcare (Basel) 2024; 12:1730. [PMID: 39273754 PMCID: PMC11394803 DOI: 10.3390/healthcare12171730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2024] [Revised: 08/23/2024] [Accepted: 08/27/2024] [Indexed: 09/15/2024] Open
Abstract
Artificial Intelligence (AI) has shown remarkable potential to revolutionise healthcare by enhancing diagnostics, improving treatment outcomes, and streamlining administrative processes. In the global regulatory landscape, several countries are working on regulating AI in healthcare. There are five key regulatory issues that need to be addressed: (i) data security and protection-measures to cover the "digital health footprints" left unknowingly by patients when they access AI in health services; (ii) data quality-availability of safe and secure data and more open database sources for AI, algorithms, and datasets to ensure equity and prevent demographic bias; (iii) validation of algorithms-mapping of the explainability and causability of the AI system; (iv) accountability-whether this lies with the healthcare professional, healthcare organisation, or the personified AI algorithm; (v) ethics and equitable access-whether fundamental rights of people are met in an ethical manner. Policymakers may need to consider the entire life cycle of AI in healthcare services and the databases that were used for the training of the AI system, along with requirements for their risk assessments to be publicly accessible for effective regulatory oversight. AI services that enhance their functionality over time need to undergo repeated algorithmic impact assessment and must also demonstrate real-time performance. Harmonising regulatory frameworks at the international level would help to resolve cross-border issues of AI in healthcare services.
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Affiliation(s)
- Kavitha Palaniappan
- Centre of Regulatory Excellence, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Elaine Yan Ting Lin
- Centre of Regulatory Excellence, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Silke Vogel
- Centre of Regulatory Excellence, Duke-NUS Medical School, Singapore 169857, Singapore
| | - John C W Lim
- Centre of Regulatory Excellence, Duke-NUS Medical School, Singapore 169857, Singapore
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Li Y, Wang M, Wang L, Cao Y, Liu Y, Zhao Y, Yuan R, Yang M, Lu S, Sun Z, Zhou F, Qian Z, Kang H. Advances in the Application of AI Robots in Critical Care: Scoping Review. J Med Internet Res 2024; 26:e54095. [PMID: 38801765 PMCID: PMC11165292 DOI: 10.2196/54095] [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: 10/29/2023] [Revised: 03/07/2024] [Accepted: 04/22/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND In recent epochs, the field of critical medicine has experienced significant advancements due to the integration of artificial intelligence (AI). Specifically, AI robots have evolved from theoretical concepts to being actively implemented in clinical trials and applications. The intensive care unit (ICU), known for its reliance on a vast amount of medical information, presents a promising avenue for the deployment of robotic AI, anticipated to bring substantial improvements to patient care. OBJECTIVE This review aims to comprehensively summarize the current state of AI robots in the field of critical care by searching for previous studies, developments, and applications of AI robots related to ICU wards. In addition, it seeks to address the ethical challenges arising from their use, including concerns related to safety, patient privacy, responsibility delineation, and cost-benefit analysis. METHODS Following the scoping review framework proposed by Arksey and O'Malley and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we conducted a scoping review to delineate the breadth of research in this field of AI robots in ICU and reported the findings. The literature search was carried out on May 1, 2023, across 3 databases: PubMed, Embase, and the IEEE Xplore Digital Library. Eligible publications were initially screened based on their titles and abstracts. Publications that passed the preliminary screening underwent a comprehensive review. Various research characteristics were extracted, summarized, and analyzed from the final publications. RESULTS Of the 5908 publications screened, 77 (1.3%) underwent a full review. These studies collectively spanned 21 ICU robotics projects, encompassing their system development and testing, clinical trials, and approval processes. Upon an expert-reviewed classification framework, these were categorized into 5 main types: therapeutic assistance robots, nursing assistance robots, rehabilitation assistance robots, telepresence robots, and logistics and disinfection robots. Most of these are already widely deployed and commercialized in ICUs, although a select few remain under testing. All robotic systems and tools are engineered to deliver more personalized, convenient, and intelligent medical services to patients in the ICU, concurrently aiming to reduce the substantial workload on ICU medical staff and promote therapeutic and care procedures. This review further explored the prevailing challenges, particularly focusing on ethical and safety concerns, proposing viable solutions or methodologies, and illustrating the prospective capabilities and potential of AI-driven robotic technologies in the ICU environment. Ultimately, we foresee a pivotal role for robots in a future scenario of a fully automated continuum from admission to discharge within the ICU. CONCLUSIONS This review highlights the potential of AI robots to transform ICU care by improving patient treatment, support, and rehabilitation processes. However, it also recognizes the ethical complexities and operational challenges that come with their implementation, offering possible solutions for future development and optimization.
