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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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Kulasegaram KM, Grierson L, Barber C, Chahine S, Chou FC, Cleland J, Ellis R, Holmboe ES, Pusic M, Schumacher D, Tolsgaard MG, Tsai CC, Wenghofer E, Touchie C. Data sharing and big data in health professions education: Ottawa consensus statement and recommendations for scholarship. MEDICAL TEACHER 2024; 46:471-485. [PMID: 38306211 DOI: 10.1080/0142159x.2023.2298762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 12/20/2023] [Indexed: 02/04/2024]
Abstract
Changes in digital technology, increasing volume of data collection, and advances in methods have the potential to unleash the value of big data generated through the education of health professionals. Coupled with this potential are legitimate concerns about how data can be used or misused in ways that limit autonomy, equity, or harm stakeholders. This consensus statement is intended to address these issues by foregrounding the ethical imperatives for engaging with big data as well as the potential risks and challenges. Recognizing the wide and ever evolving scope of big data scholarship, we focus on foundational issues for framing and engaging in research. We ground our recommendations in the context of big data created through data sharing across and within the stages of the continuum of the education and training of health professionals. Ultimately, the goal of this statement is to support a culture of trust and quality for big data research to deliver on its promises for health professions education (HPE) and the health of society. Based on expert consensus and review of the literature, we report 19 recommendations in (1) framing scholarship and research through research, (2) considering unique ethical practices, (3) governance of data sharing collaborations that engage stakeholders, (4) data sharing processes best practices, (5) the importance of knowledge translation, and (6) advancing the quality of scholarship through multidisciplinary collaboration. The recommendations were modified and refined based on feedback from the 2022 Ottawa Conference attendees and subsequent public engagement. Adoption of these recommendations can help HPE scholars share data ethically and engage in high impact big data scholarship, which in turn can help the field meet the ultimate goal: high-quality education that leads to high-quality healthcare.
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Affiliation(s)
| | - Lawrence Grierson
- Department of Family Medicine, McMaster University, Hamilton, Canada
| | - Cassandra Barber
- School of Health Professions Education, Maastricht University, Maastricht, Netherlands
| | - Saad Chahine
- Faculty of Education, Queen's University, Kingston, Canada
| | - Fremen Chichen Chou
- Faculty of Education, Center for Faculty Development, China Medical University Hospital, Taichung City, Taiwan
| | - Jennifer Cleland
- Director of Medical Education Research & Scholarship Unit, Lee Kong Chian School of Medicine, Singapore
| | | | - Eric S Holmboe
- Accreditation Council for Graduate Medical Education, Chicago, IL, USA
| | | | - Daniel Schumacher
- Cincinnati Children's Hospital Medical Center/University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Martin G Tolsgaard
- Copenhagen Academy for Medical Education and Simulation, University of Copenhagen, Copenhagen, Denmark
| | - Chin-Chung Tsai
- Program of Learning Sciences, National Taiwan Normal University, Taipei, Taiwan
| | - Elizabeth Wenghofer
- School of Kinesiology and Health Sciences, Laurentian University, Sudbury, Canada
| | - Claire Touchie
- University of Ottawa/The Ottawa Hospital, Ottawa, Canada
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Schröder M, Muller SH, Vradi E, Mielke J, Lim YM, Couvelard F, Mostert M, Koudstaal S, Eijkemans MJ, Gerlinger C. Sharing Medical Big Data While Preserving Patient Confidentiality in Innovative Medicines Initiative: A Summary and Case Report from BigData@Heart. BIG DATA 2023; 11:399-407. [PMID: 37889577 PMCID: PMC10733752 DOI: 10.1089/big.2022.0178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/29/2023]
Abstract
Sharing individual patient data (IPD) is a simple concept but complex to achieve due to data privacy and data security concerns, underdeveloped guidelines, and legal barriers. Sharing IPD is additionally difficult in big data-driven collaborations such as Bigdata@Heart in the Innovative Medicines Initiative, due to competing interests between diverse consortium members. One project within BigData@Heart, case study 1, needed to pool data from seven heterogeneous data sets: five randomized controlled trials from three different industry partners, and two disease registries. Sharing IPD was not considered feasible due to legal requirements and the sensitive medical nature of these data. In addition, harmonizing the data sets for a federated data analysis was difficult due to capacity constraints and the heterogeneity of the data sets. An alternative option was to share summary statistics through contingency tables. Here it is demonstrated that this method along with anonymization methods to ensure patient anonymity had minimal loss of information. Although sharing IPD should continue to be encouraged and strived for, our approach achieved a good balance between data transparency while protecting patient privacy. It also allowed a successful collaboration between industry and academia.
