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Hussain D, Al-Masni MA, Aslam M, Sadeghi-Niaraki A, Hussain J, Gu YH, Naqvi RA. Revolutionizing tumor detection and classification in multimodality imaging based on deep learning approaches: Methods, applications and limitations. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:857-911. [PMID: 38701131 DOI: 10.3233/xst-230429] [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: 05/05/2024]
Abstract
BACKGROUND The emergence of deep learning (DL) techniques has revolutionized tumor detection and classification in medical imaging, with multimodal medical imaging (MMI) gaining recognition for its precision in diagnosis, treatment, and progression tracking. OBJECTIVE This review comprehensively examines DL methods in transforming tumor detection and classification across MMI modalities, aiming to provide insights into advancements, limitations, and key challenges for further progress. METHODS Systematic literature analysis identifies DL studies for tumor detection and classification, outlining methodologies including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants. Integration of multimodality imaging enhances accuracy and robustness. RESULTS Recent advancements in DL-based MMI evaluation methods are surveyed, focusing on tumor detection and classification tasks. Various DL approaches, including CNNs, YOLO, Siamese Networks, Fusion-Based Models, Attention-Based Models, and Generative Adversarial Networks, are discussed with emphasis on PET-MRI, PET-CT, and SPECT-CT. FUTURE DIRECTIONS The review outlines emerging trends and future directions in DL-based tumor analysis, aiming to guide researchers and clinicians toward more effective diagnosis and prognosis. Continued innovation and collaboration are stressed in this rapidly evolving domain. CONCLUSION Conclusions drawn from literature analysis underscore the efficacy of DL approaches in tumor detection and classification, highlighting their potential to address challenges in MMI analysis and their implications for clinical practice.
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Affiliation(s)
- Dildar Hussain
- Department of Artificial Intelligence and Data Science, Sejong University, Seoul, Korea
| | - Mohammed A Al-Masni
- Department of Artificial Intelligence and Data Science, Sejong University, Seoul, Korea
| | - Muhammad Aslam
- Department of Artificial Intelligence and Data Science, Sejong University, Seoul, Korea
| | - Abolghasem Sadeghi-Niaraki
- Department of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Korea
| | - Jamil Hussain
- Department of Artificial Intelligence and Data Science, Sejong University, Seoul, Korea
| | - Yeong Hyeon Gu
- Department of Artificial Intelligence and Data Science, Sejong University, Seoul, Korea
| | - Rizwan Ali Naqvi
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, Korea
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Lin AY, Arabandi S, Beale T, Duncan WD, Hicks A, Hogan WR, Jensen M, Koppel R, Martínez-Costa C, Nytrø Ø, Obeid JS, de Oliveira JP, Ruttenberg A, Seppälä S, Smith B, Soergel D, Zheng J, Schulz S. Improving the Quality and Utility of Electronic Health Record Data through Ontologies. STANDARDS (BASEL, SWITZERLAND) 2023; 3:316-340. [PMID: 37873508 PMCID: PMC10591519 DOI: 10.3390/standards3030023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
The translational research community, in general, and the Clinical and Translational Science Awards (CTSA) community, in particular, share the vision of repurposing EHRs for research that will improve the quality of clinical practice. Many members of these communities are also aware that electronic health records (EHRs) suffer limitations of data becoming poorly structured, biased, and unusable out of original context. This creates obstacles to the continuity of care, utility, quality improvement, and translational research. Analogous limitations to sharing objective data in other areas of the natural sciences have been successfully overcome by developing and using common ontologies. This White Paper presents the authors' rationale for the use of ontologies with computable semantics for the improvement of clinical data quality and EHR usability formulated for researchers with a stake in clinical and translational science and who are advocates for the use of information technology in medicine but at the same time are concerned by current major shortfalls. This White Paper outlines pitfalls, opportunities, and solutions and recommends increased investment in research and development of ontologies with computable semantics for a new generation of EHRs.
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Affiliation(s)
- Asiyah Yu Lin
- National Institutes of Health, Bethesda, MD 20892, USA
| | | | | | - William D. Duncan
- College of Dentistry, University of Florida, Gainesville, FL 32610, USA
| | - Amanda Hicks
- The Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723, USA
| | - William R. Hogan
- Data Science Institute, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | | | - Ross Koppel
- Department of Medical Informatics, Jacobs School of Medicine, University at Buffalo, Buffalo, NY 14260, USA
- Department of Medical Informatics, School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Catalina Martínez-Costa
- Department of Informatics and Systems, Faculty of Computer Science, University of Murcia, 30100 Murcia, Spain
| | - Øystein Nytrø
- Department of Computer Science, UIT Arctic University of Norway, 9037 Tromsø, Norway
- Department of Computer Science, Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Jihad S. Obeid
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
| | | | - Alan Ruttenberg
- School of Dental Medicine, University at Buffalo, Buffalo, NY 14260, USA
| | - Selja Seppälä
- Department of Business Information Systems, University College Cork, T12 K8AF Cork, Ireland
| | - Barry Smith
- Department of Philosophy, University at Buffalo, Buffalo, NY 14260, USA
| | - Dagobert Soergel
- Department of Philosophy, University at Buffalo, Buffalo, NY 14260, USA
| | - Jie Zheng
- Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48104, USA
| | - Stefan Schulz
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, 8036 Graz, Austria
- Averbis GmbH, Salzstrasse 15, 79098 Freiburg im Breisgau, Germany
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Siddiqi MH, Idris M, Alruwaili M. FAIR Health Informatics: A Health Informatics Framework for Verifiable and Explainable Data Analysis. Healthcare (Basel) 2023; 11:1713. [PMID: 37372831 DOI: 10.3390/healthcare11121713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 06/04/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
The recent COVID-19 pandemic has hit humanity very hard in ways rarely observed before. In this digitally connected world, the health informatics and investigation domains (both public and private) lack a robust framework to enable rapid investigation and cures. Since the data in the healthcare domain are highly confidential, any framework in the healthcare domain must work on real data, be verifiable, and support reproducibility for evidence purposes. In this paper, we propose a health informatics framework that supports data acquisition from various sources in real-time, correlates these data from various sources among each other and to the domain-specific terminologies, and supports querying and analyses. Various sources include sensory data from wearable sensors, clinical investigation (for trials and devices) data from private/public agencies, personnel health records, academic publications in the healthcare domain, and semantic information such as clinical ontologies and the Medical Subject Heading ontology. The linking and correlation of various sources include mapping personnel wearable data to health records, clinical oncology terms to clinical trials, and so on. The framework is designed such that the data are Findable, Accessible, Interoperable, and Reusable with proper Identity and Access Mechanisms. This practically means to tracing and linking each step in the data management lifecycle through discovery, ease of access and exchange, and data reuse. We present a practical use case to correlate a variety of aspects of data relating to a certain medical subject heading from the Medical Subject Headings ontology and academic publications with clinical investigation data. The proposed architecture supports streaming data acquisition and servicing and processing changes throughout the lifecycle of the data management. This is necessary in certain events, such as when the status of a certain clinical or other health-related investigation needs to be updated. In such cases, it is required to track and view the outline of those events for the analysis and traceability of the clinical investigation and to define interventions if necessary.
