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Bertelsen PS, Bossen C, Knudsen C, Pedersen AM. Data work and practices in healthcare: A scoping review. Int J Med Inform 2024; 184:105348. [PMID: 38309238 DOI: 10.1016/j.ijmedinf.2024.105348] [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: 09/18/2023] [Revised: 01/17/2024] [Accepted: 01/23/2024] [Indexed: 02/05/2024]
Abstract
CONTEXT In healthcare, digitization has been widespread and profound, entailing a deluge of data. This has spurred ambitions for healthcare to become data-driven to improve efficiency and quality, and within medicine itself to improve diagnosing and treating diseases. The generation and processing of data requires human intervention and work, though this is often not acknowledged. PURPOSE The paper investigates who, where, by which means, and for which purposes data work is conducted which is crucial for healthcare managers and policy makers if ambitions to become data-driven are to succeed. To guide further research, it also provides an overview of existing research on data work and practices. METHODS We conducted a scoping review based on a search for papers including the terms healthcare or health care combined with at least one of the following terms: data work, data worker*, data practice*, data practitioner* in Scopus and Web of Science. 74 papers on data work or practices in healthcare were included. ANALYSIS The 74 papers were coded and analyzed regarding the following themes: the kind of data workers and practitioners, organizational settings, involved technologies, purposes, data work tasks, theories and concepts, and definitions of data work and practice. RESULTS Data work is pervasive in healthcare and conducted by various professions and people and in various contexts. The field researching data work and practices is emerging, with publications spread across multiple venues. and there is a need for more precise definitions of data work. Further, data work and practices are useful concepts that have enabled the exploration of those efforts and tasks in detail. CONCLUSION The research on data work and practices in healthcare is emerging and promising. We call for more research to consolidate the field and to better understand and support the work needed for healthcare to become data-driven.
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Affiliation(s)
| | - Claus Bossen
- Department of Digital Design and Information Studies, Aarhus University, Denmark.
| | - Casper Knudsen
- Department of Sustainability and Planning, Aalborg University, Denmark
| | - Asbjørn M Pedersen
- Department of Digital Design and Information Studies, Aarhus University, Denmark
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2
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Nield L, Thelwell M, Chan A, Choppin S, Marshall S. Patient perceptions of three-dimensional (3D) surface imaging technology and traditional methods used to assess anthropometry. OBESITY PILLARS (ONLINE) 2024; 9:100100. [PMID: 38357215 PMCID: PMC10865393 DOI: 10.1016/j.obpill.2024.100100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/16/2024] [Accepted: 01/17/2024] [Indexed: 02/16/2024]
Abstract
Background Obesity and overweight are commonplace, yet attrition rates in weight management clinics are high. Traditional methods of body measurement may be a deterrent due to invasive and time-consuming measurements and negative experiences of how data are presented back to individuals. Emerging new technologies, such as three-dimensional (3D) surface imaging technology, might provide a suitable alternative. This study aimed to understand acceptability of traditional and 3D surface imaging-based body measures, and whether perceptions differ between population groups. Methods This study used a questionnaire to explore body image, body measurement and shape, followed by a qualitative semi-structured interview and first-hand experience of traditional and 3D surface imaging-based body measures. Results 49 participants responded to the questionnaire and 26 participants attended for the body measurements and interview over a 2-month period. There were 3 main themes from the qualitative data 1) Use of technology, 2) Participant experience, expectations and perceptions and 3) Perceived benefits and uses. Conclusion From this study, 3D-surface imaging appeared to be acceptable to patients as a method for anthropometric measurements, which may reduce anxiety and improve attrition rates in some populations. Further work is required to understand the scalability, and the role and implications of these technologies in weight management practice. (University Research Ethics Committee reference number ER41719941).
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Affiliation(s)
- Lucie Nield
- Advanced Wellbeing Research Centre, Sheffield Hallam University, Olympic Legacy Park, Sheffield, S9 3TU, UK
| | - Michael Thelwell
- Advanced Wellbeing Research Centre, Sheffield Hallam University, Olympic Legacy Park, Sheffield, S9 3TU, UK
| | - Audrey Chan
- Sheffield Business School, City Campus, Sheffield Hallam University, S1 1WB, UK
| | - Simon Choppin
- Advanced Wellbeing Research Centre, Sheffield Hallam University, Olympic Legacy Park, Sheffield, S9 3TU, UK
| | - Steven Marshall
- Sheffield Business School, City Campus, Sheffield Hallam University, S1 1WB, UK
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Carmichael L, Hall W, Boniface M. Personal data store ecosystems in health and social care. Front Public Health 2024; 12:1348044. [PMID: 38384893 PMCID: PMC10880866 DOI: 10.3389/fpubh.2024.1348044] [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: 12/11/2023] [Accepted: 01/12/2024] [Indexed: 02/23/2024] Open
Abstract
This paper considers how the development of personal data store ecosystems in health and social care may offer one person-centered approach to improving the ways in which individual generated and gathered data-e.g., from wearables and other personal monitoring and tracking devices-can be used for wellbeing, individual care, and research. Personal data stores aim to provide safe and secure digital spaces that enable people to self-manage, use, and share personal data with others in a way that aligns with their individual needs and preferences. A key motivation for personal data stores is to give an individual more access and meaningful control over their personal data, and greater visibility over how it is used by others. This commentary discusses meanings and motivations behind the personal data store concept-examples are provided to illustrate the opportunities such ecosystems can offer in health and social care, and associated research and implementation challenges are also examined.