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Affiliation(s)
- Megan Schröder
- The Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Ludwig-Maximilians-Universität München, Münich, Germany
| | - Sam H.A. Muller
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Eleni Vradi
- Biomedical Data Science II, Bayer AG, Berlin, Germany
| | - Johanna Mielke
- Research and Early Development, Bayer AG, Wuppertal, Germany
| | - Yvonne M.F. Lim
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Institute for Clinical Research, National Institutes of Health, Selangor, Malaysia
| | - Fabrice Couvelard
- Institut de Recherches Internationales SERVIER (I.R.I.S.), Suresnes, France
| | - Menno Mostert
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Stefan Koudstaal
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Cardiology, Groene Hart Ziekenhuis, Gouda, The Netherlands
| | - Marinus J.C. Eijkemans
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Christoph Gerlinger
- Clinical Statistics and Data Insights, Bayer AG, Berlin, Germany
- Department of Gynecology, Obstetrics and Reproductive Medicine, University Medical School of Saarland, Homburg/Saar, Germany
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4
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Bradshaw A, Hughes N, Vallez-Garcia D, Chokoshvili D, Owens A, Hansen C, Emmert K, Maetzler W, Killin L, Barnes R, Brookes AJ, Visser PJ, Hofmann-Apitius M, Diaz C, Steukers L. Data sharing in neurodegenerative disease research: challenges and learnings from the innovative medicines initiative public-private partnership model. Front Neurol 2023; 14:1187095. [PMID: 37545729 PMCID: PMC10397390 DOI: 10.3389/fneur.2023.1187095] [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: 03/15/2023] [Accepted: 06/02/2023] [Indexed: 08/08/2023] Open
Abstract
Efficient data sharing is hampered by an array of organizational, ethical, behavioral, and technical challenges, slowing research progress and reducing the utility of data generated by clinical research studies on neurodegenerative diseases. There is a particular need to address differences between public and private sector environments for research and data sharing, which have varying standards, expectations, motivations, and interests. The Neuronet data sharing Working Group was set up to understand the existing barriers to data sharing in public-private partnership projects, and to provide guidance to overcome these barriers, by convening data sharing experts from diverse projects in the IMI neurodegeneration portfolio. In this policy and practice review, we outline the challenges and learnings of the WG, providing the neurodegeneration community with examples of good practices and recommendations on how to overcome obstacles to data sharing. These obstacles span organizational issues linked to the unique structure of cross-sectoral, collaborative research initiatives, to technical issues that affect the storage, structure and annotations of individual datasets. We also identify sociotechnical hurdles, such as academic recognition and reward systems that disincentivise data sharing, and legal challenges linked to heightened perceptions of data privacy risk, compounded by a lack of clear guidance on GDPR compliance mechanisms for public-private research. Focusing on real-world, neuroimaging and digital biomarker data, we highlight particular challenges and learnings for data sharing, such as data management planning, development of ethical codes of conduct, and harmonization of protocols and curation processes. Cross-cutting solutions and enablers include the principles of transparency, standardization and co-design - from open, accessible metadata catalogs that enhance findability of data, to measures that increase visibility and trust in data reuse.