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Affiliation(s)
| | | | - Madallah Alruwaili
- College of Computer and Information Sciences, Jouf University, Sakaka 73211, Saudi Arabia
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4
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Eysenbach G, Ulrich H, Bergh B, Schreiweis B. Functional Requirements for Medical Data Integration into Knowledge Management Environments: Requirements Elicitation Approach Based on Systematic Literature Analysis. J Med Internet Res 2023; 25:e41344. [PMID: 36757764 PMCID: PMC9951079 DOI: 10.2196/41344] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 10/24/2022] [Accepted: 11/17/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND In patient care, data are historically generated and stored in heterogeneous databases that are domain specific and often noninteroperable or isolated. As the amount of health data increases, the number of isolated data silos is also expected to grow, limiting the accessibility of the collected data. Medical informatics is developing ways to move from siloed data to a more harmonized arrangement in information architectures. This paradigm shift will allow future research to integrate medical data at various levels and from various sources. Currently, comprehensive requirements engineering is working on data integration projects in both patient care- and research-oriented contexts, and it is significantly contributing to the success of such projects. In addition to various stakeholder-based methods, document-based requirement elicitation is a valid method for improving the scope and quality of requirements. OBJECTIVE Our main objective was to provide a general catalog of functional requirements for integrating medical data into knowledge management environments. We aimed to identify where integration projects intersect to derive consistent and representative functional requirements from the literature. On the basis of these findings, we identified which functional requirements for data integration exist in the literature and thus provide a general catalog of requirements. METHODS This work began by conducting a literature-based requirement elicitation based on a broad requirement engineering approach. Thus, in the first step, we performed a web-based systematic literature review to identify published articles that dealt with the requirements for medical data integration. We identified and analyzed the available literature by applying the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. In the second step, we screened the results for functional requirements using the requirements engineering method of document analysis and derived the requirements into a uniform requirement syntax. Finally, we classified the elicited requirements into a category scheme that represents the data life cycle. RESULTS Our 2-step requirements elicitation approach yielded 821 articles, of which 61 (7.4%) were included in the requirement elicitation process. There, we identified 220 requirements, which were covered by 314 references. We assigned the requirements to different data life cycle categories as follows: 25% (55/220) to data acquisition, 35.9% (79/220) to data processing, 12.7% (28/220) to data storage, 9.1% (20/220) to data analysis, 6.4% (14/220) to metadata management, 2.3% (5/220) to data lineage, 3.2% (7/220) to data traceability, and 5.5% (12/220) to data security. CONCLUSIONS The aim of this study was to present a cross-section of functional data integration-related requirements defined in the literature by other researchers. The aim was achieved with 220 distinct requirements from 61 publications. We concluded that scientific publications are, in principle, a reliable source of information for functional requirements with respect to medical data integration. Finally, we provide a broad catalog to support other scientists in the requirement elicitation phase.
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Affiliation(s)
- G Eysenbach
- Institute for Medical Informatics and StatisticsKiel University and University Hospital Schleswig-HolsteinKielGermany
| | - Hannes Ulrich
- Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany
| | - Björn Bergh
- Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany
| | - Björn Schreiweis
- Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany
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Pant K, Bhatia M, Pant R. Integrated care with digital health innovation: pressing challenges. JOURNAL OF INTEGRATED CARE 2022. [DOI: 10.1108/jica-01-2022-0008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeDigital health care has emerged as one of the most important means to deliver integrated care by care providers in recent years. As the use of digital health increases, there are some pressing issues such as interoperability of data across different healthcare information systems, regulatory environment and security and privacy of patient’s information which need to be discussed and addressed in order to reduce information silos and to ensure efficient and seamless use of digital health technologies. The purpose of this paper is to address these issues.Design/methodology/approachIn this paper the authors outline the key concepts of interoperability, key challenges pertaining in achieving interoperability and concepts of security and privacy in context of digital health models of integrated care.FindingsThe study suggests that standardization of digital health information systems and connecting existing systems to health network, addressing privacy and security related issues through a comprehensive but supportive regulatory environment and educating citizens and healthcare providers are some of the ways to achieve effective use of digital health in models of integrated care.Originality/valueAlthough the concepts of privacy and interoperability are not new, however, as per best of the authors’ knowledge, this is the first attempt to discuss the challenges and possible actions to meet the objective of achieving integrated care through digital innovation.
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Canaway R, Boyle D, Manski-Nankervis JA, Gray K. Identifying primary care datasets and perspectives on their secondary use: a survey of Australian data users and custodians. BMC Med Inform Decis Mak 2022; 22:94. [PMID: 35387634 PMCID: PMC8988328 DOI: 10.1186/s12911-022-01830-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 03/23/2022] [Indexed: 11/17/2022] Open
Abstract
Background Most people receive most of their health care in in Australia in primary care, yet researchers and policymakers have limited access to resulting clinical data. Widening access to primary care data and linking it with hospital or other data can contribute to research informing policy and provision of services and care; however, limitations of primary care data and barriers to access curtail its use. The Australian Health Research Alliance (AHRA) is seeking to build capacity in data-driven healthcare improvement; this study formed part of its workplan.
Methods The study aimed to build capacity for data driven healthcare improvement through identifying primary care datasets in Australia available for secondary use and understand data quality frameworks being applied to them, and factors affecting national capacity for secondary use of primary care data from the perspectives of data custodians and users. Purposive and snowball sampling were used to disseminate a questionnaire and respondents were invited to contribute additional information via semi-structured interviews. Results Sixty-two respondents collectively named 106 datasets from eclectic sources, indicating a broad conceptualisation of what a primary care dataset available for secondary use is. The datasets were generated from multiple clinical software systems, using different data extraction tools, resulting in non-standardised data structures. Use of non-standard data quality frameworks were described by two-thirds of data custodians. Building trust between citizens, clinicians, third party data custodians and data end-users was considered by many to be a key enabler to improve primary care data quality and efficiencies related to secondary use. Trust building qualities included meaningful stakeholder engagement, transparency, strong leadership, shared vision, robust data security and data privacy protection. Resources to improve capacity for primary care data access and use were sought for data collection tool improvements, workforce upskilling and education, incentivising data collection and making data access more affordable. Conclusions The large number of identified Australian primary care related datasets suggests duplication of labour related to data collection, preparation and utilisation. Benefits of secondary use of primary care data were many, and strong national leadership is required to reach consensus on how to address limitations and barriers, for example accreditation of EMR clinical software systems and the adoption of agreed data and quality standards at all stages of the clinical and research data-use lifecycle. The study informed the workplan of AHRA’s Transformational Data Collaboration to improve partner engagement and use of clinical data for research. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-022-01830-9.