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Affiliation(s)
- Laura Carmichael
- IT Innovation Centre Part of the Digital Health and Biomedical Engineering Research Group, School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom
| | - Wendy Hall
- School of Electronics and Computer Science, Web Science Institute, University of Southampton, Southampton, United Kingdom
| | - Michael Boniface
- IT Innovation Centre Part of the Digital Health and Biomedical Engineering Research Group, School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom
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Li S, Du Y, Meireles C, Song D, Sharma K, Yin Z, Brimhall B, Wang J. Decoding Heterogeneity in Data-Driven Self-Monitoring Adherence Trajectories in Digital Lifestyle Interventions for Weight Loss: A Qualitative Study. RESEARCH SQUARE 2024:rs.3.rs-3854650. [PMID: 38313251 PMCID: PMC10836100 DOI: 10.21203/rs.3.rs-3854650/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
Background Data-driven trajectory modeling is a promising approach for identifying meaningful participant subgroups with various self-monitoring (SM) responses in digital lifestyle interventions. However, there is limited research investigating factors that underlie different subgroups. This qualitative study aimed to investigate factors contributing to participant subgroups with distinct SM trajectory in a digital lifestyle intervention over 6 months. Methods Data were collected from a subset of participants (n = 20) in a 6-month digital lifestyle intervention. Participants were classified into Lower SM Group (n = 10) or a Higher SM (n = 10) subgroup based on their SM adherence trajectories over 6 months. Qualitative data were obtained from semi-structured interviews conducted at 3 months. Data were thematically analyzed using a constant comparative approach. Results Participants were middle-aged (52.9 ± 10.2 years), mostly female (65%), and of Hispanic ethnicity (55%). Four major themes with emerged from the thematic analysis: Acceptance towards SM Technologies, Perceived SM Benefits, Perceived SM Barriers, and Responses When Facing SM Barriers. Participants across both subgroups perceived SM as positive feedback, aiding in diet and physical activity behavior changes. Both groups cited individual and technical barriers to SM, including forgetfulness, the burdensome SM process, and inaccuracy. The Higher SM Group displayed positive problem-solving skills that helped them overcome the SM barriers. In contrast, some in the Lower SM Group felt discouraged from SM. Both subgroups found diet SM particularly challenging, especially due to technical issues such as the inaccurate food database, the time-consuming food entry process in the Fitbit app. Conclusions This study complements findings from our previous quantitative research, which used data-drive trajectory modeling approach to identify distinct participant subgroups in a digital lifestyle based on individuals' 6-month SM adherence trajectories. Our results highlight the potential of enhancing action planning problem solving skills to improve SM adherence in the Lower SM Group. Our findings also emphasize the necessity of addressing the technical issues associated with current diet SM approaches. Overall, findings from our study may inform the development of practical SM improvement strategies in future digital lifestyle interventions. Trial registration The study was pre-registered at ClinicalTrials.gov (NCT05071287) on April 30, 2022.
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Affiliation(s)
- Shiyu Li
- Department of Kinesiology, Pennsylvania State University
| | - Yan Du
- School of Nursing, UT Health San Antonio
| | | | - Dan Song
- College of Nursing, Florida State University
| | | | - Zenong Yin
- Department of Public Health, The University of Texas at San Antonio
| | | | - Jing Wang
- College of Nursing, Florida State University
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Oudin A, Maatoug R, Bourla A, Ferreri F, Bonnot O, Millet B, Schoeller F, Mouchabac S, Adrien V. Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health. J Med Internet Res 2023; 25:e44502. [PMID: 37792430 PMCID: PMC10585447 DOI: 10.2196/44502] [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: 11/22/2022] [Revised: 07/10/2023] [Accepted: 08/21/2023] [Indexed: 10/05/2023] Open
Abstract
The term "digital phenotype" refers to the digital footprint left by patient-environment interactions. It has potential for both research and clinical applications but challenges our conception of health care by opposing 2 distinct approaches to medicine: one centered on illness with the aim of classifying and curing disease, and the other centered on patients, their personal distress, and their lived experiences. In the context of mental health and psychiatry, the potential benefits of digital phenotyping include creating new avenues for treatment and enabling patients to take control of their own well-being. However, this comes at the cost of sacrificing the fundamental human element of psychotherapy, which is crucial to addressing patients' distress. In this viewpoint paper, we discuss the advances rendered possible by digital phenotyping and highlight the risk that this technology may pose by partially excluding health care professionals from the diagnosis and therapeutic process, thereby foregoing an essential dimension of care. We conclude by setting out concrete recommendations on how to improve current digital phenotyping technology so that it can be harnessed to redefine mental health by empowering patients without alienating them.
