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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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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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Hay AD, Moore MV, Taylor J, Turner N, Noble S, Cabral C, Horwood J, Prasad V, Curtis K, Delaney B, Damoiseaux R, Domínguez J, Tapuria A, Harris S, Little P, Lovering A, Morris R, Rowley K, Sadoo A, Schilder A, Venekamp R, Wilkes S, Curcin V. Immediate oral versus immediate topical versus delayed oral antibiotics for children with acute otitis media with discharge: the REST three-arm non-inferiority electronic platform-supported RCT. Health Technol Assess 2021; 25:1-76. [PMID: 34816795 DOI: 10.3310/hta25670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Acute otitis media is a painful infection of the middle ear that is commonly seen in children. In some children, the eardrum spontaneously bursts, discharging visible pus (otorrhoea) into the outer ear. OBJECTIVE To compare the clinical effectiveness of immediate topical antibiotics or delayed oral antibiotics with the clinical effectiveness of immediate oral antibiotics in reducing symptom duration in children presenting to primary care with acute otitis media with discharge and the economic impact of the alternative strategies. DESIGN This was a pragmatic, three-arm, individually randomised (stratified by age < 2 vs. ≥ 2 years), non-inferiority, open-label trial, with economic and qualitative evaluations, supported by a health-record-integrated electronic trial platform [TRANSFoRm (Translational Research and Patient Safety in Europe)] with an internal pilot. SETTING A total of 44 English general practices. PARTICIPANTS Children aged ≥ 12 months and < 16 years whose parents (or carers) were seeking medical care for unilateral otorrhoea (ear discharge) following recent-onset (≤ 7 days) acute otitis media. INTERVENTIONS (1) Immediate ciprofloxacin (0.3%) solution, four drops given three times daily for 7 days, or (2) delayed 'dose-by-age' amoxicillin suspension given three times daily (clarithromycin twice daily if the child was penicillin allergic) for 7 days, with structured delaying advice. All parents were given standardised information regarding symptom management (paracetamol/ibuprofen/fluids) and advice to complete the course. COMPARATOR Immediate 'dose-by-age' oral amoxicillin given three times daily (or clarithromycin given twice daily) for 7 days. Parents received standardised symptom management advice along with advice to complete the course. MAIN OUTCOME MEASURE Time from randomisation to the first day on which all symptoms (pain, fever, being unwell, sleep disturbance, otorrhoea and episodes of distress/crying) were rated 'no' or 'very slight' problem (without need for analgesia). METHODS Participants were recruited from routine primary care appointments. The planned sample size was 399 children. Follow-up used parent-completed validated symptom diaries. RESULTS Delays in software deployment and configuration led to small recruitment numbers and trial closure at the end of the internal pilot. Twenty-two children (median age 5 years; 62% boys) were randomised: five, seven and 10 to immediate oral, delayed oral and immediate topical antibiotics, respectively. All children received prescriptions as randomised. Seven (32%) children fully adhered to the treatment as allocated. Symptom duration data were available for 17 (77%) children. The median (interquartile range) number of days until symptom resolution in the immediate oral, delayed oral and immediate topical antibiotic arms was 6 (4-9), 4 (3-7) and 4 (3-6), respectively. Comparative analyses were not conducted because of small numbers. There were no serious adverse events and six reports of new or worsening symptoms. Qualitative clinician interviews showed that the trial question was important. When the platform functioned as intended, it was liked. However, staff reported malfunctioning software for long periods, resulting in missed recruitment opportunities. Troubleshooting the software placed significant burdens on staff. LIMITATIONS The over-riding weakness was the failure to recruit enough children. CONCLUSIONS We were unable to answer the main research question because of a failure to reach the required sample size. Our experience of running an electronic platform-supported trial in primary care has highlighted challenges from which we have drawn recommendations for the National Institute for Health Research (NIHR) and the research community. These should be considered before such a platform is used again. TRIAL REGISTRATION Current Controlled Trials ISRCTN12873692 and EudraCT 2017-003635-10. FUNDING This project was funded by the NIHR Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 67. See the NIHR Journals Library website for further project information.