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Affiliation(s)
- Rachel Canaway
- Department of General Practice, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Douglas Boyle
- Department of General Practice, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC, 3010, Australia.
| | - Jo-Anne Manski-Nankervis
- Department of General Practice, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Kathleen Gray
- School of Computing and Information Systems and Melbourne Medical School, The University of Melbourne, Parkville, VIC, 3010, Australia
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Abounassar EM, El-Kafrawy P, Abd El-Latif AA. Security and Interoperability Issues with Internet of Things (IoT) in Healthcare Industry: A Survey. STUDIES IN BIG DATA 2022:159-189. [DOI: 10.1007/978-3-030-85428-7_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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The Prescription of Drug Ontology 2.0 (PDRO): More Than the Sum of Its Parts. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182212025. [PMID: 34831777 PMCID: PMC8619589 DOI: 10.3390/ijerph182212025] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/06/2021] [Accepted: 11/12/2021] [Indexed: 01/23/2023]
Abstract
While drugs and related products have profoundly changed the lives of people around the world, ongoing challenges remain, including inappropriate use of a drug product. Inappropriate uses can be explained in part by ambiguous or incomplete information, for example, missing reasons for treatments, ambiguous information on how to take a medication, or lack of information on medication-related events outside the health care system. In order to fully assess the situation, data from multiple systems (electronic medical records, pharmacy and radiology information systems, laboratory management systems, etc.) from multiple organizations (outpatient clinics, hospitals, long-term care facilities, laboratories, pharmacies, registries, governments) on a large geographical scale is needed. Formal knowledge models like ontologies can help address such an information integration challenge. Existing approaches like the Observational Medical Outcomes Partnership are discussed and contrasted with the use of ontologies and systems using them for data integration. The PRescription Drug Ontology 2.0 (PDRO 2.0) is then presented and entities that are paramount in addressing this problematic are described. Finally, the benefits of using PDRO are discussed through a series of exemplar situation.
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Cecchetti AA, Bhardwaj N, Murughiyan U, Kothakapu G, Sundaram U. Fueling Clinical and Translational Research in Appalachia: Informatics Platform Approach. JMIR Med Inform 2020; 8:e17962. [PMID: 33052114 PMCID: PMC7593861 DOI: 10.2196/17962] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 07/24/2020] [Accepted: 07/27/2020] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND The Appalachian population is distinct, not just culturally and geographically but also in its health care needs, facing the most health care disparities in the United States. To meet these unique demands, Appalachian medical centers need an arsenal of analytics and data science tools with the foundation of a centralized data warehouse to transform health care data into actionable clinical interventions. However, this is an especially challenging task given the fragmented state of medical data within Appalachia and the need for integration of other types of data such as environmental, social, and economic with medical data. OBJECTIVE This paper aims to present the structure and process of the development of an integrated platform at a midlevel Appalachian academic medical center along with its initial uses. METHODS The Appalachian Informatics Platform was developed by the Appalachian Clinical and Translational Science Institute's Division of Clinical Informatics and consists of 4 major components: a centralized clinical data warehouse, modeling (statistical and machine learning), visualization, and model evaluation. Data from different clinical systems, billing systems, and state- or national-level data sets were integrated into a centralized data warehouse. The platform supports research efforts by enabling curation and analysis of data using the different components, as appropriate. RESULTS The Appalachian Informatics Platform is functional and has supported several research efforts since its implementation for a variety of purposes, such as increasing knowledge of the pathophysiology of diseases, risk identification, risk prediction, and health care resource utilization research and estimation of the economic impact of diseases. CONCLUSIONS The platform provides an inexpensive yet seamless way to translate clinical and translational research ideas into clinical applications for regions similar to Appalachia that have limited resources and a largely rural population.
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Affiliation(s)
- Alfred A Cecchetti
- Department of Clinical and Translational Science, Joan C. Edwards School of Medicine, Marshall University, Huntington, WV, United States
| | - Niharika Bhardwaj
- Department of Clinical and Translational Science, Joan C. Edwards School of Medicine, Marshall University, Huntington, WV, United States
| | - Usha Murughiyan
- Department of Clinical and Translational Science, Joan C. Edwards School of Medicine, Marshall University, Huntington, WV, United States
| | - Gouthami Kothakapu
- Department of Clinical and Translational Science, Joan C. Edwards School of Medicine, Marshall University, Huntington, WV, United States
| | - Uma Sundaram
- Department of Clinical and Translational Science, Joan C. Edwards School of Medicine, Marshall University, Huntington, WV, United States
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Bouaud J, Pelayo S, Lamy JB, Prebet C, Ngo C, Teixeira L, Guézennec G, Séroussi B. Implementation of an ontological reasoning to support the guideline-based management of primary breast cancer patients in the DESIREE project. Artif Intell Med 2020; 108:101922. [DOI: 10.1016/j.artmed.2020.101922] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 02/25/2020] [Accepted: 07/01/2020] [Indexed: 10/23/2022]
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Abou-Nassar EM, Iliyasu AM, El-Kafrawy PM, Song OY, Bashir AK, El-Latif AAA. DITrust Chain: Towards Blockchain-Based Trust Models for Sustainable Healthcare IoT Systems. IEEE ACCESS 2020; 8:111223-111238. [DOI: 10.1109/access.2020.2999468] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Okoli GN, Kostopoulou O, Delaney BC. Is symptom-based diagnosis of lung cancer possible? A systematic review and meta-analysis of symptomatic lung cancer prior to diagnosis for comparison with real-time data from routine general practice. PLoS One 2018; 13:e0207686. [PMID: 30462699 PMCID: PMC6248994 DOI: 10.1371/journal.pone.0207686] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Accepted: 11/05/2018] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Lung cancer is a good example of the potential benefit of symptom-based diagnosis, as it is the commonest cancer worldwide, with the highest mortality from late diagnosis and poor symptom recognition. The diagnosis and risk assessment tools currently available have been shown to require further validation. In this study, we determine the symptoms associated with lung cancer prior to diagnosis and demonstrate that by separating prior risk based on factors such as smoking history and age, from presenting symptoms and combining them at the individual patient level, we can make greater use of this knowledge to create a practical framework for the symptomatic diagnosis of individual patients presenting in primary care. AIM To provide an evidence-based analysis of symptoms observed in lung cancer patients prior to diagnosis. DESIGN AND SETTING Systematic review and meta-analysis of primary and secondary care data. METHOD Seven databases were searched (MEDLINE, Embase, Cumulative Index to Nursing and Allied Health Literature, Health Management Information Consortium, Web of Science, British Nursing Index and Cochrane Library). Thirteen studies were selected based on predetermined eligibility and quality criteria for diagnostic assessment to establish the value of symptom-based diagnosis using diagnosistic odds ratio (DOR) and summary receiver operating characteristic (SROC) curve. In addition, routinely collated real-time data from primary care electronic health records (EHR), TransHis, was analysed to compare with our findings. RESULTS Haemoptysis was found to have the greatest diagnostic value for lung cancer, diagnostic odds ratio (DOR) 6.39 (3.32-12.28), followed by dyspnoea 2.73 (1.54-4.85) then cough 2.64 (1.24-5.64) and lastly chest pain 2.02 (0.88-4.60). The use of symptom-based diagnosis to accurately diagnose lung cancer cases from non-cases was determined using the summary receiver operating characteristic (SROC) curve, the area under the curve (AUC) was consistently above 0.6 for each of the symptoms described, indicating reasonable discriminatory power. The positive predictive value (PPV) of diagnostic symptoms depends on an individual's prior risk of lung cancer, as well as their presenting symptom pattern. For at risk individuals we calculated prior risk using validated epidemiological models for risk factors such as age and smoking history, then combined with the calculated likelihood ratios for each symptom to establish posterior risk or positive predictive value (PPV). CONCLUSION Our findings show that there is diagnostic value in the clinical symptoms associated with lung cancer and the potential benefit of characterising these symptoms using routine data studies to identify high-risk patients.