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Affiliation(s)
| | | | - David Vallez-Garcia
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | | | - Andrew Owens
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Clint Hansen
- Department of Neurology, University Hospital Schleswig-Holstein, Campus Kiel and Kiel University, Kiel, Germany
| | - Kirsten Emmert
- Department of Neurology, University Hospital Schleswig-Holstein, Campus Kiel and Kiel University, Kiel, Germany
| | - Walter Maetzler
- Department of Neurology, University Hospital Schleswig-Holstein, Campus Kiel and Kiel University, Kiel, Germany
| | - Lewis Killin
- Synapse Research Management Partners, Barcelona, Spain
| | | | - Anthony J. Brookes
- Department of Genetics and Genome Biology, University of Leicester, Leicester, United Kingdom
| | - Pieter Jelle Visser
- Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, University of Maastricht, Maastricht, Netherlands
| | | | - Carlos Diaz
- Synapse Research Management Partners, Barcelona, Spain
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Shukla PK, Stalin S, Joshi S, Shukla PK, Pareek PK. Optimization assisted bidirectional gated recurrent unit for healthcare monitoring system in big-data. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
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6
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Muller SHA, van Thiel GJMW, Mostert M, van Delden JJM. Dynamic consent, communication and return of results in large-scale health data reuse: Survey of public preferences. Digit Health 2023; 9:20552076231190997. [PMID: 37599899 PMCID: PMC10434987 DOI: 10.1177/20552076231190997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 07/13/2023] [Indexed: 08/22/2023] Open
Abstract
Dynamic consent forms a comprehensive, tailored approach for interacting with research participants. We conducted a survey study to inquire how research participants evaluate the elements of consent, information provision, communication and return of results within dynamic consent in a hypothetical health data reuse scenario. We distributed a digital questionnaire among a purposive sample of patient panel members. Data were analysed using descriptive and nonparametric inferential statistics. Respondents favoured the potential to manage changing consent preferences over time. There was much agreement between people favouring closer and more specific control over data reuse approval and those in favour of broader approval, facilitated by an opt-out system or an independent data reuse committee. People want to receive more information about reuse, outcomes and return of results. Respondents supported an interactive model of research participation, welcoming regular, diverse and interactive forms of communication, like a digital communication platform. Approval for reuse and providing meaningful information, including meaningful return of results, are intricately related to facilitating better communication. Respondents favoured return of actionable research results. These findings emphasize the potential of dynamic consent for enabling participants to maintain control over how their data are being used for which purposes by whom. Allowing different options to shape a dynamic consent interface in health data reuse in a personalized manner is pivotal to accommodate plurality in a flexible though robust manner. Interaction via dynamic consent enables participants to tailor the elements of participation they deem relevant to their own preferences, engaging diverse perspectives, interests and preferences.
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Affiliation(s)
- Sam HA Muller
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Ghislaine JMW van Thiel
- 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
| | - Johannes JM van Delden
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
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7
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Muller SHA, van Thiel GJMW, Vrana M, Mostert M, van Delden JJM. Patients' and Publics' Preferences for Data-Intensive Health Research Governance: Survey Study. JMIR Hum Factors 2022; 9:e36797. [PMID: 36069794 PMCID: PMC9494211 DOI: 10.2196/36797] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 05/18/2022] [Accepted: 07/18/2022] [Indexed: 11/28/2022] Open
Abstract
Background Patients and publics are generally positive about data-intensive health research. However, conditions need to be fulfilled for their support. Ensuring confidentiality, security, and privacy of patients’ health data is pivotal. Patients and publics have concerns about secondary use of data by commercial parties and the risk of data misuse, reasons for which they favor personal control of their data. Yet, the potential of public benefit highlights the potential of building trust to attenuate these perceptions of harm and risk. Nevertheless, empirical evidence on how conditions for support of data-intensive health research can be operationalized to that end remains scant. Objective This study aims to inform efforts to design governance frameworks for data-intensive health research, by gaining insight into the preferences of patients and publics for governance policies and measures. Methods We distributed a digital questionnaire among a purposive sample of patients and publics. Data were analyzed using descriptive statistics and nonparametric inferential statistics to compare group differences and explore associations between policy preferences. Results Study participants (N=987) strongly favored sharing their health data for scientific health research. Personal decision-making about which research projects health data are shared with (346/980, 35.3%), which researchers/organizations can have access (380/978, 38.9%), and the provision of information (458/981, 46.7%) were found highly important. Health data–sharing policies strengthening direct personal control, like being able to decide under which conditions health data are shared (538/969, 55.5%), were found highly important. Policies strengthening collective governance, like reliability checks (805/967, 83.2%) and security safeguards (787/976, 80.6%), were also found highly important. Further analysis revealed that participants willing to share health data, to a lesser extent, demanded policies strengthening direct personal control than participants who were reluctant to share health data. This was the case for the option to have health data deleted at any time (P<.001) and the ability to decide the conditions under which health data can be shared (P<.001). Overall, policies and measures enforcing conditions for support at the collective level of governance, like having an independent committee to evaluate requests for access to health data (P=.02), were most strongly favored. This also applied to participants who explicitly stressed that it was important to be able to decide the conditions under which health data can be shared, for instance, whether sanctions on data misuse are in place (P=.03). Conclusions This study revealed that both a positive attitude toward health data sharing and demand for personal decision-making abilities were associated with policies and measures strengthening control at the collective level of governance. We recommend pursuing the development of this type of governance policy. More importantly, further study is required to understand how governance policies and measures can contribute to the trustworthiness of data-intensive health research.