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Affiliation(s)
- Alastair D Hay
- Centre for Academic Primary Care, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Michael V Moore
- Primary Care and Population Sciences, University of Southampton, Southampton, UK
| | - Jodi Taylor
- Bristol Randomised Trials Collaboration, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Nicholas Turner
- Bristol Randomised Trials Collaboration, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Sian Noble
- Bristol Randomised Trials Collaboration, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Christie Cabral
- Centre for Academic Primary Care, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jeremy Horwood
- Centre for Academic Primary Care, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Vibhore Prasad
- School of Population Health and Environmental Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Kathryn Curtis
- Bristol Randomised Trials Collaboration, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Brendan Delaney
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Roger Damoiseaux
- Julius Center for Health Sciences and Primary Care & Department of Otorhinolaryngology, UMC Utrecht, Utrecht, the Netherlands
| | - Jesús Domínguez
- School of Population Health and Environmental Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Archana Tapuria
- School of Population Health and Environmental Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Sue Harris
- Bristol Randomised Trials Collaboration, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Paul Little
- Primary Care and Population Sciences, University of Southampton, Southampton, UK
| | - Andrew Lovering
- Department of Medical Microbiology, North Bristol NHS Trust, Bristol, UK
| | - Richard Morris
- Bristol Randomised Trials Collaboration, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Kate Rowley
- Bristol Randomised Trials Collaboration, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Annie Sadoo
- Bristol Randomised Trials Collaboration, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Anne Schilder
- Ear Institute, University College London, London, UK
| | - Roderick Venekamp
- Julius Center for Health Sciences and Primary Care & Department of Otorhinolaryngology, UMC Utrecht, Utrecht, the Netherlands
| | - Scott Wilkes
- School of Medicine, University of Sunderland, Sunderland, UK
| | - Vasa Curcin
- School of Population Health and Environmental Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
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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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Mate S, Bürkle T, Kapsner LA, Toddenroth D, Kampf MO, Sedlmayr M, Castellanos I, Prokosch HU, Kraus S. A method for the graphical modeling of relative temporal constraints. J Biomed Inform 2019; 100:103314. [PMID: 31629921 DOI: 10.1016/j.jbi.2019.103314] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2019] [Revised: 08/13/2019] [Accepted: 10/14/2019] [Indexed: 02/06/2023]
Abstract
Searching for patient cohorts in electronic patient data often requires the definition of temporal constraints between the selection criteria. However, beyond a certain degree of temporal complexity, the non-graphical, form-based approaches implemented in current translational research platforms may be limited when modeling such constraints. In our opinion, there is a need for an easily accessible and implementable, fully graphical method for creating temporal queries. We aim to respond to this challenge with a new graphical notation. Based on Allen's time interval algebra, it allows for modeling temporal queries by arranging simple horizontal bars depicting symbolic time intervals. To make our approach applicable to complex temporal patterns, we apply two extensions: with duration intervals, we enable the inference about relative temporal distances between patient events, and with time interval modifiers, we support counting and excluding patient events, as well as constraining numeric values. We describe how to generate database queries from this notation. We provide a prototypical implementation, consisting of a temporal query modeling frontend and an experimental backend that connects to an i2b2 system. We evaluate our modeling approach on the MIMIC-III database to demonstrate that it can be used for modeling typical temporal phenotyping queries.
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Affiliation(s)
- Sebastian Mate
- Medical Centre for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany.
| | - Thomas Bürkle
- Bern University of Applied Sciences, Biel, Switzerland
| | - Lorenz A Kapsner
- Medical Centre for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Dennis Toddenroth
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Marvin O Kampf
- Medical Centre for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Martin Sedlmayr
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Ixchel Castellanos
- Department of Anesthesiology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Hans-Ulrich Prokosch
- Medical Centre for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany; Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Stefan Kraus
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
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Jaulent MC, Leprovost D, Charlet J, Choquet R. Semantic interoperability challenges to process large amount of data perspectives in forensic and legal medicine. J Forensic Leg Med 2018; 57:19-23. [PMID: 29801946 DOI: 10.1016/j.jflm.2016.10.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Revised: 10/06/2016] [Accepted: 10/07/2016] [Indexed: 10/20/2022]
Abstract
This article is a position paper dealing with semantic interoperability challenges. It addresses the Variety and Veracity dimensions when integrating, sharing and reusing large amount of heterogeneous data for data analysis and decision making applications in the healthcare domain. Many issues are raised by the necessity to conform Big Data to interoperability standards. We discuss how semantics can contribute to the improvement of information sharing and address the problem of data mediation with domain ontologies. We then introduce the main steps for building domain ontologies as they could be implemented in the context of Forensic and Legal medicine. We conclude with a particular emphasis on the current limitations in standardisation and the importance of knowledge formalization.