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Affiliation(s)
- Grace N. Okoli
- Clinical Lecturer in Primary Care, School of Population Health & Environmental Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
| | - Olga Kostopoulou
- Reader in Medical Decision Making, Department of Surgery and Cancer, Imperial College London, Norfolk Place, London, United Kingdom
| | - Brendan C. Delaney
- Chair in Medical Informatics and Decision Making, Imperial College London, Department of Surgery and Cancer, St Mary's Campus, London, United Kingdom
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Bruland P, Doods J, Brix T, Dugas M, Storck M. Connecting healthcare and clinical research: Workflow optimizations through seamless integration of EHR, pseudonymization services and EDC systems. Int J Med Inform 2018; 119:103-108. [PMID: 30342678 DOI: 10.1016/j.ijmedinf.2018.09.007] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 07/02/2018] [Accepted: 09/06/2018] [Indexed: 11/30/2022]
Abstract
OBJECTIVE In the last years, several projects promote the secondary use of routine healthcare data based on electronic health record (EHR) data. In multicenter studies, dedicated pseudonymization services are applied for unified pseudonym handling. Healthcare, clinical research and pseudonymization systems are generally disconnected. Hence, the aim of this research work is to integrate these applications and to evaluate the workflow of clinical research. METHODS We analyzed and identified technical solutions for legislation compliant automatic pseudonym generation and for the integration into EHR as well as electronic data capture (EDC) systems. The Mainzelliste was used as pseudonymization service, which is available as open source solution and compliant with the data privacy concept in Germany. Subject of the integration was the local EHR and an in-house developed EDC system. A time and motion study was conducted to evaluate the effects on the workflow. RESULTS Integration of EHR, pseudonymization service and EDC systems is technically feasible and leads to a less fragmented usage of all applications. Generated pseudonyms are obtained from the service hosted at a trusted third party and can now be used in the EDC as well as in the EHR system for direct access and re-identification. The evaluation of 90 registration iterations shows that the time for documentation has been significantly reduced in average by 39.6 s (56.3%) from 71 ± 8 s to 31 ± 5 s per registered study patient. CONCLUSIONS By incorporating EHR, EDC and pseudonymization systems, it is now feasible to support multicenter studies and registers out of an integrated system landscape within a hospital. Optimizing the workflow of patient registration for clinical research allows reduction of double data entry and transcription errors as well as a seamless transition from clinical routine to research data collection.
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Affiliation(s)
- Philipp Bruland
- Institute of Medical Informatics, University of Münster, Münster, Germany.
| | - Justin Doods
- Institute of Medical Informatics, University of Münster, Münster, Germany.
| | - Tobias Brix
- Institute of Medical Informatics, University of Münster, Münster, Germany.
| | - Martin Dugas
- Institute of Medical Informatics, University of Münster, Münster, Germany.
| | - Michael Storck
- Institute of Medical Informatics, University of Münster, Münster, Germany.
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Zhang H, Guo Y, Li Q, George TJ, Shenkman E, Modave F, Bian J. An ontology-guided semantic data integration framework to support integrative data analysis of cancer survival. BMC Med Inform Decis Mak 2018; 18:41. [PMID: 30066664 PMCID: PMC6069766 DOI: 10.1186/s12911-018-0636-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Cancer is the second leading cause of death in the United States, exceeded only by heart disease. Extant cancer survival analyses have primarily focused on individual-level factors due to limited data availability from a single data source. There is a need to integrate data from different sources to simultaneously study as much risk factors as possible. Thus, we proposed an ontology-based approach to integrate heterogeneous datasets addressing key data integration challenges. METHODS Following best practices in ontology engineering, we created the Ontology for Cancer Research Variables (OCRV) adapting existing semantic resources such as the National Cancer Institute (NCI) Thesaurus. Using the global-as-view data integration approach, we created mapping axioms to link the data elements in different sources to OCRV. Implemented upon the Ontop platform, we built a data integration pipeline to query, extract, and transform data in relational databases using semantic queries into a pooled dataset according to the downstream multi-level Integrative Data Analysis (IDA) needs. RESULTS Based on our use cases in the cancer survival IDA, we created tailored ontological structures in OCRV to facilitate the data integration tasks. Specifically, we created a flexible framework addressing key integration challenges: (1) using a shared, controlled vocabulary to make data understandable to both human and computers, (2) explicitly modeling the semantic relationships makes it possible to compute and reason with the data, (3) linking patients to contextual and environmental factors through geographic variables, (4) being able to document the data manipulation and integration processes clearly in the ontologies. CONCLUSIONS Using an ontology-based data integration approach not only standardizes the definitions of data variables through a common, controlled vocabulary, but also makes the semantic relationships among variables from different sources explicit and clear to all users of the same datasets. Such an approach resolves the ambiguity in variable selection, extraction and integration processes and thus improve reproducibility of the IDA.
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Affiliation(s)
- Hansi Zhang
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building Suite 3228, 2004 Mowry Road, PO Box 100219, Gainesville, FL, 32610-0219, USA
| | - Yi Guo
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building Suite 3228, 2004 Mowry Road, PO Box 100219, Gainesville, FL, 32610-0219, USA
| | - Qian Li
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building Suite 3228, 2004 Mowry Road, PO Box 100219, Gainesville, FL, 32610-0219, USA
| | - Thomas J George
- Division of Hematology and Oncology, Department of Medicine, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Elizabeth Shenkman
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building Suite 3228, 2004 Mowry Road, PO Box 100219, Gainesville, FL, 32610-0219, USA
| | - François Modave
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building Suite 3228, 2004 Mowry Road, PO Box 100219, Gainesville, FL, 32610-0219, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building Suite 3228, 2004 Mowry Road, PO Box 100219, Gainesville, FL, 32610-0219, USA.