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Affiliation(s)
- Yun Li
- Medical School of Chinese PLA, Beijing, China
- The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Min Wang
- Medical School of Chinese PLA, Beijing, China
- The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Lu Wang
- Medical School of Chinese PLA, Beijing, China
- The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Yuan Cao
- The Second Hospital, Hebei Medical University, Hebei, China
| | - Yuyan Liu
- Medical School of Chinese PLA, Beijing, China
- The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Yan Zhao
- The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Rui Yuan
- Medical School of Chinese PLA, Beijing, China
- The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Mengmeng Yang
- The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Siqian Lu
- Beidou Academic & Research Center, Beidou Life Science, Guangzhou, China
| | - Zhichao Sun
- Beidou Academic & Research Center, Beidou Life Science, Guangzhou, China
| | - Feihu Zhou
- The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Zhirong Qian
- Beidou Academic & Research Center, Beidou Life Science, Guangzhou, China
- Department of Radiation Oncology, Fujian Medical University Union Hospital, Fujian, China
- The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Hongjun Kang
- The First Medical Centre, Chinese PLA General Hospital, Beijing, China
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Toma M, Bönisch C, Löhnhardt B, Kelm M, Bohnenberger H, Winkelmann S, Ströbel P, Kesztyüs T. Research collaboration data platform ensuring general data protection. Sci Rep 2024; 14:11887. [PMID: 38789442 PMCID: PMC11126409 DOI: 10.1038/s41598-024-61912-8] [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: 10/10/2023] [Accepted: 05/10/2024] [Indexed: 05/26/2024] Open
Abstract
Translational data is of paramount importance for medical research and clinical innovation. It has the potential to benefit individuals and organizations, however, the protection of personal data must be guaranteed. Collecting diverse omics data and electronic health records (EHR), re-using the minimized data, as well as providing a reliable data transfer between different institutions are mandatory steps for the development of the promising field of big data and artificial intelligence in medical research. This is made possible within the proposed data platform in this research project. The established data platform enables the collaboration between public and commercial organizations by data transfer from various clinical systems into a cloud for supporting multi-site research while ensuring compliant data governance.
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Affiliation(s)
| | - Caroline Bönisch
- Medical Data Integration Center, Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany.
- Faculty of Electrical Engineering and Computer Science, University of Applied Sciences Stralsund, Stralsund, Germany.
| | - Benjamin Löhnhardt
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
| | | | | | - Sven Winkelmann
- Siemens Healthineers AG, Erlangen, Germany
- Nuremberg Institute of Technology, Nuremberg, Germany
| | - Philipp Ströbel
- Institute of Pathology, University Medical Center Göttingen, Göttingen, Germany
| | - Tibor Kesztyüs
- Medical Data Integration Center, Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
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4
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Smit JAR, Mostert M, van der Graaf R, Grobbee DE, van Delden JJM. Specific measures for data-intensive health research without consent: a systematic review of soft law instruments and academic literature. Eur J Hum Genet 2024; 32:21-30. [PMID: 37848609 PMCID: PMC10772063 DOI: 10.1038/s41431-023-01471-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 08/24/2023] [Accepted: 09/18/2023] [Indexed: 10/19/2023] Open
Abstract
It is a common misunderstanding of current European data protection law that when consent is not being used as lawful basis, the processing of personal data is prohibited. Article 9(2)(j) of the European General Data Protection Regulation (GDPR) permits Member States to establish a legal basis in national law that allows for the processing of personal data for scientific research purposes without consent. However, the European legislator has formulated this "research exemption" as an opening clause, rendering the GDPR not specific as to what measures exactly are required to comply with the research exemption. This may have significant implications for both the protection of personal data and the advancement of data-intensive health research. We performed a systematic review of relevant soft law instruments and academic literature to identify what measures are mentioned in those documents. Our analysis resulted in the identification of four overarching themes of suggested measures: organizational measures; technical measures; oversight and review mechanisms; and public engagement and participation. Some of the suggested measures do not substantially contribute to the clarification of the GDPR's "suitable and specific measures" requirement because they remain vague or broad in nature and encompass all types of data processing. However, the themes oversight and review mechanisms and public engagement and participation provide valuable insights which can be put to practice. Nevertheless, further clarification of the measures and safeguards that should be installed when invoking the research exemption remains necessary.