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Affiliation(s)
- Marie-Christine Jaulent
- INSERM, U1142, LIMICS, F-75006, Paris, France; Sorbonne Universités, UPMC Université Paris 06, UMR_S 1142, LIMICS, F-75006, Paris, France; Université Paris 13, Sorbonne Paris Cité, UMR_S 1142, LIMICS, F-93430, Villetaneuse, France.
| | - Damien Leprovost
- INSERM, U1142, LIMICS, F-75006, Paris, France; Sorbonne Universités, UPMC Université Paris 06, UMR_S 1142, LIMICS, F-75006, Paris, France; Université Paris 13, Sorbonne Paris Cité, UMR_S 1142, LIMICS, F-93430, Villetaneuse, France
| | - Jean Charlet
- INSERM, U1142, LIMICS, F-75006, Paris, France; Sorbonne Universités, UPMC Université Paris 06, UMR_S 1142, LIMICS, F-75006, Paris, France; Université Paris 13, Sorbonne Paris Cité, UMR_S 1142, LIMICS, F-93430, Villetaneuse, France; AP-HP, Assistance Publique Hôpitaux de Paris, Paris, France
| | - Remy Choquet
- INSERM, U1142, LIMICS, F-75006, Paris, France; Sorbonne Universités, UPMC Université Paris 06, UMR_S 1142, LIMICS, F-75006, Paris, France; Université Paris 13, Sorbonne Paris Cité, UMR_S 1142, LIMICS, F-93430, Villetaneuse, France; BNDMR, Assistance Publique Hôpitaux de Paris, Hôpital Necker Enfants Malades, Paris, France
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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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Ethier JF, Curcin V, McGilchrist MM, Choi Keung SNL, Zhao L, Andreasson A, Bródka P, Michalski R, Arvanitis TN, Mastellos N, Burgun A, Delaney BC. eSource for clinical trials: Implementation and evaluation of a standards-based approach in a real world trial. Int J Med Inform 2017; 106:17-24. [PMID: 28870379 DOI: 10.1016/j.ijmedinf.2017.06.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Revised: 06/20/2017] [Accepted: 06/24/2017] [Indexed: 01/24/2023]
Abstract
OBJECTIVE The Learning Health System (LHS) requires integration of research into routine practice. 'eSource' or embedding clinical trial functionalities into routine electronic health record (EHR) systems has long been put forward as a solution to the rising costs of research. We aimed to create and validate an eSource solution that would be readily extensible as part of a LHS. MATERIALS AND METHODS The EU FP7 TRANSFoRm project's approach is based on dual modelling, using the Clinical Research Information Model (CRIM) and the Clinical Data Integration Model of meaning (CDIM) to bridge the gap between clinical and research data structures, using the CDISC Operational Data Model (ODM) standard. Validation against GCP requirements was conducted in a clinical site, and a cluster randomised evaluation by site nested into a live clinical trial. RESULTS Using the form definition element of ODM, we linked precisely modelled data queries to data elements, constrained against CDIM concepts, to enable automated patient identification for specific protocols and pre-population of electronic case report forms (e-CRF). Both control and eSource sites recruited better than expected with no significant difference. Completeness of clinical forms was significantly improved by eSource, but Patient Related Outcome Measures (PROMs) were less well completed on smartphones than paper in this population. DISCUSSION The TRANSFoRm approach provides an ontologically-based approach to eSource in a low-resource, heterogeneous, highly distributed environment, that allows precise prospective mapping of data elements in the EHR. CONCLUSION Further studies using this approach to CDISC should optimise the delivery of PROMS, whilst building a sustainable infrastructure for eSource with research networks, trials units and EHR vendors.
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Affiliation(s)
| | - Vasa Curcin
- Department of Informatics, King's College London, London, United Kingdom.
| | | | | | - Lei Zhao
- Institute of Digital Healthcare, WMG, University of Warwick, Coventry, United Kingdom.
| | - Anna Andreasson
- Division of Family Medicine and Primary Care, Karolinska Institute Stockholm, Sweden.
| | - Piotr Bródka
- Department of Computational Intelligence, Wroclaw Institute of Science and Technology, Wroclaw, Poland.
| | - Radoslaw Michalski
- Department of Computational Intelligence, Wroclaw Institute of Science and Technology, Wroclaw, Poland.
| | - Theodoros N Arvanitis
- Institute of Digital Healthcare, WMG, University of Warwick, Coventry, United Kingdom.
| | - Nikolaos Mastellos
- Department of Primary Care and Public Health, Imperial College London, London, United Kingdom.
| | - Anita Burgun
- INSERM 1138, eq 22 Université Paris-Descartes, Paris, France.
| | - Brendan C Delaney
- Institute of Global Health Innovation, Department of Surgery and Cancer Imperial College London, London, United Kingdom.
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Computer-Aided Clinical Trial Recruitment Based on Domain-Specific Language Translation: A Case Study of Retinopathy of Prematurity. JOURNAL OF HEALTHCARE ENGINEERING 2017; 2017:7862672. [PMID: 29065644 PMCID: PMC5396472 DOI: 10.1155/2017/7862672] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Accepted: 02/05/2017] [Indexed: 11/25/2022]
Abstract
Reusing the data from healthcare information systems can effectively facilitate clinical trials (CTs). How to select candidate patients eligible for CT recruitment criteria is a central task. Related work either depends on DBA (database administrator) to convert the recruitment criteria to native SQL queries or involves the data mapping between a standard ontology/information model and individual data source schema. This paper proposes an alternative computer-aided CT recruitment paradigm, based on syntax translation between different DSLs (domain-specific languages). In this paradigm, the CT recruitment criteria are first formally represented as production rules. The referenced rule variables are all from the underlying database schema. Then the production rule is translated to an intermediate query-oriented DSL (e.g., LINQ). Finally, the intermediate DSL is directly mapped to native database queries (e.g., SQL) automated by ORM (object-relational mapping).