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Affiliation(s)
- Antoine Oudin
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Pitié-Salpêtrière Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Redwan Maatoug
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Pitié-Salpêtrière Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Alexis Bourla
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
- Medical Strategy and Innovation Department, Clariane, Paris, France
- NeuroStim Psychiatry Practice, Paris, France
| | - Florian Ferreri
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Olivier Bonnot
- Department of Child and Adolescent Psychiatry, Nantes University Hospital, Nantes, France
- Pays de la Loire Psychology Laboratory, Nantes University, Nantes, France
| | - Bruno Millet
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Pitié-Salpêtrière Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Félix Schoeller
- Institute for Advanced Consciousness Studies, Santa Monica, CA, United States
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Stéphane Mouchabac
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Vladimir Adrien
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
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Haase CB, Ajjawi R, Bearman M, Brodersen JB, Risor T, Hoeyer K. Data as symptom: Doctors' responses to patient-provided data in general practice. SOCIAL STUDIES OF SCIENCE 2023; 53:522-544. [PMID: 37096688 PMCID: PMC10363926 DOI: 10.1177/03063127231164345] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
People are increasingly able to generate their own health data through new technologies such as wearables and online symptom checkers. However, generating data is one thing, interpreting them another. General practitioners (GPs) are likely to be the first to help with interpretations. Policymakers in the European Union are investing heavily in infrastructures to provide GPs access to patient measurements. But there may be a disconnect between policy ambitions and the everyday practices of GPs. To investigate this, we conducted semi-structured interviews with 23 Danish GPs. According to the GPs, patients relatively rarely bring data to them. GPs mostly remember three types of patient-generated data that patients bring to them for interpretation: heart and sleep measurements from wearables and results from online symptom checkers. However, they also spoke extensively about data work with patient queries concerning measurements from the GPs' own online Patient Reported Outcome system and online access to laboratory results. We juxtapose GP reflections on these five data types and between policy ambitions and everyday practices. These data require substantial recontextualization work before the GPs ascribe them evidential value and act on them. Even when they perceived as actionable, patient-provided data are not approached as measurements, as suggested by policy frameworks. Rather, GPs treat them as analogous to symptoms-that is to say, GPs treat patient-provided data as subjective evidence rather than authoritative measures. Drawing on Science and Technology Studies (STS) literature,we suggest that GPs must be part of the conversation with policy makers and digital entrepreneurs around when and how to integrate patient-generated data into healthcare infrastructures.
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Affiliation(s)
| | - Rola Ajjawi
- Deakin University, Melbourne, VIC, Australia
| | | | - John Brandt Brodersen
- University of Copenhagen, Copenhagen, Denmark
- Primary Health Care Research Unit, Region Zealand, Denmark
- University of Tromsø, Tromsø, Norway
| | - Torsten Risor
- University of Copenhagen, Copenhagen, Denmark
- University of Tromsø, Tromsø, Norway
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Akyüz K, Goisauf M, Chassang G, Kozera Ł, Mežinska S, Tzortzatou-Nanopoulou O, Mayrhofer MT. Post-identifiability in changing sociotechnological genomic data environments. BIOSOCIETIES 2023:1-28. [PMID: 37359141 PMCID: PMC10042674 DOI: 10.1057/s41292-023-00299-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/13/2023] [Indexed: 03/30/2023]
Abstract
Data practices in biomedical research often rely on standards that build on normative assumptions regarding privacy and involve 'ethics work.' In an increasingly datafied research environment, identifiability gains a new temporal and spatial dimension, especially in regard to genomic data. In this paper, we analyze how genomic identifiability is considered as a specific data issue in a recent controversial case: publication of the genome sequence of the HeLa cell line. Considering developments in the sociotechnological and data environment, such as big data, biomedical, recreational, and research uses of genomics, our analysis highlights what it means to be (re-)identifiable in the postgenomic era. By showing how the risk of genomic identifiability is not a specificity of the HeLa controversy, but rather a systematic data issue, we argue that a new conceptualization is needed. With the notion of post-identifiability as a sociotechnological situation, we show how past assumptions and ideas about future possibilities come together in the case of genomic identifiability. We conclude by discussing how kinship, temporality, and openness are subject to renewed negotiations along with the changing understandings and expectations of identifiability and status of genomic data.
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Affiliation(s)
- Kaya Akyüz
- Department of Science and Technology Studies, University of Vienna, Universitätsstraße 7/Stiege II/6, Stock (NIG), 1010 Vienna, Austria
- BBMRI-ERIC, Graz, Austria
| | - Melanie Goisauf
- Department of Science and Technology Studies, University of Vienna, Universitätsstraße 7/Stiege II/6, Stock (NIG), 1010 Vienna, Austria
- BBMRI-ERIC, Graz, Austria
| | - Gauthier Chassang
- CERPOP, Université de Toulouse, Inserm, Université Paul Sabatier, Toulouse, France
- Plateforme GenoToul Societal “Ethique et Biosciences”, Toulouse, France
| | | | - Signe Mežinska
- Institute of Clinical and Preventive Medicine, University of Latvia, Riga, Latvia
- BBMRI.LV, Riga, Latvia
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Green S, Hillersdal L, Holt J, Hoeyer K, Wadmann S. The practical ethics of repurposing health data: how to acknowledge invisible data work and the need for prioritization. MEDICINE, HEALTH CARE, AND PHILOSOPHY 2023; 26:119-132. [PMID: 36402853 PMCID: PMC9676846 DOI: 10.1007/s11019-022-10128-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/04/2022] [Indexed: 06/16/2023]
Abstract
Throughout the Global North, policymakers invest in large-scale integration of health-data infrastructures to facilitate the reuse of clinical data for administration, research, and innovation. Debates about the ethical implications of data repurposing have focused extensively on issues of patient autonomy and privacy. We suggest that it is time to scrutinize also how the everyday work of healthcare staff is affected by political ambitions of data reuse for an increasing number of purposes, and how different purposes are prioritized. Our analysis builds on ethnographic studies within the Danish healthcare system, which is internationally known for its high degree of digitalization and well-connected data infrastructures. Although data repurposing ought to be relatively seamless in this context, we demonstrate how it involves costs and trade-offs for those who produce and use health data. Even when IT systems and automation strategies are introduced to enhance efficiency and reduce data work, they can end up generating new forms of data work and fragmentation of clinically relevant information. We identify five types of data work related to the production, completion, validation, sorting, and recontextualization of health data. Each of these requires medical expertise and clinical resources. We propose that the implications for these forms of data work should be considered early in the planning stages of initiatives for large-scale data sharing and reuse, such as the European Health Data Space. We believe that political awareness of clinical costs and trade-offs related to such data work can provide better and more informed decisions about data repurposing.