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Affiliation(s)
- Julie-Anne R Smit
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Menno Mostert
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Rieke van der Graaf
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Diederick E Grobbee
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Johannes J M van Delden
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
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Gehrmann J, Herczog E, Decker S, Beyan O. What prevents us from reusing medical real-world data in research. Sci Data 2023; 10:459. [PMID: 37443164 DOI: 10.1038/s41597-023-02361-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023] Open
Affiliation(s)
- Julia Gehrmann
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Institute for Biomedical Informatics, Cologne, Germany.
| | | | - Stefan Decker
- Chair of Computer Science 5, RWTH Aachen University, Aachen, Germany
- Department of Data Science and Artificial Intelligence, Fraunhofer FIT, Sankt Augustin, Germany
| | - Oya Beyan
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Institute for Biomedical Informatics, Cologne, Germany
- Department of Data Science and Artificial Intelligence, Fraunhofer FIT, Sankt Augustin, Germany
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6
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Meszaros J, Minari J, Huys I. The future regulation of artificial intelligence systems in healthcare services and medical research in the European Union. Front Genet 2022; 13:927721. [PMID: 36267404 PMCID: PMC9576843 DOI: 10.3389/fgene.2022.927721] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 07/06/2022] [Indexed: 11/13/2022] Open
Abstract
Despite its promising future, the application of artificial intelligence (AI) and automated decision-making in healthcare services and medical research faces several legal and ethical hurdles. The European Union (EU) is tackling these issues with the existing legal framework and drafting new regulations, such as the proposed AI Act. The EU General Data Protection Regulation (GDPR) partly regulates AI systems, with rules on processing personal data and protecting data subjects against solely automated decision-making. In healthcare services, (automated) decisions are made more frequently and rapidly. However, medical research focuses on innovation and efficacy, with less direct decisions on individuals. Therefore, the GDPR’s restrictions on solely automated decision-making apply mainly to healthcare services, and the rights of patients and research participants may significantly differ. The proposed AI Act introduced a risk-based approach to AI systems based on the principles of ethical AI. We analysed the complex connection between the GDPR and AI Act, highlighting the main issues and finding ways to harmonise the principles of data protection and ethical AI. The proposed AI Act may complement the GDPR in healthcare services and medical research. Although several years may pass before the AI Act comes into force, many of its goals will be realised before that.