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11
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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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12
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Yuksel M, Gonul S, Laleci Erturkmen GB, Sinaci AA, Invernizzi P, Facchinetti S, Migliavacca A, Bergvall T, Depraetere K, De Roo J. An Interoperability Platform Enabling Reuse of Electronic Health Records for Signal Verification Studies. BIOMED RESEARCH INTERNATIONAL 2016; 2016:6741418. [PMID: 27123451 PMCID: PMC4830705 DOI: 10.1155/2016/6741418] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2015] [Accepted: 10/04/2015] [Indexed: 01/17/2023]
Abstract
Depending mostly on voluntarily sent spontaneous reports, pharmacovigilance studies are hampered by low quantity and quality of patient data. Our objective is to improve postmarket safety studies by enabling safety analysts to seamlessly access a wide range of EHR sources for collecting deidentified medical data sets of selected patient populations and tracing the reported incidents back to original EHRs. We have developed an ontological framework where EHR sources and target clinical research systems can continue using their own local data models, interfaces, and terminology systems, while structural interoperability and Semantic Interoperability are handled through rule-based reasoning on formal representations of different models and terminology systems maintained in the SALUS Semantic Resource Set. SALUS Common Information Model at the core of this set acts as the common mediator. We demonstrate the capabilities of our framework through one of the SALUS safety analysis tools, namely, the Case Series Characterization Tool, which have been deployed on top of regional EHR Data Warehouse of the Lombardy Region containing about 1 billion records from 16 million patients and validated by several pharmacovigilance researchers with real-life cases. The results confirm significant improvements in signal detection and evaluation compared to traditional methods with the missing background information.
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Affiliation(s)
- Mustafa Yuksel
- SRDC Software Research & Development and Consultancy Ltd., 06800 Ankara, Turkey
| | - Suat Gonul
- SRDC Software Research & Development and Consultancy Ltd., 06800 Ankara, Turkey
- Department of Computer Engineering, Middle East Technical University, 06800 Ankara, Turkey
| | | | - Ali Anil Sinaci
- SRDC Software Research & Development and Consultancy Ltd., 06800 Ankara, Turkey
| | - Paolo Invernizzi
- Lombardia Informatica S.p.A., Via Torquato Taramelli, 26 20124 Milano, Italy
| | - Sara Facchinetti
- Lombardia Informatica S.p.A., Via Torquato Taramelli, 26 20124 Milano, Italy
| | - Andrea Migliavacca
- Lombardia Informatica S.p.A., Via Torquato Taramelli, 26 20124 Milano, Italy
| | - Tomas Bergvall
- WHO Collaborating Centre for International Drug Monitoring, Uppsala Monitoring Centre (UMC), 753 20 Uppsala, Sweden
| | - Kristof Depraetere
- Advanced Clinical Applications Research Group, Agfa HealthCare, 9000 Gent, Belgium
| | - Jos De Roo
- Advanced Clinical Applications Research Group, Agfa HealthCare, 9000 Gent, Belgium
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Yoon D, Schuemie MJ, Kim JH, Kim DK, Park MY, Ahn EK, Jung EY, Park DK, Cho SY, Shin D, Hwang Y, Park RW. A normalization method for combination of laboratory test results from different electronic healthcare databases in a distributed research network. Pharmacoepidemiol Drug Saf 2015; 25:307-16. [PMID: 26527579 DOI: 10.1002/pds.3893] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2015] [Revised: 08/18/2015] [Accepted: 09/22/2015] [Indexed: 11/10/2022]
Abstract
PURPOSE Distributed research networks (DRNs) afford statistical power by integrating observational data from multiple partners for retrospective studies. However, laboratory test results across care sites are derived using different assays from varying patient populations, making it difficult to simply combine data for analysis. Additionally, existing normalization methods are not suitable for retrospective studies. We normalized laboratory results from different data sources by adjusting for heterogeneous clinico-epidemiologic characteristics of the data and called this the subgroup-adjusted normalization (SAN) method. METHODS Subgroup-adjusted normalization renders the means and standard deviations of distributions identical under population structure-adjusted conditions. To evaluate its performance, we compared SAN with existing methods for simulated and real datasets consisting of blood urea nitrogen, serum creatinine, hematocrit, hemoglobin, serum potassium, and total bilirubin. Various clinico-epidemiologic characteristics can be applied together in SAN. For simplicity of comparison, age and gender were used to adjust population heterogeneity in this study. RESULTS In simulations, SAN had the lowest standardized difference in means (SDM) and Kolmogorov-Smirnov values for all tests (p < 0.05). In a real dataset, SAN had the lowest SDM and Kolmogorov-Smirnov values for blood urea nitrogen, hematocrit, hemoglobin, and serum potassium, and the lowest SDM for serum creatinine (p < 0.05). CONCLUSION Subgroup-adjusted normalization performed better than normalization using other methods. The SAN method is applicable in a DRN environment and should facilitate analysis of data integrated across DRN partners for retrospective observational studies.