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Affiliation(s)
- Sara Green
- Section for History and Philosophy of Science, Department of Science Education, University of Copenhagen, Niels Bohr Building (NBB), Universitetsparken 5, 2100 Copenhagen Ø, Denmark
| | - Line Hillersdal
- Department of Anthropology, University of Copenhagen, Øster Farimagsgade 5, 1353 Copenhagen K, Denmark
| | - Jette Holt
- Infectious Disease Epidemiology & Prevention, The National Center for Infection Control (CEI), Artillerivej 5, 2300 Copenhagen S, Denmark
| | - Klaus Hoeyer
- Centre for Medical Science and Technology Studies, Department of Public Health, University of Copenhagen, Øster Farigmagsgade 5, 1014 Copenhagen K, Denmark
| | - Sarah Wadmann
- The Danish Center for Social Science Research, VIVE, Herluf Trolles Gade 11, 1052 Copenhagen, Denmark
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Zimmermann BM, Willem T, Bredthauer CJ, Buyx A. Ethical Issues in Social Media Recruitment for Clinical Studies: Ethical Analysis and Framework. J Med Internet Res 2022; 24:e31231. [PMID: 35503247 PMCID: PMC9115665 DOI: 10.2196/31231] [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: 06/14/2021] [Revised: 11/11/2021] [Accepted: 12/02/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Social media recruitment for clinical studies holds the promise of being a cost-effective way of attracting traditionally marginalized populations and promoting patient engagement with researchers and a particular study. However, using social media for recruiting clinical study participants also poses a range of ethical issues. OBJECTIVE This study aims to provide a comprehensive overview of the ethical benefits and risks to be considered for social media recruitment in clinical studies and develop practical recommendations on how to implement these considerations. METHODS On the basis of established principles of clinical ethics and research ethics, we reviewed the conceptual and empirical literature for ethical benefits and challenges related to social media recruitment. From these, we derived a conceptual framework to evaluate the eligibility of social media use for recruitment for a specific clinical study. RESULTS We identified three eligibility criteria for social media recruitment for clinical studies: information and consent, risks for target groups, and recruitment effectiveness. These criteria can be used to evaluate the implementation of a social media recruitment strategy at its planning stage. We have discussed the practical implications of these criteria for researchers. CONCLUSIONS The ethical challenges related to social media recruitment are context sensitive. Therefore, social media recruitment should be planned rigorously, taking into account the target group, the appropriateness of social media as a recruitment channel, and the resources available to execute the strategy.
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Affiliation(s)
- Bettina M Zimmermann
- Institute of History and Ethics in Medicine, School of Medicine, Technical University of Munich, Munich, Germany.,Institute for Biomedical Ethics, University of Basel, Basel, Switzerland
| | - Theresa Willem
- Institute of History and Ethics in Medicine, School of Medicine, Technical University of Munich, Munich, Germany.,Department of Science, Technology and Society, School of Social Sciences and Technology, Technical University of Munich, Munich, Germany
| | - Carl Justus Bredthauer
- Institute of History and Ethics in Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - Alena Buyx
- Institute of History and Ethics in Medicine, School of Medicine, Technical University of Munich, Munich, Germany
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Spranger J, Niederberger M. Big Data in der Gesundheitsförderung und Prävention. PRÄVENTION UND GESUNDHEITSFÖRDERUNG 2022. [PMCID: PMC8247614 DOI: 10.1007/s11553-021-00871-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Zusammenfassung
Hintergrund
Die Nutzung großer und vielfältiger Datenmengen (Big Data) kann zur Gewinnung gesundheitsbezogener Erkenntnisse führen. Die Relevanz untermauern aktuelle Erfordernisse, bspw. in Zusammenhang mit der Digitalisierung, der Gesundheitsversorgung in Ausnahmesituationen und der zunehmenden Bedeutung von Personalisierungsprozessen in der Gesundheitsforschung. Das Potenzial von Big Data zur Erforschung vulnerabler Gruppen ist strittig, jedoch vor dem Hintergrund relativ stabiler sozialbedingter gesundheitlicher Ungleichheit besonders relevant.