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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, Utrecht, 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, Utrecht, Netherlands
| | | | - Menno Mostert
- Department of Medical Humanities, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Johannes J M van Delden
- Department of Medical Humanities, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
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8
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Nakazawa E, Fukushi T, Tachibana K, Uehara R, Arie F, Akter N, Maruyama M, Morita K, Araki T, Sadato N. The way forward for neuroethics in Japan: A review of five topics surrounding present challenges. Neurosci Res 2022; 183:7-16. [PMID: 35882301 DOI: 10.1016/j.neures.2022.07.006] [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: 12/23/2021] [Revised: 06/20/2022] [Accepted: 07/20/2022] [Indexed: 11/30/2022]
Abstract
Neuroethics is the study of how neuroscience impacts humans and society. About 15 years have passed since neuroethics was introduced to Japan, yet the field of neuroethics still seeks developed methodologies and an established academic identity. In light of progress in neuroscience and neurotechnology, the challenges for Japanese neuroethics in the 2020s can be categorized into five topics. (1) The need for further research into the importance of informed consent in psychiatric research and the promotion of public-patient engagement. (2) The need for a framework that constructs a global environment for neuroscience research that utilizes reliable samples and data. (3) The need for ethical support within a Japanese context regarding the construction of brain banks and the research surrounding their use. It is also important to reconsider the moral value of the human neural system and make comparisons with non-human primates. (4) An urgent need to study neuromodulation technologies that intervene in emotions. (5) The need to reconsider neuroscience and neurotechnology from social points of view. Rules for neuroenhancements and do-it-yourself neurotechnologies are urgently needed, while from a broader perspective, it is essential to study the points of contact between neuroscience and public health.
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Affiliation(s)
- Eisuke Nakazawa
- The University of Tokyo, Department of Biomedical Ethics, Faculty of Medicine, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033 Japan.
| | - Tamami Fukushi
- Japan Agency for Medical Research and Development, 1-7-1 Otemachi, Chiyoda-ku, Tokyo 100-0004 Japan; National Institute for Physiological Sciences, National Institutes of Natural Sciences, 38 Nishigonaka Myodaiji, Okazaki-shi, Aichi 444-8585 Japan; Faculty of Human Welfare, Tokyo Online University, Nishi-Shinjuku Shinjuku-ku, Tokyo 160-0023 JAPAN
| | - Koji Tachibana
- Chiba University, Faculty of Humanities, 1-33, Yayoicho, Inage-ku, Chiba-shi, Chiba, 263-8522 Japan; Pellegrino Center for Clinical Bioethics, Georgetown University Medical Center, 4000 Reservoir Rd NW, Washington, DC 20007, United States
| | - Ryo Uehara
- Kansai University, Department of Informatics, 2-1-1 Ryozenjicho, Takatsuki-shi, Osaka 569-1095 Japan
| | - Fumie Arie
- National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira-shi, Tokyo 187-8551, Japan
| | - Nargis Akter
- National Institute for Physiological Sciences, National Institutes of Natural Sciences, 38 Nishigonaka Myodaiji, Okazaki-shi, Aichi 444-8585 Japan
| | - Megumi Maruyama
- National Institute for Physiological Sciences, National Institutes of Natural Sciences, 38 Nishigonaka Myodaiji, Okazaki-shi, Aichi 444-8585 Japan
| | - Kentaro Morita
- Department of Rehabilitation, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655 JAPAN
| | - Toshiyuki Araki
- National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira-shi, Tokyo 187-8551, Japan
| | - Norihiro Sadato
- National Institute for Physiological Sciences, National Institutes of Natural Sciences, 38 Nishigonaka Myodaiji, Okazaki-shi, Aichi 444-8585 Japan; Research Organization of Science and Technology, Ritsumeikan University
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Chavva IR, Crawford AL, Mazurek MH, Yuen MM, Prabhat AM, Payabvash S, Sze G, Falcone GJ, Matouk CC, de Havenon A, Kim JA, Sharma R, Schiff SJ, Rosen MS, Kalpathy-Cramer J, Iglesias Gonzalez JE, Kimberly WT, Sheth KN. Deep Learning Applications for Acute Stroke Management. Ann Neurol 2022; 92:574-587. [PMID: 35689531 DOI: 10.1002/ana.26435] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/27/2022] [Accepted: 06/04/2022] [Indexed: 11/08/2022]
Abstract
Brain imaging is essential to the clinical care of patients with stroke, a leading cause of disability and death worldwide. Whereas advanced neuroimaging techniques offer opportunities for aiding acute stroke management, several factors, including time delays, inter-clinician variability, and lack of systemic conglomeration of clinical information, hinder their maximal utility. Recent advances in deep machine learning (DL) offer new strategies for harnessing computational medical image analysis to inform decision making in acute stroke. We examine the current state of the field for DL models in stroke triage. First, we provide a brief, clinical practice-focused primer on DL. Next, we examine real-world examples of DL applications in pixel-wise labeling, volumetric lesion segmentation, stroke detection, and prediction of tissue fate postintervention. We evaluate recent deployments of deep neural networks and their ability to automatically select relevant clinical features for acute decision making, reduce inter-rater variability, and boost reliability in rapid neuroimaging assessments, and integrate neuroimaging with electronic medical record (EMR) data in order to support clinicians in routine and triage stroke management. Ultimately, we aim to provide a framework for critically evaluating existing automated approaches, thus equipping clinicians with the ability to understand and potentially apply DL approaches in order to address challenges in clinical practice. ANN NEUROL 2022.