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Affiliation(s)
- Dukyong Yoon
- Department of Biomedical Informatics, Ajou University School of Medicine, Ajou University, Suwon, Korea.,Observational Health Data Sciences and Informatics, New York, NY, USA
| | - Martijn J Schuemie
- Observational Health Data Sciences and Informatics, New York, NY, USA.,Janssen Research and Development LLC, Titusville, FL, USA
| | - Ju Han Kim
- Seoul National University Biomedical Informatics, Seoul National University College of Medicine, Seoul, Korea
| | - Dong Ki Kim
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Man Young Park
- Mibyeong Research Center, Korea Institute of Oriental Medicine, Daejeon, Korea
| | - Eun Kyoung Ahn
- Department of Biomedical Informatics, Ajou University School of Medicine, Ajou University, Suwon, Korea
| | - Eun-Young Jung
- Centre for u-Healthcare, Gachon University Gil Hospital, Korea
| | - Dong Kyun Park
- Centre for u-Healthcare, Gachon University Gil Hospital, Korea
| | - Soo Yeon Cho
- Department of Biomedical Informatics, Ajou University School of Medicine, Ajou University, Suwon, Korea
| | - Dahye Shin
- Department of Biomedical Informatics, Ajou University School of Medicine, Ajou University, Suwon, Korea
| | - Yeonsoo Hwang
- Center for Medical Informatics, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Rae Woong Park
- Department of Biomedical Informatics, Ajou University School of Medicine, Ajou University, Suwon, Korea.,Observational Health Data Sciences and Informatics, New York, NY, USA
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Noor AM, Holmberg L, Gillett C, Grigoriadis A. Big Data: the challenge for small research groups in the era of cancer genomics. Br J Cancer 2015; 113:1405-12. [PMID: 26492224 PMCID: PMC4815885 DOI: 10.1038/bjc.2015.341] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2015] [Revised: 08/04/2015] [Accepted: 08/09/2015] [Indexed: 01/06/2023] Open
Abstract
In the past decade, cancer research has seen an increasing trend towards high-throughput techniques and translational approaches. The increasing availability of assays that utilise smaller quantities of source material and produce higher volumes of data output have resulted in the necessity for data storage solutions beyond those previously used. Multifactorial data, both large in sample size and heterogeneous in context, needs to be integrated in a standardised, cost-effective and secure manner. This requires technical solutions and administrative support not normally financially accounted for in small- to moderate-sized research groups. In this review, we highlight the Big Data challenges faced by translational research groups in the precision medicine era; an era in which the genomes of over 75 000 patients will be sequenced by the National Health Service over the next 3 years to advance healthcare. In particular, we have looked at three main themes of data management in relation to cancer research, namely (1) cancer ontology management, (2) IT infrastructures that have been developed to support data management and (3) the unique ethical challenges introduced by utilising Big Data in research.