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Affiliation(s)
- Janos Meszaros
- Division of Clinical Pharmacology and Pharmacotherapy, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
- Centre for IT and IP Law (CiTiP), KU Leuven, Leuven, Belgium
- *Correspondence: Janos Meszaros,
| | - Jusaku Minari
- Uehiro Research Division for iPS Cell Ethics, Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan
| | - Isabelle Huys
- Division of Clinical Pharmacology and Pharmacotherapy, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
- Centre for IT and IP Law (CiTiP), KU Leuven, Leuven, Belgium
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Roman-Belmonte JM, De la Corte-Rodriguez H, Rodriguez-Merchan EC, Vazquez-Sasot A, Rodriguez-Damiani BA, Resino-Luís C, Sanchez-Laguna F. The three horizons model applied to medical science. Postgrad Med 2022; 134:776-783. [DOI: 10.1080/00325481.2022.2124086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- Juan M. Roman-Belmonte
- Department of Physical Medicine and Rehabilitation, Cruz Roja San José y Santa Adela University Hospital, Madrid, Spain
| | | | - E. Carlos Rodriguez-Merchan
- Department of Orthopedic Surgery, La Paz University Hospital, Madrid, Spain
- Osteoarticular Surgery Research, Hospital La Paz Institute for Health Research – IdiPAZ (La Paz University Hospital – Autonomous University of Madrid), Madrid, Spain
| | - Aranzazu Vazquez-Sasot
- Department of Physical Medicine and Rehabilitation, Cruz Roja San José y Santa Adela University Hospital, Madrid, Spain
| | - Beatriz A. Rodriguez-Damiani
- Department of Physical Medicine and Rehabilitation, Cruz Roja San José y Santa Adela University Hospital, Madrid, Spain
| | - Cristina Resino-Luís
- Department of Physical Medicine and Rehabilitation, Cruz Roja San José y Santa Adela University Hospital, Madrid, Spain
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8
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Muller SHA, Kalkman S, van Thiel GJMW, Mostert M, van Delden JJM. The social licence for data-intensive health research: towards co-creation, public value and trust. BMC Med Ethics 2021; 22:110. [PMID: 34376204 PMCID: PMC8353823 DOI: 10.1186/s12910-021-00677-5] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 08/03/2021] [Indexed: 11/10/2022] Open
Abstract
Background The rise of Big Data-driven health research challenges the assumed contribution of medical research to the public good, raising questions about whether the status of such research as a common good should be taken for granted, and how public trust can be preserved. Scandals arising out of sharing data during medical research have pointed out that going beyond the requirements of law may be necessary for sustaining trust in data-intensive health research. We propose building upon the use of a social licence for achieving such ethical governance. Main text We performed a narrative review of the social licence as presented in the biomedical literature. We used a systematic search and selection process, followed by a critical conceptual analysis. The systematic search resulted in nine publications. Our conceptual analysis aims to clarify how societal permission can be granted to health research projects which rely upon the reuse and/or linkage of health data. These activities may be morally demanding. For these types of activities, a moral legitimation, beyond the limits of law, may need to be sought in order to preserve trust. Our analysis indicates that a social licence encourages us to recognise a broad range of stakeholder interests and perspectives in data-intensive health research. This is especially true for patients contributing data. Incorporating such a practice paves the way towards an ethical governance, based upon trust. Public engagement that involves patients from the start is called for to strengthen this social licence. Conclusions There are several merits to using the concept of social licence as a guideline for ethical governance. Firstly, it fits the novel scale of data-related risks; secondly, it focuses attention on trustworthiness; and finally, it offers co-creation as a way forward. Greater trust can be achieved in the governance of data-intensive health research by highlighting strategic dialogue with both patients contributing the data, and the public in general. This should ultimately contribute to a more ethical practice of governance. Supplementary Information The online version contains supplementary material available at 10.1186/s12910-021-00677-5.
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Affiliation(s)
- Sam H A Muller
- Department of Medical Humanities, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584 CX, Utrecht, The Netherlands.
| | - Shona Kalkman
- Department of Medical Humanities, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584 CX, Utrecht, The Netherlands
| | - Ghislaine J M W van Thiel
- Department of Medical Humanities, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584 CX, Utrecht, The Netherlands
| | - Menno Mostert
- Department of Medical Humanities, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584 CX, Utrecht, The Netherlands
| | - Johannes J M van Delden
- Department of Medical Humanities, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584 CX, Utrecht, The Netherlands
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Wehrens R, Sihag V, Sülz S, van Elten H, van Raaij E, de Bont A, Weggelaar-Jansen AM. Understanding the Uptake of Big Data in Health Care: Protocol for a Multinational Mixed-Methods Study. JMIR Res Protoc 2020; 9:e16779. [PMID: 33090113 PMCID: PMC7644380 DOI: 10.2196/16779] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 07/17/2020] [Accepted: 07/21/2020] [Indexed: 11/25/2022] Open
Abstract