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Tapuria A, Bruland P, Delaney B, Kalra D, Curcin V. Comparison and transformation between CDISC ODM and EN13606 EHR standards in connecting EHR data with clinical trial research data. Digit Health 2018; 4:2055207618777676. [PMID: 29942639 PMCID: PMC6016569 DOI: 10.1177/2055207618777676] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Accepted: 04/13/2018] [Indexed: 01/01/2023] Open
Abstract
Objectives Integrating Electronic Health Record (EHR) systems into the field of clinical trials still contains several challenges and obstacles. Heterogeneous standards and specifications are used to represent healthcare and clinical trial information. Therefore, this work investigates the mapping and data interoperability between healthcare and research standards: EN13606 used for the EHRs and the Clinical Data Interchange Standards Consortium Operational Data Model (CDISC ODM) used for clinical research. Methods Based on the specifications of CDISC ODM 1.3.2 and EN13606, a mapping between the structure and components of both standards has been performed. Archetype Definition Language (ADL) forms built with the EN13606 editor were transformed to ODM XML and reviewed. As a proof of concept, clinical sample data has been transformed into ODM and imported into an electronic data capture system. Reverse transformation from ODM to ADL has also been performed and finally reviewed concerning map-ability. Results The mapping between EN13606 and CDISC ODM shows the similarities and differences between the components and overall record structure of the two standards. An EN13606 archetype corresponds with a group of items within CDISC ODM. Transformations of element names, descriptions, different languages, datatypes, cardinality, optionality, units, value range and terminology codes are possible from EN13606 to CDISC ODM and vice versa. Conclusion It is feasible to map data elements between EN13606 and CDISC ODM and transformation of forms between ADL and ODM XML format is possible with only minor limitations. EN13606 can accommodate clinical information in a more structured manner with more constraints, whereas CDISC ODM is more suitable and specific for clinical trials and studies. It is feasible to transform EHR data in the EN13606 form to ODM to transfer it into research database. The attempt to use EN13606 to build a study protocol (that was already built with CDISC ODM) also suggests the possibility of using EN13606 standard in place of CDISC ODM if needed to avoid transformations.
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16
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Hemingway H, Asselbergs FW, Danesh J, Dobson R, Maniadakis N, Maggioni A, van Thiel GJM, Cronin M, Brobert G, Vardas P, Anker SD, Grobbee DE, Denaxas S. Big data from electronic health records for early and late translational cardiovascular research: challenges and potential. Eur Heart J 2018; 39:1481-1495. [PMID: 29370377 PMCID: PMC6019015 DOI: 10.1093/eurheartj/ehx487] [Citation(s) in RCA: 129] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 07/19/2017] [Accepted: 08/08/2017] [Indexed: 12/13/2022] Open
Abstract
Aims Cohorts of millions of people's health records, whole genome sequencing, imaging, sensor, societal and publicly available data present a rapidly expanding digital trace of health. We aimed to critically review, for the first time, the challenges and potential of big data across early and late stages of translational cardiovascular disease research. Methods and results We sought exemplars based on literature reviews and expertise across the BigData@Heart Consortium. We identified formidable challenges including: data quality, knowing what data exist, the legal and ethical framework for their use, data sharing, building and maintaining public trust, developing standards for defining disease, developing tools for scalable, replicable science and equipping the clinical and scientific work force with new inter-disciplinary skills. Opportunities claimed for big health record data include: richer profiles of health and disease from birth to death and from the molecular to the societal scale; accelerated understanding of disease causation and progression, discovery of new mechanisms and treatment-relevant disease sub-phenotypes, understanding health and diseases in whole populations and whole health systems and returning actionable feedback loops to improve (and potentially disrupt) existing models of research and care, with greater efficiency. In early translational research we identified exemplars including: discovery of fundamental biological processes e.g. linking exome sequences to lifelong electronic health records (EHR) (e.g. human knockout experiments); drug development: genomic approaches to drug target validation; precision medicine: e.g. DNA integrated into hospital EHR for pre-emptive pharmacogenomics. In late translational research we identified exemplars including: learning health systems with outcome trials integrated into clinical care; citizen driven health with 24/7 multi-parameter patient monitoring to improve outcomes and population-based linkages of multiple EHR sources for higher resolution clinical epidemiology and public health. Conclusion High volumes of inherently diverse ('big') EHR data are beginning to disrupt the nature of cardiovascular research and care. Such big data have the potential to improve our understanding of disease causation and classification relevant for early translation and to contribute actionable analytics to improve health and healthcare.