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Affiliation(s)
- Aisyah Mohd Noor
- Research Oncology, Faculty of Life Sciences and Medicine, King's College London, Guy's Hospital, London SE1 9RT, UK
| | - Lars Holmberg
- Research Oncology, Faculty of Life Sciences and Medicine, King's College London, Guy's Hospital, London SE1 9RT, UK.,Department of Surgical Sciences, Uppsala University, Uppsala 751 85, Sweden
| | - Cheryl Gillett
- Research Oncology, Faculty of Life Sciences and Medicine, King's College London, Guy's Hospital, London SE1 9RT, UK.,Faculty of Life Sciences and Medicine, King's Health Partners Cancer Biobank, King's College London, Research Oncology, Guy's Hospital, London SE1 9RT, UK
| | - Anita Grigoriadis
- Research Oncology, Faculty of Life Sciences and Medicine, King's College London, Guy's Hospital, London SE1 9RT, UK.,Breast Cancer Now Research Unit, Research Oncology, Faculty of Life Sciences and Medicine, King's College London, Guy's Hospital, London SE1 9RT, UK
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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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Corrigan D, McDonnell R, Zarabzadeh A, Fahey T. A Multistep Maturity Model for the Implementation of Electronic and Computable Diagnostic Clinical Prediction Rules (eCPRs). EGEMS 2015; 3:1153. [PMID: 26290890 PMCID: PMC4537149 DOI: 10.13063/2327-9214.1153] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Introduction: The use of Clinical Prediction Rules (CPRs) has been advocated as one way of implementing actionable evidence-based rules in clinical practice. The current highly manual nature of deriving CPRs makes them difficult to use and maintain. Addressing the known limitations of CPRs requires implementing more flexible and dynamic models of CPR development. We describe the application of Information and Communication Technology (ICT) to provide a platform for the derivation and dissemination of CPRs derived through analysis and continual learning from electronic patient data. Model Components: We propose a multistep maturity model for constructing electronic and computable CPRs (eCPRs). The model has six levels – from the lowest level of CPR maturity (literaturebased CPRs) to a fully electronic and computable service-oriented model of CPRs that are sensitive to specific demographic patient populations. We describe examples of implementations of the core model components – focusing on CPR representation, interoperability, electronic dissemination, CPR learning, and user interface requirements. Conclusion: The traditional focus on derivation and narrow validation of CPRs has severely limited their wider acceptance. The evolution and maturity model described here outlines a progression toward eCPRs consistent with the vision of a learning health system (LHS) – using central repositories of CPR knowledge, accessible open standards, and generalizable models to avoid repetition of previous work. This is useful for developing more ambitious strategies to address limitations of the traditional CPR development life cycle. The model described here is a starting point for promoting discussion about what a more dynamic CPR development process should look like.
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Affiliation(s)
- Derek Corrigan
- HRB Centre for Primary Care Research, RCSI Medical School, Dublin
| | - Ronan McDonnell
- HRB Centre for Primary Care Research, RCSI Medical School, Dublin
| | - Atieh Zarabzadeh
- HRB Centre for Primary Care Research, RCSI Medical School, Dublin
| | - Tom Fahey
- HRB Centre for Primary Care Research, RCSI Medical School, Dublin
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Ontology-based data integration between clinical and research systems. PLoS One 2015; 10:e0116656. [PMID: 25588043 PMCID: PMC4294641 DOI: 10.1371/journal.pone.0116656] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2014] [Accepted: 12/06/2014] [Indexed: 12/17/2022] Open
Abstract
Data from the electronic medical record comprise numerous structured but uncoded ele-ments, which are not linked to standard terminologies. Reuse of such data for secondary research purposes has gained in importance recently. However, the identification of rele-vant data elements and the creation of database jobs for extraction, transformation and loading (ETL) are challenging: With current methods such as data warehousing, it is not feasible to efficiently maintain and reuse semantically complex data extraction and trans-formation routines. We present an ontology-supported approach to overcome this challenge by making use of abstraction: Instead of defining ETL procedures at the database level, we use ontologies to organize and describe the medical concepts of both the source system and the target system. Instead of using unique, specifically developed SQL statements or ETL jobs, we define declarative transformation rules within ontologies and illustrate how these constructs can then be used to automatically generate SQL code to perform the desired ETL procedures. This demonstrates how a suitable level of abstraction may not only aid the interpretation of clinical data, but can also foster the reutilization of methods for un-locking it.
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Kuchinke W, Karakoyun T, Gengler S, Ohmann C. First Clinical Research Informatics (CRI) Solutions Day: advanced IT support from EU projects for clinical trials. J Clin Bioinforma 2015. [PMCID: PMC4460745 DOI: 10.1186/2043-9113-5-s1-a1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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20
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Kuchinke W, Karakoyun T, Ohmann C, Arvanitis TN, Taweel A, Delaney BC, Speedie SM. Extension of the primary care research object model (PCROM) as clinical research information model (CRIM) for the "learning healthcare system". BMC Med Inform Decis Mak 2014; 14:118. [PMID: 25519481 PMCID: PMC4276023 DOI: 10.1186/s12911-014-0118-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2013] [Accepted: 12/04/2014] [Indexed: 11/17/2022] Open
Abstract
Background Patient data from general practices is already used for many types of epidemiological research and increasingly, primary care systems to facilitate randomized clinical trials. The EU funded project TRANSFoRm aims to create a “Learning Healthcare System” at a European level that is able to support all types of research using primary care data, to recruit patients and follow patients in clinical studies and to improve diagnosis and therapy. The implementation of such a Learning Healthcare System needs an information model for clinical research (CRIM), as an informational backbone to integrate aspects of primary care with clinical trials and database searches. Methods Workflow descriptions and corresponding data objects of two clinical use cases (Gastro-Oesophageal Reflux Disease and Type 2 Diabetes) were described in UML activity diagrams. The components of activity diagrams were mapped to information objects of PCROM (Primary Care Research Object Model) and BRIDG (Biomedical Research Integrated Domain Group) and evaluated. The class diagram of PCROM was adapted to comply with workflow descriptions. Results The suitability of PCROM, a primary care information model already used for clinical trials, to act as an information model for TRANSFoRm was evaluated and resulted in its extension with 14 new information object types, two extensions of existing objects and the introduction of two new high-ranking concepts (CARE area and ENTRY area). No PCROM component was redundant. Our result illustrates that in primary care based research an important but underestimated portion of research activity takes place in the area of care (e.g. patient consultation, screening, recruitment and response to adverse events). The newly introduced CARE area for care-related research activities accounts for this shift and includes Episode of Care and Encounter as two new basic elements. In the ENTRY area different aspects of data collection were combined, including data semantics for observations, assessment activities, intervention activities and patient reporting to enable case report form (CRF) based data collection combined with decision support. Conclusions Research with primary care data needs an extended information model that covers research activities at the care site which are characteristic for primary care based research and the requirements of the complicated data collection processes.