Background Despite the high potential of big data, their applications in health care face many organizational, social, financial, and regulatory challenges. The societal dimensions of big data are underrepresented in much medical research. Little is known about integrating big data applications in the corporate routines of hospitals and other care providers. Equally little is understood about embedding big data applications in daily work practices and how they lead to actual improvements for health care actors, such as patients, care professionals, care providers, information technology companies, payers, and the society. Objective This planned study aims to provide an integrated analysis of big data applications, focusing on the interrelations among concrete big data experiments, organizational routines, and relevant systemic and societal dimensions. To understand the similarities and differences between interactions in various contexts, the study covers 12 big data pilot projects in eight European countries, each with its own health care system. Workshops will be held with stakeholders to discuss the findings, our recommendations, and the implementation. Dissemination is supported by visual representations developed to share the knowledge gained. Methods This study will utilize a mixed-methods approach that combines performance measurements, interviews, document analysis, and cocreation workshops. Analysis will be structured around the following four key dimensions: performance, embedding, legitimation, and value creation. Data and their interrelations across the dimensions will be synthesized per application and per country. Results The study was funded in August 2017. Data collection started in April 2018 and will continue until September 2021. The multidisciplinary focus of this study enables us to combine insights from several social sciences (health policy analysis, business administration, innovation studies, organization studies, ethics, and health services research) to advance a holistic understanding of big data value realization. The multinational character enables comparative analysis across the following eight European countries: Austria, France, Germany, Ireland, the Netherlands, Spain, Sweden, and the United Kingdom. Given that national and organizational contexts change over time, it will not be possible to isolate the factors and actors that explain the implementation of big data applications. The visual representations developed for dissemination purposes will help to reduce complexity and clarify the relations between the various dimensions. Conclusions This study will develop an integrated approach to big data applications that considers the interrelations among concrete big data experiments, organizational routines, and relevant systemic and societal dimensions. International Registered Report Identifier (IRRID) DERR1-10.2196/16779
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Affiliation(s)
- Rik Wehrens
- Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, Netherlands
| | - Vikrant Sihag
- Rotterdam School of Management, Erasmus University Rotterdam, Rotterdam, Netherlands.,Department of Industrial Engineering & Innovation Sciences, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Sandra Sülz
- Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, Netherlands
| | - Hilco van Elten
- Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, Netherlands
| | - Erik van Raaij
- Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, Netherlands.,Rotterdam School of Management, Erasmus University Rotterdam, Rotterdam, Netherlands
| | - Antoinette de Bont
- Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, Netherlands
| | - Anne Marie Weggelaar-Jansen
- Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, Netherlands.,School of Medical Physics and Engineering, University of Technology Eindhoven, Eindhoven, Netherlands
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Cumyn A, Barton A, Dault R, Cloutier A, Jalbert R, Ethier J. Informed consent within a learning health system: A scoping review. Learn Health Syst 2020; 4:e10206. [PMID: 32313834 PMCID: PMC7156861 DOI: 10.1002/lrh2.10206] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 09/18/2019] [Accepted: 10/08/2019] [Indexed: 12/27/2022] Open
Abstract
INTRODUCTION A major consideration for the implementation of a learning health system (LHS) is consent from participants to the use of their data for research purposes. The main objective of this paper was to identify in the literature which types of consent have been proposed for participation in research observational activities in a LHS. We were particularly interested in understanding which approaches were seen as most feasible and acceptable and in which context, in order to inform the development of a Quebec-based LHS. METHODS Using a scoping review methodology, we searched scientific and legal databases as well as the gray literature using specific terms. Full-text articles were reviewed independently by two authors on the basis of the following concepts: (a) LHS and (b) approach to consent. The selected papers were imported in NVivo software for analysis in the light of a conceptual framework that distinguishes various, largely independent dimensions of consent. RESULTS A total of 93 publications were analysed for this review. Several studies reach opposing conclusions concerning the best approach to consent within a LHS. However, in the light of the conceptual framework we developed, we found that many of these results are distorted by the conflation between various characteristics of consent. Thus, when these characteristics are distinguished, the results mainly suggest the prime importance of the communication process, by contrast to the scope of consent or the kind of action required by participants (opt-in/opt-out). We identified two models of consent that were especially relevant for our purpose: metaconsent and dynamic consent. CONCLUSIONS Our review shows the importance of distinguishing carefully the various features of the consent process. It also suggests that the metaconsent model is a valuable model within a LHS, as it addresses many of the issues raised with regards to feasibility and acceptability. We propose to complement this model by adding the modalities of the information process to the dimensions relevant in the metaconsent process.
