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Wu ZM, Kan J, Ye F, You W, Wu XQ, Tian NL, Lin S, Ge Z, Liu ZZ, Li XB, Gao XF, Chen J, Wang Y, Wen SY, Xie P, Cong HL, Liu LJ, Zeng HS, Zhou L, Liu F, Zheng YH, Li R, Ji HL, Zhou SH, Zhao SM, Qian XS, Luo J, Wang X, Zhang JJ, Chen SL. PCSK9 inhibitor added to high-intensity statin therapy to prevent cardiovascular events in patients with acute coronary syndrome after percutaneous coronary intervention: a randomized, double- blind, placebo-controlled, multicenter SHAWN study. Am Heart J 2024; 277:58-65. [PMID: 38942221 DOI: 10.1016/j.ahj.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 06/10/2024] [Accepted: 06/11/2024] [Indexed: 06/30/2024]
Abstract
BACKGROUND It is currently uncertain whether the combination of a proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor and high-intensity statin treatment can effectively reduce cardiovascular events in patients with acute coronary syndrome (ACS) who have undergone percutaneous coronary intervention (PCI) for culprit lesions. METHODS This study protocol describes a double-blind, randomized, placebo-controlled, multicenter study aiming to investigate the efficacy and safety of combining a PCSK9 inhibitor with high-intensity statin therapy in patients with ACS following PCI. A total of 1,212 patients with ACS and multiple lesions will be enrolled and randomly assigned to receive either PCSK9 inhibitor plus high-intensity statin therapy or high-intensity statin monotherapy. The randomization process will be stratified by sites, diabetes, initial presentation and use of stable (≥4 weeks) statin treatment at presentation. PCSK 9 inhibitor or its placebo is injected within 4 hours after PCI for the culprit lesion. The primary endpoint is the composite of cardiovascular death, myocardial infarction, stroke, re-hospitalization due to ACS or heart failure, or any ischemia-driven coronary revascularization at 1-year follow-up between 2 groups. Safety endpoints mean PCSK 9 inhibitor and statin intolerance. CONCLUSION The SHAWN study has been specifically designed to evaluate the effectiveness and safety of adding a PCSK9 inhibitor to high-intensity statin therapy in patients who have experienced ACS following PCI. The primary objective of this study is to generate new evidence regarding the potential benefits of combining a PCSK9 inhibitor with high-intensity statin treatment in reducing cardiovascular events among these patients.
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Affiliation(s)
- Zhi-Ming Wu
- Division of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Jing Kan
- Division of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Fei Ye
- Division of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Wei You
- Division of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xiang-Qi Wu
- Division of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Nai-Liang Tian
- Division of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Song Lin
- Division of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Zhen Ge
- Division of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Zhi-Zhong Liu
- Division of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xiao-Bo Li
- Division of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xiao-Fei Gao
- Division of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Jing Chen
- Division of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yan Wang
- Division of Cardiology, Xiamen Cardiovascular Hospital, Xiamen University, Xiamen, China
| | - Shang-Yu Wen
- Division of Cardiology, Tianjin 4th Central Hospital, Tianjin, China
| | - Ping Xie
- Division of Cardiology, Gansu Province People's Hospital, Lanzhou, China
| | - Hong-Liang Cong
- Division of Cardiology, Tianjin Chest Hospital, Tianjin, China
| | - Li-Jun Liu
- Division of Cardiology, The First Affiliated Hospital of Anhui University of Science and Technology, Huainan, China
| | - He-Song Zeng
- Division of Cardiology, Huazhong University of Science and Technology Tongji Medical College Tongji Hospital, Wuhan, China
| | - Lei Zhou
- Division of Cardiology, Changzhou Jintan First People's Hospital, Changzhou,China
| | - Fan Liu
- Division of Cardiology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Yong-Hong Zheng
- Division of Cardiology, Liyang Hospital of Chinese Medicine, Liyang, China
| | - Rui Li
- Division of Cardiology, First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Hong-Lei Ji
- Division of Cardiology, The First Hospital of Jilin University, Jilin, China
| | - Sheng-Hua Zhou
- Division of Cardiology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Shou-Ming Zhao
- Division of Cardiology, First Peoples of Hospital of Taicang, Suzhou, China
| | - Xue-Song Qian
- Division of Cardiology, Zhangjiagang First People's Hospital, Zhangjiagang, China
| | - Jun Luo
- Division of Cardiology, The People's Hospital of Ganzhou, Ganzhou, China
| | - Xin Wang
- Division of Cardiology, Lianyungang Hospital of Chinese Medicine, Lianyungang, China
| | - Jun-Jie Zhang
- Division of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Shao-Liang Chen
- Division of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
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Fouad K, Vavrek R, Surles-Zeigler MC, Huie JR, Radabaugh HL, Gurkoff GG, Visser U, Grethe JS, Martone ME, Ferguson AR, Gensel JC, Torres-Espin A. A practical guide to data management and sharing for biomedical laboratory researchers. Exp Neurol 2024; 378:114815. [PMID: 38762093 DOI: 10.1016/j.expneurol.2024.114815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 05/13/2024] [Accepted: 05/14/2024] [Indexed: 05/20/2024]
Abstract
Effective data management and sharing have become increasingly crucial in biomedical research; however, many laboratory researchers lack the necessary tools and knowledge to address this challenge. This article provides an introductory guide into research data management (RDM), and the importance of FAIR (Findable, Accessible, Interoperable, and Reusable) data-sharing principles for laboratory researchers produced by practicing scientists. We explore the advantages of implementing organized data management strategies and introduce key concepts such as data standards, data documentation, and the distinction between machine and human-readable data formats. Furthermore, we offer practical guidance for creating a data management plan and establishing efficient data workflows within the laboratory setting, suitable for labs of all sizes. This includes an examination of requirements analysis, the development of a data dictionary for routine data elements, the implementation of unique subject identifiers, and the formulation of standard operating procedures (SOPs) for seamless data flow. To aid researchers in implementing these practices, we present a simple organizational system as an illustrative example, which can be tailored to suit individual needs and research requirements. By presenting a user-friendly approach, this guide serves as an introduction to the field of RDM and offers practical tips to help researchers effortlessly meet the common data management and sharing mandates rapidly becoming prevalent in biomedical research.