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Christoph J, Griebel L, Leb I, Engel I, Köpcke F, Toddenroth D, Prokosch HU, Laufer J, Marquardt K, Sedlmayr M. Secure Secondary Use of Clinical Data with Cloud-based NLP Services. Towards a Highly Scalable Research Infrastructure. Methods Inf Med 2014; 54:276-82. [PMID: 25377309 DOI: 10.3414/me13-01-0133] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2013] [Accepted: 10/08/2014] [Indexed: 01/26/2023]
Abstract
OBJECTIVES The secondary use of clinical data provides large opportunities for clinical and translational research as well as quality assurance projects. For such purposes, it is necessary to provide a flexible and scalable infrastructure that is compliant with privacy requirements. The major goals of the cloud4health project are to define such an architecture, to implement a technical prototype that fulfills these requirements and to evaluate it with three use cases. METHODS The architecture provides components for multiple data provider sites such as hospitals to extract free text as well as structured data from local sources and de-identify such data for further anonymous or pseudonymous processing. Free text documentation is analyzed and transformed into structured information by text-mining services, which are provided within a cloud-computing environment. Thus, newly gained annotations can be integrated along with the already available structured data items and the resulting data sets can be uploaded to a central study portal for further analysis. RESULTS Based on the architecture design, a prototype has been implemented and is under evaluation in three clinical use cases. Data from several hundred patients provided by a University Hospital and a private hospital chain have already been processed. CONCLUSIONS Cloud4health has shown how existing components for secondary use of structured data can be complemented with text-mining in a privacy compliant manner. The cloud-computing paradigm allows a flexible and dynamically adaptable service provision that facilitates the adoption of services by data providers without own investments in respective hardware resources and software tools.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - M Sedlmayr
- Dr. Martin Sedlmayr, Lehrstuhl für Medizinische Informatik, Friedrich-Alexander-Universität Erlangen-Nürnberg, Wetterkreuz 13, 91058 Erlangen, Germany, E-mail:
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Bache R, Taweel A, Miles S, Delaney BC. An eligibility criteria query language for heterogeneous data warehouses. Methods Inf Med 2014; 54:41-4. [PMID: 24985949 DOI: 10.3414/me13-02-0027] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2013] [Accepted: 05/07/2014] [Indexed: 11/09/2022]
Abstract
INTRODUCTION This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems". OBJECTIVES The increasing availability of electronic clinical data provides great potential for finding eligible patients for clinical research. However, data heterogeneity makes it difficult for clinical researchers to interrogate sources consistently. Existing standard query languages are often not sufficient to query across diverse representations. Thus, a higher-level domain language is needed so that queries become data-representation agnostic. To this end, we define a clinician-readable computational language for querying whether patients meet eligibility criteria (ECs) from clinical trials. This language is capable of implementing the temporal semantics required by many ECs, and can be automatically evaluated on heterogeneous data sources. METHODS By reference to standards and examples of existing ECs, a clinician-readable query language was developed. Using a model-based approach, it was implemented to transform captured ECs into queries that interrogate heterogeneous data warehouses. The query language was evaluated on two types of data sources, each different in structure and content. RESULTS The query language abstracts the level of expressivity so that researchers construct their ECs with no prior knowledge of the data sources. It was evaluated on two types of semantically and structurally diverse data warehouses. This query language is now used to express ECs in the EHR4CR project. A survey shows that it was perceived by the majority of users to be useful, easy to understand and unambiguous. DISCUSSION An EC-specific language enables clinical researchers to express their ECs as a query such that the user is isolated from complexities of different heterogeneous clinical data sets. More generally, the approach demonstrates that a domain query language has potential for overcoming the problems of semantic interoperability and is applicable where the nature of the queries is well understood and the data is conceptually similar but in different representations. CONCLUSIONS Our language provides a strong basis for use across different clinical domains for expressing ECs by overcoming the heterogeneous nature of electronic clinical data whilst maintaining semantic consistency. It is readily comprehensible by target users. This demonstrates that a domain query language can be both usable and interoperable.