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Affiliation(s)
- Annabelle Cumyn
- Département de médecine, Faculté de médecine et des sciences de la santéUniversité de SherbrookeQuebecCanada
- Groupe de recherche interdisciplinaire en informatique de la santé (GRIIS), Faculté de médecine et des sciences de la santé/Faculté des sciencesUniversité de SherbrookeQuebecCanada
| | - Adrien Barton
- Groupe de recherche interdisciplinaire en informatique de la santé (GRIIS), Faculté de médecine et des sciences de la santé/Faculté des sciencesUniversité de SherbrookeQuebecCanada
- Centre national de la recherche scientifique ‐ Institut de recherche en informatique de Toulouse (CNRS‐IRIT)ToulouseFrance
| | - Roxanne Dault
- Groupe de recherche interdisciplinaire en informatique de la santé (GRIIS), Faculté de médecine et des sciences de la santé/Faculté des sciencesUniversité de SherbrookeQuebecCanada
| | - Anne‐Marie Cloutier
- Groupe de recherche interdisciplinaire en informatique de la santé (GRIIS), Faculté de médecine et des sciences de la santé/Faculté des sciencesUniversité de SherbrookeQuebecCanada
| | - Rosalie Jalbert
- Groupe de recherche interdisciplinaire en informatique de la santé (GRIIS), Faculté de médecine et des sciences de la santé/Faculté des sciencesUniversité de SherbrookeQuebecCanada
| | - Jean‐François Ethier
- Département de médecine, Faculté de médecine et des sciences de la santéUniversité de SherbrookeQuebecCanada
- Groupe de recherche interdisciplinaire en informatique de la santé (GRIIS), Faculté de médecine et des sciences de la santé/Faculté des sciencesUniversité de SherbrookeQuebecCanada
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11
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Cohen JA, Trojano M, Mowry EM, Uitdehaag BMJ, Reingold SC, Marrie RA. Leveraging real-world data to investigate multiple sclerosis disease behavior, prognosis, and treatment. Mult Scler 2020; 26:23-37. [PMID: 31778094 PMCID: PMC6950891 DOI: 10.1177/1352458519892555] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 11/10/2019] [Indexed: 12/21/2022]
Abstract
Randomized controlled clinical trials and real-world observational studies provide complementary information but with different validity. Some clinical questions (disease behavior, prognosis, validation of outcome measures, comparative effectiveness, and long-term safety of therapies) are often better addressed using real-world data reflecting larger, more representative populations. Integration of disease history, clinician-reported outcomes, performance tests, and patient-reported outcome measures during patient encounters; imaging and biospecimen analyses; and data from wearable devices increase dataset utility. However, observational studies utilizing these data are susceptible to many potential sources of bias, creating barriers to acceptance by regulatory agencies and the medical community. Therefore, data standardization and validation within datasets, harmonization across datasets, and application of appropriate analysis methods are important considerations. We review approaches to improve the scope, quality, and analyses of real-world data to advance understanding of multiple sclerosis and its treatment, as an example of opportunities to better support patient care and research.