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Affiliation(s)
- Harry Hemingway
- Research Department of Clinical Epidemiology, The Farr Institute of Health Informatics Research, University College London, 222 Euston Road, London NW1 2DA, UK
- The National Institute for Health Research, Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, University College London, 222 Euston Road, London NW1 2DA, UK
| | - Folkert W Asselbergs
- Research Department of Clinical Epidemiology, The Farr Institute of Health Informatics Research, University College London, 222 Euston Road, London NW1 2DA, UK
- The National Institute for Health Research, Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, University College London, 222 Euston Road, London NW1 2DA, UK
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
| | - John Danesh
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Worts Causeway, Cambridge CB1 8RN, UK
| | - Richard Dobson
- Research Department of Clinical Epidemiology, The Farr Institute of Health Informatics Research, University College London, 222 Euston Road, London NW1 2DA, UK
- The National Institute for Health Research, Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, University College London, 222 Euston Road, London NW1 2DA, UK
- NIHR Biomedical Research Centre for Mental Health (IOP), King‘s College London, De Crespigny Park, London SE5 8AF, UK
| | - Nikolaos Maniadakis
- European Society of Cardiology (ESC), 2035 Route des Colles, Les Templiers - CS 80179 Biot, 06903 Sophia Antipolis, France
| | - Aldo Maggioni
- European Society of Cardiology (ESC), 2035 Route des Colles, Les Templiers - CS 80179 Biot, 06903 Sophia Antipolis, France
| | - Ghislaine J M van Thiel
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
| | - Maureen Cronin
- Vifor Pharma Ltd, lughofstrasse 61, 8152 Glattbrugg, Zurich, Switzerland
| | - Gunnar Brobert
- Department of Epidemiology, Bayer Pharma AG, Müllerstrasse 178, 13353 Berlin, Germany
| | - Panos Vardas
- European Society of Cardiology (ESC), 2035 Route des Colles, Les Templiers - CS 80179 Biot, 06903 Sophia Antipolis, France
| | - Stefan D Anker
- Division of Cardiology and Metabolism—Heart Failure, Cachexia & Sarcopenia; Department of Cardiology (CVK), Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité University Medicine, Charitépl. 1, 10117 Berlin, Germany
- Department of Cardiology and Pneumology, University Medicine Göttingen (UMG), Robert-Koch-Strasse 40, 37099, Göttingen, Germany
| | - Diederick E Grobbee
- Julius Centre for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Spiros Denaxas
- Research Department of Clinical Epidemiology, The Farr Institute of Health Informatics Research, University College London, 222 Euston Road, London NW1 2DA, UK
- The National Institute for Health Research, Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, University College London, 222 Euston Road, London NW1 2DA, UK
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Ethier J, McGilchrist M, Barton A, Cloutier A, Curcin V, Delaney BC, Burgun A. The TRANSFoRm project: Experience and lessons learned regarding functional and interoperability requirements to support primary care. Learn Health Syst 2018; 2:e10037. [PMID: 31245579 PMCID: PMC6508823 DOI: 10.1002/lrh2.10037] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Revised: 07/05/2017] [Accepted: 07/12/2017] [Indexed: 01/02/2023] Open
Abstract
INTRODUCTION The current model of medical knowledge production, transfer, and application suffers from serious shortcomings. Learning health systems (LHS) have recently emerged as a potential solution-systems in which health information generated from patients is continuously analyzed to improve knowledge that will be transferred to patient care. METHOD Various approaches of data integration already exist and could be considered for the implementation of a LHS. We discuss what are the possible informatics approaches to address the functional requirements of LHS, in the specific context of primary care, and present the experience and lessons learned from the TRANSFoRm project. RESULT Implemented in 4 countries around 5 systems, TRANSFoRm is based on a local-as-view data mediation approach integrating the structural and terminological models in the same framework. It clearly demonstrated that it has the potential to address the requirements for a LHS in primary care, by dealing with data fragmented across multiple points of service. Also, it has the potential to support the generation of hypotheses from the context of clinical care, retrospective and prospective research, and decision support systems that improve the relevance of medical decisions. CONCLUSION The LHS approach embodies a shift from an institution-centered to a patient-centered perspective in knowledge production and transfer and can address important challenges in the primary care setting.
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Affiliation(s)
- Jean‐François Ethier
- Department of Medicine, Faculty of Medicine and Health SciencesUniversité de SherbrookeSherbrookeCanada
- INSERM UMR 1138 team 22 Centre de Recherche des Cordeliers, Faculté de médecineUniversité Paris Descartes—Sorbonne Paris CitéParisFrance
| | - Mark McGilchrist
- Division of Population Health SciencesUniversity of DundeeDundeeUK
| | - Adrien Barton
- Department of Medicine, Faculty of Medicine and Health SciencesUniversité de SherbrookeSherbrookeCanada
| | - Anne‐Marie Cloutier
- Department of Medicine, Faculty of Medicine and Health SciencesUniversité de SherbrookeSherbrookeCanada
| | - Vasa Curcin
- Division of Health and Social Care Research, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
| | - Brendan C. Delaney
- Department of Surgery and Cancer, Faculty of MedicineImperial College LondonLondonUK
| | - Anita Burgun
- INSERM UMR 1138 team 22 Centre de Recherche des Cordeliers, Faculté de médecineUniversité Paris Descartes—Sorbonne Paris CitéParisFrance
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Corrigan D, Munnelly G, Kazienko P, Kajdanowicz T, Soler J, Mahmoud S, Porat T, Kostopoulou O, Curcin V, Delaney B. Requirements and validation of a prototype learning health system for clinical diagnosis. Learn Health Syst 2017; 1:e10026. [PMID: 31245568 PMCID: PMC6508515 DOI: 10.1002/lrh2.10026] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Revised: 04/21/2017] [Accepted: 04/27/2017] [Indexed: 12/27/2022] Open
Abstract
INTRODUCTION Diagnostic error is a major threat to patient safety in the context of family practice. The patient safety implications are severe for both patient and clinician. Traditional approaches to diagnostic decision support have lacked broad acceptance for a number of well-documented reasons: poor integration with electronic health records and clinician workflow, static evidence that lacks transparency and trust, and use of proprietary technical standards hindering wider interoperability. The learning health system (LHS) provides a suitable infrastructure for development of a new breed of learning decision support tools. These tools exploit the potential for appropriate use of the growing volumes of aggregated sources of electronic health records. METHODS We describe the experiences of the TRANSFoRm project developing a diagnostic decision support infrastructure consistent with the wider goals of the LHS. We describe an architecture that is model driven, service oriented, constructed using open standards, and supports evidence derived from electronic sources of patient data. We describe the architecture and implementation of 2 critical aspects for a successful LHS: the model representation and translation of clinical evidence into effective practice and the generation of curated clinical evidence that can be used to populate those models, thus closing the LHS loop. RESULTS/CONCLUSIONS Six core design requirements for implementing a diagnostic LHS are identified and successfully implemented as part of this research work. A number of significant technical and policy challenges are identified for the LHS community to consider, and these are discussed in the context of evaluating this work: medico-legal responsibility for generated diagnostic evidence, developing trust in the LHS (particularly important from the perspective of decision support), and constraints imposed by clinical terminologies on evidence generation.
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Meystre SM, Lovis C, Bürkle T, Tognola G, Budrionis A, Lehmann CU. Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress. Yearb Med Inform 2017; 26:38-52. [PMID: 28480475 PMCID: PMC6239225 DOI: 10.15265/iy-2017-007] [Citation(s) in RCA: 84] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Indexed: 12/30/2022] Open
Abstract
Objective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research.
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Affiliation(s)
- S. M. Meystre
- Medical University of South Carolina, Charleston, SC, USA
| | - C. Lovis
- Division of Medical Information Sciences, University Hospitals of Geneva, Switzerland
| | - T. Bürkle
- University of Applied Sciences, Bern, Switzerland
| | - G. Tognola
- Institute of Electronics, Computer and Telecommunication Engineering, Italian Natl. Research Council IEIIT-CNR, Milan, Italy
| | - A. Budrionis
- Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway
| | - C. U. Lehmann
- Departments of Biomedical Informatics and Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
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20
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Burgun A, Bernal-Delgado E, Kuchinke W, van Staa T, Cunningham J, Lettieri E, Mazzali C, Oksen D, Estupiñan F, Barone A, Chène G. Health Data for Public Health: Towards New Ways of Combining Data Sources to Support Research Efforts in Europe. Yearb Med Inform 2017; 26:235-240. [PMID: 29063571 PMCID: PMC6239221 DOI: 10.15265/iy-2017-034] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Indexed: 12/21/2022] Open
Abstract
Objectives: To present the European landscape regarding the re-use of health administrative data for research. Methods: We present some collaborative projects and solutions that have been developed by Nordic countries, Italy, Spain, France, Germany, and the UK, to facilitate access to their health data for research purposes. Results: Research in public health is transitioning from siloed systems to more accessible and re-usable data resources. Following the example of the Nordic countries, several European countries aim at facilitating the re-use of their health administrative databases for research purposes. However, the ecosystem is still a complex patchwork, with different rules, policies, and processes for data provision. Conclusion: The challenges are such that with the abundance of health administrative data, only a European, overarching public health research infrastructure, is able to efficiently facilitate access to this data and accelerate research based on these highly valuable resources.
