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Bossen C, Bertelsen PS. Digital health care and data work: Who are the data professionals? HEALTH INF MANAG J 2024; 53:243-251. [PMID: 37491822 DOI: 10.1177/18333583231183083] [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] [Indexed: 07/27/2023]
Abstract
BACKGROUND This article reports on a study that investigated data professionals in health care. The topic is interesting and relevant because of the ongoing trend towards digitisation of the healthcare domain and efforts for it to become data driven, which entail a wide variety of work with data. OBJECTIVE Despite an interest in data science and more broadly in data work, we know surprisingly little about the people who work with data in healthcare. Therefore, we investigated data work at a large national healthcare data organisation in Denmark. METHOD An explorative mixed method approach combining a non-probability technique for design of an open survey with a target population of 300+ and 11 semi-structured interviews, was applied. RESULTS We report findings relevant to educational background, work identity, work tasks, and how staff acquired competences and knowledge, as well as what these attributes comprised. We found recurring themes of healthcare knowledge, data analytical skills, and information technology, reflected in education, competences and knowledge. However, there was considerable variation within and beyond those themes, and indeed most competences were learned "on the job" rather than as part of formal education. CONCLUSION Becoming a professional working with data in health care can be the result of different career paths. The most recurring work identity was that of "data analyst"; however, a wide variety of responses indicated that a stable data worker identity has not yet developed. IMPLICATIONS The findings present implications for educational policy makers and healthcare managers.
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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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O'Driscoll R, Bakerly ND. Automated audit of hospital oxygen use devised during the COVID-19 pandemic. BMJ Open Respir Res 2023; 10:e001866. [PMID: 38154912 PMCID: PMC10759130 DOI: 10.1136/bmjresp-2023-001866] [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: 06/01/2023] [Accepted: 10/20/2023] [Indexed: 12/30/2023] Open
Abstract
BACKGROUND The British Thoracic Society (BTS) has organised intermittent audits of hospital oxygen use in UK hospitals since 2008. Manual audits are time-consuming and subject to human errors. Oxygen prescribing and bedside observations including National Early Warning Scores (NEWS2 scores) are undertaken within an integrated electronic medical record (EMR) at this hospital. METHODS The hospital's Business Information team were commissioned in late 2019 to devise a bespoke automated audit of oxygen prescribing and use. A summary report displays the oxygen saturation alongside the oxygen prescription status of every patient in the hospital except for critical care units which do not use NEWS2. The display has a 'traffic-light' colour scheme (green within target range, amber or red if below range or if above range on supplemental oxygen), with a graph showing oxygen use and saturation levels for patients with each prescribed target range. Clinicians can access raw data including oxygen saturation, oxygen device and flow rate for each individual patient. RESULTS Over 51 audits involving 34 352 sets of observations, an average of 6.0% involved use of oxygen and 88.6% of these had a valid oxygen prescription. During the first wave of the COVID-19 pandemic in spring 2020, the monthly percentage of observations involving oxygen use increased to a peak of 10.4% followed by a rise to 10.6% during the second wave and 7.4% during the third (Omicron) wave. Oxygen use returned to baseline after each wave. CONCLUSIONS In hospitals with integrated EMRs, it is possible to automate all fundamental aspects of the BTS oxygen audits and to monitor oxygen use at individual patient level and a hospital-wide level. This could be particularly valuable during major events such as the COVID-19 pandemic. This methodology could be extended to other clinical audits where the audit questions relate to routinely collected EMR data.
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Affiliation(s)
- Ronan O'Driscoll
- Respiratory Medicine, Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Salford, UK ronan.o'
| | - Nawar Diar Bakerly
- Respiratory Medicine, Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Salford, UK
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Kim MJ, Kim HJ, Kang D, Ahn HK, Shin SY, Park S, Cho J, Park YH. Preliminary Attainability Assessment of Real-World Data for Answering Major Clinical Research Questions in Breast Cancer Brain Metastasis: Framework Development and Validation Study. J Med Internet Res 2023; 25:e43359. [PMID: 36951923 PMCID: PMC10131620 DOI: 10.2196/43359] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 01/02/2023] [Accepted: 01/03/2023] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND In recent decades, real-world evidence (RWE) in oncology has rapidly gained traction for its potential to answer clinical questions that cannot be directly addressed by randomized clinical trials. Integrating real-world data (RWD) into clinical research promises to contribute to more sustainable research designs, including extension, augmentation, enrichment, and pragmatic designs. Nevertheless, clinical research using RWD is still limited because of concerns regarding the shortage of best practices for extracting, harmonizing, and analyzing RWD. In particular, pragmatic screening methods to determine whether the content of a data source is sufficient to answer the research questions before conducting the research with RWD have not yet been established. OBJECTIVE We examined the PAR (Preliminary Attainability Assessment of Real-World Data) framework and assessed its utility in breast cancer brain metastasis (BCBM), which has an unmet medical need for data attainability screening at the preliminary step of observational studies that use RWD. METHODS The PAR framework was proposed to assess data attainability from a particular data source during the early research process. The PAR framework has four sequential stages, starting with clinical question clarification: (1) operational definition of variables, (2) data matching (structural/semantic), (3) data screening and extraction, and (4) data attainability diagramming. We identified 5 clinical questions to be used for PAR framework evaluation through interviews and validated them with a survey of breast cancer experts. We used the Samsung Medical Center Breast Cancer Registry, a hospital-based real-time registry implemented in March 2021, leveraging the institution's anonymized and deidentified clinical data warehouse platform. The number of breast cancer patients in the registry was 45,129; it covered the period from June 1995 to December 2021. The registry consists of 24 base data marts that represent disease-specific breast cancer characteristics and care pathways. The outcomes included screening results of the clinical questions via the PAR framework and a procedural diagram of data attainability for each research question. RESULTS Data attainability was tested for study feasibility according to the PAR framework with 5 clinical questions for BCBM. We obtained data sets that were sufficient to conduct studies with 4 of 5 clinical questions. The research questions stratified into 3 types when we developed data fields for clearly defined research variables. In the first, only 1 question could be answered using direct data variables. In the second, the other 3 questions required surrogate definitions that combined data variables. In the third, the question turned out to be not feasible for conducting further analysis. CONCLUSIONS The adoption of the PAR framework was associated with more efficient preliminary clinical research using RWD from BCBM. Furthermore, this framework helped accelerate RWE generation through clinical research by enhancing transparency and reproducibility and lowering the entry barrier for clinical researchers.
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Affiliation(s)
- Min Jeong Kim
- Department of Digital Health, Samsung Advanced Institute for Health Science & Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Hyo Jung Kim
- Department of Digital Health, Samsung Advanced Institute for Health Science & Technology, Sungkyunkwan University, Seoul, Republic of Korea
- Center for Research Resource Standardization, Research Institution for Future Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Danbee Kang
- Center for Clinical Epidemiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Science & Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Hee Kyung Ahn
- Division of Medical Oncology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, Republic of Korea
| | - Soo-Yong Shin
- Department of Digital Health, Samsung Advanced Institute for Health Science & Technology, Sungkyunkwan University, Seoul, Republic of Korea
- Center for Research Resource Standardization, Research Institution for Future Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
| | - Seri Park
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Juhee Cho
- Center for Clinical Epidemiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Science & Technology, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Epidemiology and Medicine, The Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Yeon Hee Park
- Department of Digital Health, Samsung Advanced Institute for Health Science & Technology, Sungkyunkwan University, Seoul, Republic of Korea
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
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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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