Ziel der Arbeit
In der Studie wird untersucht, wie Expert*innen im Bereich der Analyse von Gesundheitsdaten das Potenzial von Big Data in der Gesundheitsförderung und Prävention, insbesondere zur Erforschung vulnerabler Gruppen, einschätzen.
Material und Methode
In einer Delphi-Studie wurden Expert*innen in zwei Runden mit einem Onlinefragebogen befragt, um Konsens und Dissens über das Potenzial von Big Data zu identifizieren.
Ergebnisse und Schlussfolgerung
Aus Sicht der Expert*innen birgt Big Data ein Potenzial für die Gesundheitsförderung und Prävention, insbesondere im klinischen Setting und durch die Personalisierung gesundheitsbezogener Maßnahmen. Vor allem Menschen mit seltenen Erkrankungen und ältere Personen könnten durch Big-Data-Analysen profitieren, bspw. durch beschleunigte Diagnoseprozesse oder personalisierte digitale Gesundheitsanwendungen. Uneinig sind sich die Expert*innen über den Umfang, in welchem es Forschungseinrichtungen, Krankenversicherungen oder Unternehmen, erlaubt sein soll, derartige Daten zu nutzen oder zu teilen.
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Affiliation(s)
- Julia Spranger
- Forschungsmethoden in der Gesundheitsförderung und Prävention, Pädagogische Hochschule Schwäbisch Gmünd, Oberbettringer Straße 200, 73525 Schwäbisch Gmünd, Deutschland
| | - Marlen Niederberger
- Forschungsmethoden in der Gesundheitsförderung und Prävention, Pädagogische Hochschule Schwäbisch Gmünd, Oberbettringer Straße 200, 73525 Schwäbisch Gmünd, Deutschland
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11
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Fiske A, Degelsegger-Márquez A, Marsteurer B, Prainsack B. Value-creation in the health data domain: a typology of what health data help us do. BIOSOCIETIES 2022; 18:1-25. [PMID: 35432575 PMCID: PMC9002030 DOI: 10.1057/s41292-022-00276-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/16/2022] [Indexed: 12/02/2022]
Abstract
It has become a trope to speak of the increasing value of health data in our societies. Such rhetoric is highly performative: it creates expectations, channels and justifies investments in data technologies and infrastructures, and portrays deliberations on political and legal issues as obstacles to the flow of data. Yet, important epistemic and political questions remain unexamined, such as how the value of data is created, what data journeys are envisioned by policies and regulation, and for whom data types are (intended to be) valuable. Drawing on two empirical cases, (a) interviews with physicians on the topic of digital selfcare, and (b) expectations of stakeholders on the use of Real-World Data in clinical trials, as well as existing literature, we propose a typology of what health data help us to do. This typology is intended to foster reflection about the different roles and values that data use unfolds. We conclude by discussing how regulation can better accommodate practices of valuation in the health data domain, with a particular focus on identifying regulatory challenges and opportunities for EU-level policy makers, and how Covid-19 has shed light on new aspects of each case.
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Affiliation(s)
- Amelia Fiske
- Institute of History and Ethics in Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | | | | | - Barbara Prainsack
- Department of Political Science, University of Vienna, Vienna, Austria
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12
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Trupia DV, Mathieu-Fritz A, Duong TA. The Sociological Perspective of Users' Invisible Work: A Qualitative Research Framework for Studying Digital Health Innovations Integration. J Med Internet Res 2021; 23:e25159. [PMID: 34734832 PMCID: PMC8603174 DOI: 10.2196/25159] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 01/22/2021] [Accepted: 07/31/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND When new technology is integrated into a care pathway, it faces resistance due to the changes it introduces into the existing context. To understand the success or failure of digital health innovations, it is necessary to pay attention to the adjustments that users must perform to make them work, by reshaping the context and sometimes by altering the ways in which they perform activities. This adaptation work, most of which remains invisible, constitutes an important factor in the success of innovations and the ways in which they transform care practices. OBJECTIVE This work aims to present a sociological framework for studying new health technology uses through a qualitative analysis of the different types of tasks and activities that users, both health professionals and patients, must perform to integrate these technologies and make them work in their daily routine. METHODS This paper uses a three-part method to structure a theoretical model to study users' invisible work. The first part of the method includes a thematic literature review, previously published by one of the coauthors, of major sociological studies conducted on digital health innovations integration into existing care organizations and practices. The second part extends this review to introduce definitions and applications of the users' invisible work concept. The third part consists of producing a theoretical framework to study the concept according to the different contexts and practices of the users. RESULTS The paper proposes four dimensions (organizational, interactional, practical, and experiential), each composed of a set of criteria that allow a comparative analysis of different users' work according to different health technologies. CONCLUSIONS This framework can be applied both as an analytical tool in a research protocol and as an agenda to identify less visible adoption criteria for digital health technologies.