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Affiliation(s)
- A. Burgun
- Inserm, UMR 1138, Centre de Recherche des Cordeliers, AP-HP, Paris Descartes University, Paris, France
| | - E. Bernal-Delgado
- Institute for Health Sciences in Aragon (IACS), BridgeHealth Consortium, Zaragoza, Spain
| | - W. Kuchinke
- University of Dusseldorf, Dusseldorf, Germany
| | - T. van Staa
- Health eResearch Centre, Farr Institute, University of Manchester, Manchester, United Kingdom
| | - J. Cunningham
- Health eResearch Centre, Farr Institute, University of Manchester, Manchester, United Kingdom
| | | | | | - D. Oksen
- Public Health Institute, Inserm, AVIESAN, Paris, France
| | - F. Estupiñan
- Institute for Health Sciences in Aragon (IACS), BridgeHealth Consortium, Zaragoza, Spain
| | - A. Barone
- Lombardia Informatica, Milano, Italy
| | - G. Chène
- Inserm, UMR 1219, CIC1401-EC, Univ. Bordeaux, ISPED, CHU Bordeaux, Bordeaux, France
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21
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Curcin V. Embedding data provenance into the Learning Health System to facilitate reproducible research. Learn Health Syst 2017; 1:e10019. [PMID: 31245557 PMCID: PMC6516719 DOI: 10.1002/lrh2.10019] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Revised: 08/04/2016] [Accepted: 10/09/2016] [Indexed: 12/11/2022] Open
Abstract
INTRODUCTION The learning health system (LHS) community has taken up the challenge of bringing the complex relationship between clinical research and practice into this brave new world. At the heart of the LHS vision is the notion of routine capture, transformation, and dissemination of data and knowledge, with various use cases, such as clinical studies, quality improvement initiatives, and decision support, constructed on top of specific routes that the data is taking through the system. In order to stop this increased data volume and analytical complexity from obfuscating the research process, it is essential to establish trust in the system through implementing reproducibility and auditability throughout the workflow. METHODS Data provenance technologies can automatically capture the trace of the research task and resulting data, thereby facilitating reproducible research. While some computational domains, such as bioinformatics, have embraced the technology through provenance-enabled execution middlewares, disciplines based on distributed, heterogeneous software, such as medical research, are only starting on the road to adoption, motivated by the institutional pressures to improve transparency and reproducibility. RESULTS Guided by the experiences of the TRANSFoRm project, we present the opportunities that data provenance offers to the LHS community. We illustrate how provenance can facilitate documenting 21 CFR Part 11 compliance for Food and Drug Administration submissions and provide auditability for decisions made by the decision support tools and discuss the transformational effect of routine provenance capture on data privacy, study reporting, and publishing medical research. CONCLUSIONS If the scaling up of the LHS is to succeed, we have to embed mechanisms to verify trust in the system inside our research instruments. In the research world increasingly reliant on electronic tools, provenance gives us a lingua franca to achieve traceability, which we have shown to be essential to building these mechanisms. To realize the vision of making computable provenance a feasible approach to implementing reproducibility in the LHS, we have to provide viable mechanisms for adoption. These include defining meaningful provenance models for problem domains and also introducing provenance support to existing tools in a minimally invasive manner.
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Affiliation(s)
- Vasa Curcin
- Division of Health and Social Care ResearchKing's College LondonLondonUK
- Department of InformaticsKing's College LondonLondonUK
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22
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McGovern AP, Fieldhouse H, Tippu Z, Jones S, Munro N, de Lusignan S. Glucose test provenance recording in UK primary care: was that fasted or random? Diabet Med 2017; 34:93-98. [PMID: 26773331 DOI: 10.1111/dme.13067] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/11/2016] [Indexed: 11/29/2022]
Abstract
AIMS To describe the proportion of glucose tests with unrecorded provenance in routine primary care data and identify the impact on clinical practice. METHODS A cross-sectional analysis was conducted of blood glucose measurements from the Royal College of General Practitioner Research and Surveillance Centre database, which includes primary care records from >100 practices across England and Wales. All blood glucose results recorded during 2013 were identified. Tests were grouped by provenance (fasting, oral glucose tolerance test, random, none specified and other). A clinical audit in a single primary care practice was also performed to identify the impact of failing to record glucose provenance on diabetes diagnosis. RESULTS A total of 2 137 098 people were included in the cross-sectional analysis. Of 203 350 recorded glucose measurements the majority (117 893; 58%) did not have any provenance information. The most commonly reported provenance was fasting glucose (75 044; 37%). The distribution of glucose values where provenance was not recorded was most similar to that of fasting samples. The glucose measurements of 256 people with diabetes in the audit practice (size 11 514 people) were analysed. The initial glucose measurement had no provenance information in 164 cases (64.1%). A clinician questioned the provenance of a result in 41 cases (16.0%); of these, 14 (34.1%) required repeating. Lack of provenance led to delays in the diagnosis of diabetes [median (range) 30 (3-614) days]. CONCLUSIONS The recording of glucose provenance in UK primary care could be improved. Failure to record provenance causes unnecessary repeated testing, delayed diagnosis and wasted clinician time.
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Affiliation(s)
- A P McGovern
- Department of Clinical and Experimental Medicine, University of Surrey, Guildford
| | - H Fieldhouse
- Department of Clinical and Experimental Medicine, University of Surrey, Guildford
| | - Z Tippu
- Department of Clinical and Experimental Medicine, University of Surrey, Guildford
| | - S Jones
- Department of Clinical and Experimental Medicine, University of Surrey, Guildford
| | - N Munro
- Department of Clinical and Experimental Medicine, University of Surrey, Guildford
| | - S de Lusignan
- Department of Clinical and Experimental Medicine, University of Surrey, Guildford
- Clinical Innovation and Research Centre, Royal College of General Practitioners, London, UK
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Bouamrane MM, Tao C, Sarkar IN. Managing interoperability and complexity in health systems. Methods Inf Med 2016; 54:1-4. [PMID: 25579862 DOI: 10.3414/me15-10-0001] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
In recent years, we have witnessed substantial progress in the use of clinical informatics systems to support clinicians during episodes of care, manage specialised domain knowledge, perform complex clinical data analysis and improve the management of health organisations' resources. However, the vision of fully integrated health information eco-systems, which provide relevant information and useful knowledge at the point-of-care, remains elusive. This journal Focus Theme reviews some of the enduring challenges of interoperability and complexity in clinical informatics systems. Furthermore, a range of approaches are proposed in order to address, harness and resolve some of the many remaining issues towards a greater integration of health information systems and extraction of useful or new knowledge from heterogeneous electronic data repositories.