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Affiliation(s)
- Jeffrey A Cohen
- Department of Neurology, Mellen Center for
Multiple Sclerosis Treatment and Research, Neurological Institute, Cleveland
Clinic, Cleveland, OH, USA
| | - Maria Trojano
- Department of Basic Medical Sciences,
Neurosciences and Sense Organs, University of Bari “Aldo Moro,” Bari,
Italy
| | - Ellen M Mowry
- Department of Neurology, School of Medicine, The
Johns Hopkins University, Baltimore, MD, USA
| | - Bernard MJ Uitdehaag
- Department of Neurology, Amsterdam University
Medical Center, Amsterdam, The Netherlands
| | | | - Ruth Ann Marrie
- Departments of Internal Medicine (Neurology) and
Community Health Sciences, Rady Faculty of Health Sciences, Max Rady College
of Medicine, University of Manitoba, Winnipeg, MB, Canada
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McLachlan S, Dube K, Johnson O, Buchanan D, Potts HW, Gallagher T, Fenton N. A framework for analysing learning health systems: Are we removing the most impactful barriers? Learn Health Syst 2019; 3:e10189. [PMID: 31641685 PMCID: PMC6802533 DOI: 10.1002/lrh2.10189] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 02/01/2019] [Accepted: 03/05/2019] [Indexed: 01/18/2023] Open
Abstract
INTRODUCTION Learning health systems (LHS) are one of the major computing advances in health care. However, no prior research has systematically analysed barriers and facilitators for LHS. This paper presents an investigation into the barriers, benefits, and facilitating factors for LHS in order to create a basis for their successful implementation and adoption. METHODS First, the ITPOSMO-BBF framework was developed based on the established ITPOSMO (information, technology, processes, objectives, staffing, management, and other factors) framework, extending it for analysing barriers, benefits, and facilitators. Second, the new framework was applied to LHS. RESULTS We found that LHS shares similar barriers and facilitators with electronic health records (EHR); in particular, most facilitator effort in implementing EHR and LHS goes towards barriers categorised as human factors, even though they were seen to carry fewer benefits. Barriers whose resolution would bring significant benefits in safety, quality, and health outcomes remain.LHS envisage constant generation of new clinical knowledge and practice based on the central role of collections of EHR. Once LHS are constructed and operational, they trigger new data streams into the EHR. So LHS and EHR have a symbiotic relationship. The implementation and adoption of EHRs have proved and continues to prove challenging, and there are many lessons for LHS arising from these challenges. CONCLUSIONS Successful adoption of LHS should take account of the framework proposed in this paper, especially with respect to its focus on removing barriers that have the most impact.
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Affiliation(s)
- Scott McLachlan
- Electrical Engineering and Computer ScienceQueen Mary University of LondonLondonUK
| | - Kudakwashe Dube
- Fundamental SciencesMassey UniversityPalmerston NorthNew Zealand
| | | | - Derek Buchanan
- Fundamental SciencesMassey UniversityPalmerston NorthNew Zealand
| | - Henry W.W. Potts
- Institute of Health InformaticsUniversity College LondonLondonUK
| | | | - Norman Fenton
- Electrical Engineering and Computer ScienceQueen Mary University of LondonLondonUK
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Schofield PN, Kulka U, Tapio S, Grosche B. Big data in radiation biology and epidemiology; an overview of the historical and contemporary landscape of data and biomaterial archives. Int J Radiat Biol 2019; 95:861-878. [DOI: 10.1080/09553002.2019.1589026] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Paul N. Schofield
- Department of Physiology, Development, and Neuroscience, University of Cambridge, Cambridge, UK
| | - Ulrike Kulka
- Bundesamt fuer Strahlenschutz, Neuherberg, Germany
| | - Soile Tapio
- Helmholtz Zentrum Muenchen, German Research Center for Environmental Health GmbH, Institute of Radiation Biology, Neuherberg, Germany
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Nurmi SM, Kangasniemi M, Halkoaho A, Pietilä AM. Privacy of Clinical Research Subjects: An Integrative Literature Review. J Empir Res Hum Res Ethics 2018; 14:33-48. [PMID: 30353779 DOI: 10.1177/1556264618805643] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
With changes in clinical research practice, the importance of a study-subject's privacy and the confidentiality of their personal data is growing. However, the body of research is fragmented, and a synthesis of work in this area is lacking. Accordingly, an integrative review was performed, guided by Whittemore and Knafl's work. Data from PubMed, Scopus, and CINAHL searches from January 2012 to February 2017 were analyzed via the constant comparison method. From 16 empirical and theoretical studies, six topical aspects were identified: the evolving nature of health data in clinical research, sharing of health data, the challenges of anonymizing data, collaboration among stakeholders, the complexity of regulation, and ethics-related tension between social benefits and privacy. Study subjects' privacy is an increasingly important ethics principle for clinical research, and privacy protection is rendered even more challenging by changing research practice.
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Affiliation(s)
| | | | - Arja Halkoaho
- 2 Kuopio University Hospital, Finland.,3 Tampere University of Applied Sciences, Finland
| | - Anna-Maija Pietilä
- 1 University of Eastern Finland, Kuopio, Finland.,4 Social and Health Care Services, Kuopio, Finland
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