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Affiliation(s)
- Dilara Vanessa Trupia
- LATTS, Univ Gustave Eiffel, CNRS, Ecole des Ponts, Marne-la-Vallée, France.,INSERM, Chaire Avenir Santé Numérique IMRB U955, Équipe 8, University of Paris-Est Créteil, Créteil, France
| | | | - Tu Anh Duong
- INSERM, Chaire Avenir Santé Numérique IMRB U955, Équipe 8, University of Paris-Est Créteil, Créteil, France.,AP-HP, Department of Dermatology, Hôpital Henri-Mondor, Créteil, France
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13
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Zidaru T, Morrow EM, Stockley R. Ensuring patient and public involvement in the transition to AI-assisted mental health care: A systematic scoping review and agenda for design justice. Health Expect 2021; 24:1072-1124. [PMID: 34118185 PMCID: PMC8369091 DOI: 10.1111/hex.13299] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 04/07/2021] [Accepted: 05/26/2021] [Indexed: 12/16/2022] Open
Abstract
Background Machine‐learning algorithms and big data analytics, popularly known as ‘artificial intelligence’ (AI), are being developed and taken up globally. Patient and public involvement (PPI) in the transition to AI‐assisted health care is essential for design justice based on diverse patient needs. Objective To inform the future development of PPI in AI‐assisted health care by exploring public engagement in the conceptualization, design, development, testing, implementation, use and evaluation of AI technologies for mental health. Methods Systematic scoping review drawing on design justice principles, and (i) structured searches of Web of Science (all databases) and Ovid (MEDLINE, PsycINFO, Global Health and Embase); (ii) handsearching (reference and citation tracking); (iii) grey literature; and (iv) inductive thematic analysis, tested at a workshop with health researchers. Results The review identified 144 articles that met inclusion criteria. Three main themes reflect the challenges and opportunities associated with PPI in AI‐assisted mental health care: (a) applications of AI technologies in mental health care; (b) ethics of public engagement in AI‐assisted care; and (c) public engagement in the planning, development, implementation, evaluation and diffusion of AI technologies. Conclusion The new data‐rich health landscape creates multiple ethical issues and opportunities for the development of PPI in relation to AI technologies. Further research is needed to understand effective modes of public engagement in the context of AI technologies, to examine pressing ethical and safety issues and to develop new methods of PPI at every stage, from concept design to the final review of technology in practice. Principles of design justice can guide this agenda.
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Affiliation(s)
- Teodor Zidaru
- Department of Anthropology, London School of Economics and Political Science (LSE), London, UK
| | | | - Rich Stockley
- Surrey Heartlands Health and Care Partnership, Guildford and Waverley CCG, Guildford, UK.,Insight and Feedback Team, Nursing Directorate, NHS England and NHS Improvement, London, UK.,Surrey County Council, Kingston upon Thames, UK
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14
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Pot M, Brehme M, El-Heliebi A, Gschmeidler B, Hofer P, Kroneis T, Schirmer M, Schumann S, Prainsack B. Personalized medicine in Austria: expectations and limitations. Per Med 2020; 17:423-428. [PMID: 33026295 DOI: 10.2217/pme-2020-0061] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Mirjam Pot
- Department of Political Science, University of Vienna, Vienna 1010, Austria
| | | | - Amin El-Heliebi
- Medical University of Graz, Gottfried Schatz Research Center, Division of Cell Biology, Histology and Embryology, Graz 8036, Austria.,Center for Biomarker Research in Medicine, Graz 8010, Austria
| | | | - Philipp Hofer
- Medical University of Vienna, Department of Pathology, Vienna 1090, Austria
| | - Thomas Kroneis
- Medical University of Graz, Gottfried Schatz Research Center, Division of Cell Biology, Histology and Embryology, Graz 8036, Austria.,Center for Biomarker Research in Medicine, Graz 8010, Austria
| | - Michael Schirmer
- Department of Internal Medicine, Medical University of Innsbruck, Clinic II, Innsbruck 6020, Austria
| | - Simone Schumann
- Open Science - Life Sciences in Dialogue, Vienna 1030, Austria
| | - Barbara Prainsack
- Department of Political Science, University of Vienna, Vienna 1010, Austria.,Department of Global Health & Social Medicine, King's College London, Strand, London WC2R 2LS, United Kingdom
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15
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Korvesi VM, Chouvarda I, Mastorakos G, Goulis DG. Implementation of the Endocrine Society clinical practice guidelines for gestational diabetes mellitus to a knowledge tool. Eur J Clin Invest 2020; 50:e13291. [PMID: 32446282 DOI: 10.1111/eci.13291] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 04/29/2020] [Accepted: 05/14/2020] [Indexed: 12/18/2022]
Abstract
BACKGROUND Despite the production of clinical practice guidelines (CPGs) in many medical areas, their use is not sufficiently adopted in clinical practice. Incorporation of CPGs in knowledge tools (KnowT) or decision support systems (DSS) for routine use can improve healthcare providers' compliance to CPGs. MATERIALS AND METHODS Clinical practice guidelines for gestational diabetes mellitus (GDM) were searched for, collected and compared. The CPG that met pre-specified criteria ([a] published by a European or American organization between 2010 and 2018, [b] being developed a systematic way and [c] having statements of "level of evidence" and "strength of recommendation") was chosen for implementation (Endocrine Society, 2013). Its recommendations were deconstructed, re-organized and reconstructed as an algorithm (in the form of a flowchart), which was integrated into a KnowT. Content completeness and evaluation of CPG by the Guideline Implementability Appraisal tool (GLIA) were performed as well. The primary objective was the development of a clinical algorithm in the field of GDM and its integration into a KnowT. The secondary objective was to demonstrate the completeness of the CPG content and evaluate its implementability in the KnowT. RESULTS Endocrine Society 2013 CPG was restructured as a flowchart, and a KnowT was constructed with the use of the "Openlabyrinth" software. The completeness of the content was confirmed, and GLIA appraisal demonstrated its implementability. CONCLUSION Endocrine Society 2013 CPG for GDM is a complete set of recommendations. Its structure makes possible the design of a clinical algorithm and its implementation into a KnowT.