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Affiliation(s)
- M-M Bouamrane
- Dr. Matt-Mouley Bouamrane, Institute of Health & Well-being, University of Glasgow, General Practice & Primary Care, 1 Horslethill Road , Glasgow G12 9LX, UK, E-mail:
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Budrionis A, Bellika JG. The Learning Healthcare System: Where are we now? A systematic review. J Biomed Inform 2016; 64:87-92. [PMID: 27693565 DOI: 10.1016/j.jbi.2016.09.018] [Citation(s) in RCA: 131] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Revised: 09/14/2016] [Accepted: 09/27/2016] [Indexed: 11/26/2022]
Abstract
BACKGROUND AND OBJECTIVE The Learning Healthcare System paradigm has attracted the attention of researchers worldwide. The great potential originating from high-scale health data reuse and the inclusion of patient perspectives into care models promises personalized care, lower costs of health services and minimized consumption of resources. The aim of this review is to summarize the attempts to adopt the novel paradigm, putting emphasis on implementations and evaluating the impact on current medical practices. METHOD PRISMA methodology was followed for structuring the review process. Three major research databases (PubMed, IEEE Xplore and ACM DL) were queried with the predefined search terms "learning healthcare" and "learning health". Publications containing specific theoretical or empirical results were considered. RESULTS Three hundred and fifty-eight publications were identified; however, only 32 met the inclusion criteria. Nineteen papers were characterized as theoretical contributions, while the rest presented empirical achievements. Only one paper described the initial estimates of impact and economy. DISCUSSION Individualistic communication of studies ignoring popular frameworks for assessing and reporting research achievements prevents the systematic generation of knowledge. Evaluating the impact of the Learning Healthcare System instances where it is implemented could work as a catalyst in reaching higher acceptance and adoption of the proposed ideas by healthcare worldwide; however, it mostly remains described in theory. CONCLUSIONS The review demonstrated the interest of researchers in exploring the Learning Healthcare System ideas. However, it also revealed minimal focus on evaluating the impact of the novel paradigm on both healthcare service delivery and patient outcome.
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Affiliation(s)
- Andrius Budrionis
- Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway.
| | - Johan Gustav Bellika
- Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway
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Sullivan F. Atomic data: James Mackenzie Lecture 2015. Br J Gen Pract 2016; 66:e368-70. [PMID: 27127292 PMCID: PMC4838451 DOI: 10.3399/bjgp16x685153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Affiliation(s)
- Frank Sullivan
- Gordon F Cheesbrough Research Chair and Director of UTOPIAN, Toronto, Canada; FMTU, North York General Hospital, Toronto, Canada; Professor, Department of Family & Community Medicine and Dalla Lana School of Public Health, University of Toronto, Toronto, Canada; Adjunct Scientist Institute for Clinical Evaluative Sciences (ICES), Toronto, Canada; Honorary Professor, University of Dundee, Dundee, UK
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Ateya MB, Delaney BC, Speedie SM. The value of structured data elements from electronic health records for identifying subjects for primary care clinical trials. BMC Med Inform Decis Mak 2016; 16:1. [PMID: 26754574 PMCID: PMC4709934 DOI: 10.1186/s12911-016-0239-x] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2015] [Accepted: 01/06/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND An increasing number of clinical trials are conducted in primary care settings. Making better use of existing data in the electronic health records to identify eligible subjects can improve efficiency of such studies. Our study aims to quantify the proportion of eligibility criteria that can be addressed with data in electronic health records and to compare the content of eligibility criteria in primary care with previous work. METHODS Eligibility criteria were extracted from primary care studies downloaded from the UK Clinical Research Network Study Portfolio. Criteria were broken into elemental statements. Two expert independent raters classified each statement based on whether or not structured data items in the electronic health record can be used to determine if the statement was true for a specific patient. Disagreements in classification were discussed until 100 % agreement was reached. Statements were also classified based on content and the percentages of each category were compared to two similar studies reported in the literature. RESULTS Eligibility criteria were retrieved from 228 studies and decomposed into 2619 criteria elemental statements. 74 % of the criteria elemental statements were considered likely associated with structured data in an electronic health record. 79 % of the studies had at least 60 % of their criteria statements addressable with structured data likely to be present in an electronic health record. Based on clinical content, most frequent categories were: "disease, symptom, and sign", "therapy or surgery", and "medication" (36 %, 13 %, and 10 % of total criteria statements respectively). We also identified new criteria categories related to provider and caregiver attributes (2.6 % and 1 % of total criteria statements respectively). CONCLUSIONS Electronic health records readily contain much of the data needed to assess patients' eligibility for clinical trials enrollment. Eligibility criteria content categories identified by our study can be incorporated as data elements in electronic health records to facilitate their integration with clinical trial management systems.
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Affiliation(s)
- Mohammad B Ateya
- University of Michigan Health System, University of Michigan MCIT, 24 Frank Lloyd Wright Dr., Lobby J Suite 4000, Ann Arbor, MI, 48105, USA.
| | | | - Stuart M Speedie
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
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Translational Medicine and Patient Safety in Europe: TRANSFoRm--Architecture for the Learning Health System in Europe. BIOMED RESEARCH INTERNATIONAL 2015; 2015:961526. [PMID: 26539547 PMCID: PMC4619923 DOI: 10.1155/2015/961526] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2015] [Accepted: 06/08/2015] [Indexed: 11/17/2022]
Abstract
The Learning Health System (LHS) describes linking routine healthcare systems directly with both research translation and knowledge translation as an extension of the evidence-based medicine paradigm, taking advantage of the ubiquitous use of electronic health record (EHR) systems. TRANSFoRm is an EU FP7 project that seeks to develop an infrastructure for the LHS in European primary care. Methods. The project is based on three clinical use cases, a genotype-phenotype study in diabetes, a randomised controlled trial with gastroesophageal reflux disease, and a diagnostic decision support system for chest pain, abdominal pain, and shortness of breath. Results. Four models were developed (clinical research, clinical data, provenance, and diagnosis) that form the basis of the projects approach to interoperability. These models are maintained as ontologies with binding of terms to define precise data elements. CDISC ODM and SDM standards are extended using an archetype approach to enable a two-level model of individual data elements, representing both research content and clinical content. Separate configurations of the TRANSFoRm tools serve each use case. Conclusions. The project has been successful in using ontologies and archetypes to develop a highly flexible solution to the problem of heterogeneity of data sources presented by the LHS.
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