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Affiliation(s)
- Isha R Chavva
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Anna L Crawford
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Mercy H Mazurek
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Matthew M Yuen
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | | | - Sam Payabvash
- Department of Radiology, Yale School of Medicine, New Haven, CT
| | - Gordon Sze
- Department of Radiology, Yale School of Medicine, New Haven, CT
| | - Guido J Falcone
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Charles C Matouk
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT
| | - Adam de Havenon
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Jennifer A Kim
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Richa Sharma
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Steven J Schiff
- Departments of Neurosurgery, Engineering Science and Mechanics and Physics, Penn State University, University Park, PA
| | - Matthew S Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA
| | - Juan E Iglesias Gonzalez
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA
| | - W Taylor Kimberly
- Department of Neurology, Division of Neurocritical Care, Massachusetts General Hospital, Boston, MA
| | - Kevin N Sheth
- Department of Neurology, Yale School of Medicine, New Haven, CT
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10
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Benn R, Shaw A. Examining the barriers to accepting big health data from a health marketeer's perspective. Health Mark Q 2021; 40:1-18. [PMID: 34720070 DOI: 10.1080/07359683.2021.1994115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Studies have shown that the sharing of big health data can improve patient management across primary and secondary care sectors. It can also reduce costs and can enhance the medical research process. Unfortunately, many big health data initiatives are being impeded because of a range of complex issues. This study was initiated to identify the said issues and develop a tool for health marketers to use to negate the barriers in big healthcare data projects. The study demonstrates how the Interactive Communication Technology Adoption Model can be operationalized to support qualitative researchers.
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Affiliation(s)
| | - Alan Shaw
- Leeds Business School, Leeds Beckett University, Leeds, UK
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11
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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: 21] [Impact Index Per Article: 7.0] [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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Hulsen T. Sharing Is Caring-Data Sharing Initiatives in Healthcare. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17093046. [PMID: 32349396 PMCID: PMC7246891 DOI: 10.3390/ijerph17093046] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 03/17/2020] [Accepted: 04/24/2020] [Indexed: 02/05/2023]
Abstract
In recent years, more and more health data are being generated. These data come not only from professional health systems, but also from wearable devices. All these 'big data' put together can be utilized to optimize treatments for each unique patient ('precision medicine'). For this to be possible, it is necessary that hospitals, academia and industry work together to bridge the 'valley of death' of translational medicine. However, hospitals and academia often are reluctant to share their data with other parties, even though the patient is actually the owner of his/her own health data. Academic hospitals usually invest a lot of time in setting up clinical trials and collecting data, and want to be the first ones to publish papers on this data. There are some publicly available datasets, but these are usually only shared after study (and publication) completion, which means a severe delay of months or even years before others can analyse the data. One solution is to incentivize the hospitals to share their data with (other) academic institutes and the industry. Here, we show an analysis of the current literature around data sharing, and we discuss five aspects of data sharing in the medical domain: publisher requirements, data ownership, growing support for data sharing, data sharing initiatives and how the use of federated data might be a solution. We also discuss some potential future developments around data sharing, such as medical crowdsourcing and data generalists.
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Affiliation(s)
- Tim Hulsen
- Department of Professional Health Solutions & Services, Philips Research, 5656AE Eindhoven, The Netherlands
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