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Affiliation(s)
- Vasiliki M Korvesi
- Unit of Reproductive Endocrinology, 1st Department of Obstetrics and Gynecology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Ioanna Chouvarda
- Lab of Computing, Medical Informatics and Biomedical Imaging Technologies, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - George Mastorakos
- Unit of Endocrinology, Diabetes mellitus and Metabolism, Faculty of Medicine, Aretaieion Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Dimitrios G Goulis
- Unit of Reproductive Endocrinology, 1st Department of Obstetrics and Gynecology, Aristotle University of Thessaloniki, Thessaloniki, Greece
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16
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The double-edged sword of digital self-care: Physician perspectives from Northern Germany. Soc Sci Med 2020; 260:113174. [DOI: 10.1016/j.socscimed.2020.113174] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 06/10/2020] [Accepted: 06/25/2020] [Indexed: 02/07/2023]
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17
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Abstract
OBJECTIVES Clinical Research Informatics (CRI) declares its scope in its name, but its content, both in terms of the clinical research it supports-and sometimes initiates-and the methods it has developed over time, reach much further than the name suggests. The goal of this review is to celebrate the extraordinary diversity of activity and of results, not as a prize-giving pageant, but in recognition of the field, the community that both serves and is sustained by it, and of its interdisciplinarity and its international dimension. METHODS Beyond personal awareness of a range of work commensurate with the author's own research, it is clear that, even with a thorough literature search, a comprehensive review is impossible. Moreover, the field has grown and subdivided to an extent that makes it very hard for one individual to be familiar with every branch or with more than a few branches in any depth. A literature survey was conducted that focused on informatics-related terms in the general biomedical and healthcare literature, and specific concerns ("artificial intelligence", "data models", "analytics", etc.) in the biomedical informatics (BMI) literature. In addition to a selection from the results from these searches, suggestive references within them were also considered. RESULTS The substantive sections of the paper-Artificial Intelligence, Machine Learning, and "Big Data" Analytics; Common Data Models, Data Quality, and Standards; Phenotyping and Cohort Discovery; Privacy: Deidentification, Distributed Computation, Blockchain; Causal Inference and Real-World Evidence-provide broad coverage of these active research areas, with, no doubt, a bias towards this reviewer's interests and preferences, landing on a number of papers that stood out in one way or another, or, alternatively, exemplified a particular line of work. CONCLUSIONS CRI is thriving, not only in the familiar major centers of research, but more widely, throughout the world. This is not to pretend that the distribution is uniform, but to highlight the potential for this domain to play a prominent role in supporting progress in medicine, healthcare, and wellbeing everywhere. We conclude with the observation that CRI and its practitioners would make apt stewards of the new medical knowledge that their methods will bring forward.
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Affiliation(s)
- Anthony Solomonides
- Outcomes Research Network, Research Institute, NorthShore University HealthSystem, Evanston, IL, USA
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Andersen TO, Langstrup H, Lomborg S. Experiences With Wearable Activity Data During Self-Care by Chronic Heart Patients: Qualitative Study. J Med Internet Res 2020; 22:e15873. [PMID: 32706663 PMCID: PMC7399963 DOI: 10.2196/15873] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 03/20/2020] [Accepted: 05/14/2020] [Indexed: 12/18/2022] Open
Abstract
Background Most commercial activity trackers are developed as consumer devices and not as clinical devices. The aim is to monitor and motivate sport activities, healthy living, and similar wellness purposes, and the devices are not designed to support care management in a clinical context. There are great expectations for using wearable sensor devices in health care settings, and the separate realms of wellness tracking and disease self-monitoring are increasingly becoming blurred. However, patients’ experiences with activity tracking technologies designed for use outside the clinical context have received little academic attention. Objective This study aimed to contribute to understanding how patients with a chronic disease experience activity data from consumer self-tracking devices related to self-care and their chronic illness. Our research question was: “How do patients with heart disease experience activity data in relation to self-care and chronic illness?” Methods We conducted a qualitative interview study with patients with chronic heart disease (n=27) who had an implanted cardioverter-defibrillator. Patients were invited to wear a FitBit Alta HR wearable activity tracker for 3-12 months and provide their perspectives on their experiences with step, sleep, and heart rate data. The average age was 57.2 years (25 men and 2 women), and patients used the tracker for 4-49 weeks (mean 26.1 weeks). Semistructured interviews (n=66) were conducted with patients 2–3 times and were analyzed iteratively in workshops using thematic analysis and abductive reasoning logic. Results Of the 27 patients, 18 related the heart rate, sleep, and step count data directly to their heart disease. Wearable activity trackers actualized patients’ experiences across 3 dimensions with a spectrum of contrasting experiences: (1) knowing, which spanned gaining insight and evoking doubts; (2) feeling, which spanned being reassured and becoming anxious; and (3) evaluating, which spanned promoting improvements and exposing failure. Conclusions Patients’ experiences could reside more on one end of the spectrum, could reside across all 3 dimensions, or could combine contrasting positions and even move across the spectrum over time. Activity data from wearable devices may be a resource for self-care; however, the data may simultaneously constrain and create uncertainty, fear, and anxiety. By showing how patients experience self-tracking data across dimensions of knowing, feeling, and evaluating, we point toward the richness and complexity of these data experiences in the context of chronic illness and self-care.