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Affiliation(s)
- R Bache
- R. Bache, Dept. of Primary Care and Public Health Sciences, Floor 7, Capital House, Weston Street, London SE1 3QD, UK, E-mail:
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Lim Choi Keung SN, Zhao L, Rossiter J, McGilchrist M, Culross F, Ethier JF, Burgun A, Verheij RA, Khan N, Taweel A, Curcin V, Delaney BC, Arvanitis TN. Detailed clinical modelling approach to data extraction from heterogeneous data sources for clinical research. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2014; 2014:55-9. [PMID: 25954578 PMCID: PMC4419774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
The reuse of routinely collected clinical data for clinical research is being explored as part of the drive to reduce duplicate data entry and to start making full use of the big data potential in the healthcare domain. Clinical researchers often need to extract data from patient registries and other patient record datasets for data analysis as part of clinical studies. In the TRANSFoRm project, researchers define their study requirements via a Query Formulation Workbench. We use a standardised approach to data extraction to retrieve relevant information from heterogeneous data sources, using semantic interoperability enabled via detailed clinical modelling. This approach is used for data extraction from data sources for analysis and for pre-population of electronic Case Report Forms from electronic health records in primary care clinical systems.
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Affiliation(s)
| | - Lei Zhao
- Institute of Digital Healthcare, WMG, University of Warwick, UK
| | - James Rossiter
- Institute of Digital Healthcare, WMG, University of Warwick, UK
| | | | | | | | | | - Robert A. Verheij
- NIVEL Netherlands Institute for Health Services Research, The Netherlands
| | - Nasra Khan
- NIVEL Netherlands Institute for Health Services Research, The Netherlands
| | - Adel Taweel
- Department of Primary Care and Health Sciences, King’s College London, UK
| | - Vasa Curcin
- Department of Computing, Imperial College London, UK
| | - Brendan C. Delaney
- Department of Primary Care and Health Sciences, King’s College London, UK
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Dameron O, Besana P, Zekri O, Bourdé A, Burgun A, Cuggia M. OWL model of clinical trial eligibility criteria compatible with partially-known information. J Biomed Semantics 2013; 4:17. [PMID: 24034867 PMCID: PMC3852288 DOI: 10.1186/2041-1480-4-17] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2013] [Accepted: 08/21/2013] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Clinical trials are important for patients, for researchers and for companies. One of the major bottlenecks is patient recruitment. This task requires the matching of a large volume of information about the patient with numerous eligibility criteria, in a logically-complex combination. Moreover, some of the patient's information necessary to determine the status of the eligibility criteria may not be available at the time of pre-screening. RESULTS We showed that the classic approach based on negation as failure over-estimates rejection when confronted with partially-known information about the eligibility criteria because it ignores the distinction between a trial for which patient eligibility should be rejected and trials for which patient eligibility cannot be asserted. We have also shown that 58.64% of the values were unknown in the 286 prostate cancer cases examined during the weekly urology multidisciplinary meetings at Rennes' university hospital between October 2008 and March 2009.We propose an OWL design pattern for modeling eligibility criteria based on the open world assumption to address the missing information problem. We validate our model on a fictitious clinical trial and evaluate it on two real clinical trials. Our approach successfully distinguished clinical trials for which the patient is eligible, clinical trials for which we know that the patient is not eligible and clinical trials for which the patient may be eligible provided that further pieces of information (which we can identify) can be obtained. CONCLUSIONS OWL-based reasoning based on the open world assumption provides an adequate framework for distinguishing those patients who can confidently be rejected from those whose status cannot be determined. The expected benefits are a reduction of the workload of the physicians and a higher efficiency by allowing them to focus on the patients whose eligibility actually require expertise.
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Affiliation(s)
- Olivier Dameron
- , Université de Rennes1, UMR936, F-35000 Rennes, France
- , INSERM UMR936, F-35000 Rennes, France
| | - Paolo Besana
- , Université de Rennes1, UMR936, F-35000 Rennes, France
- , INSERM UMR936, F-35000 Rennes, France
| | - Oussama Zekri
- , Centre Régional de Lutte Contre le Cancer Eugène Marquis, F-35000 Rennes, France
| | - Annabel Bourdé
- , Université de Rennes1, UMR936, F-35000 Rennes, France
- , INSERM UMR936, F-35000 Rennes, France
| | - Anita Burgun
- , Université de Rennes1, UMR936, F-35000 Rennes, France
- , INSERM UMR936, F-35000 Rennes, France
| | - Marc Cuggia
- , Université de Rennes1, UMR936, F-35000 Rennes, France
- , INSERM UMR936, F-35000 Rennes, France
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