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Affiliation(s)
| | | | - Stine Lomborg
- Department of Communication, University of Copenhagen, Copenhagen, Denmark
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Choi YI, Kim YJ, Chung JW, Kim KO, Kim H, Park RW, Park DK. Effect of Age on the Initiation of Biologic Agent Therapy in Patients With Inflammatory Bowel Disease: Korean Common Data Model Cohort Study. JMIR Med Inform 2020; 8:e15124. [PMID: 32293578 PMCID: PMC7191339 DOI: 10.2196/15124] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 10/23/2019] [Accepted: 01/27/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND The Observational Health Data Sciences and Informatics (OHDSI) network is an international collaboration established to apply open-source data analytics to a large network of health databases, including the Korean common data model (K-CDM) network. OBJECTIVE The aim of this study is to analyze the effect that age at diagnosis has on the prognosis of inflammatory bowel disease (IBD) in Korea using a CDM network database. METHODS We retrospectively analyzed the K-CDM network database from 2005 to 2015. We transformed the electronic medical record into the CDM version 5.0 used in OHDSI. A worsened IBD prognosis was defined as the initiation of therapy with biologic agents, including infliximab and adalimumab. To evaluate the effect that age at diagnosis had on the prognosis of IBD, we divided the patients into an early-onset (EO) IBD group (age at diagnosis <40 years) and a late-onset (LO) IBD group (age at diagnosis ≥40 years) with the cutoff value of age at diagnosis as 40 years, which was calculated using the Youden index method. We then used the logrank test and Cox proportional hazards model to analyze the effect that age at diagnosis (EO group vs LO group) had on the prognosis in patients with IBD. RESULTS A total of 3480 patients were enrolled. There was 2017 patients with ulcerative colitis (UC) and 1463 with Crohn's disease (CD). The median follow up period was 109.5 weeks. The EO UC group was statistically significant and showed less event-free survival (ie, experiences of biologic agents) than the LO UC group (P<.001). In CD, the EO CD group showed less event-free survival (ie, experiences of biologic agents) than the LO CD group. In the Cox proportional hazard analysis, the odds ratio (OR) of the EO UC group on experiences of biologic agents compared with the LO UC group was 2.3 (95% CI 1.3-3.8, P=.002). The OR of the EO CD group on experiences of biologic agents compared with the LO CD group was 5.4 (95% CI 1.9-14.9, P=.001). CONCLUSIONS The EO IBD group showed a worse prognosis than the LO IBD group in Korean patients with IBD. In addition, this study successfully verified the CDM model in gastrointestinal research.
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Affiliation(s)
- Youn I Choi
- Department of Gastroenterology, Gil Medical Center, Gachon University College of Internal Medicine, Incheon, Republic of Korea
| | - Yoon Jae Kim
- Department of Gastroenterology, Gil Medical Center, Gachon University College of Internal Medicine, Incheon, Republic of Korea
| | - Jun-Won Chung
- Department of Gastroenterology, Gil Medical Center, Gachon University College of Internal Medicine, Incheon, Republic of Korea
| | - Kyoung Oh Kim
- Department of Gastroenterology, Gil Medical Center, Gachon University College of Internal Medicine, Incheon, Republic of Korea
| | - Hakki Kim
- Health IT Research Center, Gil Medical Center, Gachon University, Incheon, Republic of Korea
| | | | - Dong Kyun Park
- Department of Gastroenterology, Gil Medical Center, Gachon University College of Internal Medicine, Incheon, Republic of Korea
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Capobianco E. Imprecise Data and Their Impact on Translational Research in Medicine. Front Med (Lausanne) 2020; 7:82. [PMID: 32266273 PMCID: PMC7096475 DOI: 10.3389/fmed.2020.00082] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 03/02/2020] [Indexed: 11/13/2022] Open
Abstract
The medical field expects from big data essentially two main results: the ability to build predictive models and the possibility of applying them to obtain accurate patient risk profiles and/or health trajectories. Note that the paradigm of precision has determined that similar challenges need to be faced in both population and individualized studies, namely the need of assembling, integrating, modeling, and interpreting data from a variety of information sources and scales potentially influencing disease from onset to progression. In many cases, data require computational treatment through solutions for otherwise intractable problems. However, as precision medicine remains subject to a substantial amount of data imprecision and lack of translational impact, a revision of methodological inference approaches is needed. Both the relevance and the usefulness of such revision crucially deal with the assimilation of data features dynamically interconnected.
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Affiliation(s)
- Enrico Capobianco
- Institute of Data Science and Computing, University of Miami, Miami, FL, United States
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