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Affiliation(s)
- K Fouad
- Department of Physical Therapy, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada.
| | - R Vavrek
- Department of Physical Therapy, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada
| | - M C Surles-Zeigler
- Department of Neuroscience, University of California, San Diego, La Jolla, CA, United States
| | - J R Huie
- Department of Neurosurgery, Brain and Spinal Injury Center, Weill Institutes for Neurosciences, University of California, San Francisco, San Francisco, CA, United States; San Francisco Veterans Affairs Healthcare System, San Francisco, CA, United States
| | - H L Radabaugh
- Department of Neurosurgery, Brain and Spinal Injury Center, Weill Institutes for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - G G Gurkoff
- Center for Neuroscience, University of California Davis, Davis, CA, United States; Department of Neurological Surgery, University of California Davis, Davis, CA, United States; Northern California Veterans Affairs Healthcare System, Martinez, CA, United States
| | - U Visser
- Department of Computer Science, University of Miami, Coral Gables, FL, United States
| | - J S Grethe
- Department of Neuroscience, University of California, San Diego, La Jolla, CA, United States
| | - M E Martone
- Department of Neuroscience, University of California, San Diego, La Jolla, CA, United States; San Francisco Veterans Affairs Healthcare System, San Francisco, CA, United States
| | - A R Ferguson
- Department of Neurosurgery, Brain and Spinal Injury Center, Weill Institutes for Neurosciences, University of California, San Francisco, San Francisco, CA, United States; San Francisco Veterans Affairs Healthcare System, San Francisco, CA, United States
| | - J C Gensel
- Spinal Cord and Brain Injury Research Center and Department of Physiology, University of Kentucky College of Medicine, Lexington, KY, United States.
| | - A Torres-Espin
- Department of Physical Therapy, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada; Department of Neurosurgery, Brain and Spinal Injury Center, Weill Institutes for Neurosciences, University of California, San Francisco, San Francisco, CA, United States; School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.
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Urwin E, Martin J, Sebire N, Harris A, Johnson J, Masood E, Milligan G, Mairs L, Chuter A, Ferguson M, Quinlan P, Jefferson E. A SARS-CoV-2 minimum data standard to support national serology reporting. Ann Clin Biochem 2024:45632241261274. [PMID: 38806176 DOI: 10.1177/00045632241261274] [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: 05/30/2024]
Abstract
BACKGROUND Healthcare laboratory systems produce and capture a vast array of information, yet do not always report all of this to the national infrastructure within the United Kingdom. The global COVID-19 pandemic brought about a much greater need for detailed healthcare data, one such instance being laboratory testing data. The reporting of qualitative laboratory test results (e.g. positive, negative or indeterminate) provides a basic understanding of levels of seropositivity. However, to better understand and interpret seropositivity, how it is determined and other factors that affect its calculation (i.e. levels of antibodies), quantitative laboratory test data are needed. METHOD 36 data attributes were collected from 3 NHS laboratories and 29 CO-CONNECT project partner organisations. These were assessed against the need for a minimum dataset to determine data attribute importance. An NHS laboratory feasibility study was undertaken to assess the minimum data standard, together with a literature review of national and international data standards and healthcare reports. RESULTS A COVID serology minimum data standard (CSMDS) comprising 12 data attributes was created and verified by 3 NHS laboratories to allow national granular reporting of COVID serology results. To support this, a standardised set of vocabulary terms was developed to represent laboratory analyser systems and laboratory information management systems. CONCLUSIONS This paper puts forward a minimum viable standard for COVID-19 serology data attributes to enhance its granularity and augment the national reporting of COVID-19 serology laboratory results, with implications for future pandemics.
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Affiliation(s)
- Esmond Urwin
- Digital Research Service, University of Nottingham, Nottingham, UK
| | - Joanne Martin
- Centre for Genomics and Child Health, Queen Mary University of London, London, UK
| | - Neil Sebire
- Institute of Child Health Population Policy and Practice, UCL Great Ormond Street Institute of Child Health, London, UK
| | | | - Jenny Johnson
- School of Medicine, University of Dundee, Dundee, UK
| | - Erum Masood
- School of Medicine, University of Dundee, Dundee, UK
| | | | | | - Antony Chuter
- Public and Patient Involvement Group, University of Nottingham, Nottingham, UK
| | | | - Philip Quinlan
- School of Medicine, University of Nottingham, Nottingham, UK
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Dugas M, Blumenstock M, Dittrich T, Eisenmann U, Feder SC, Fritz-Kebede F, Kessler LJ, Klass M, Knaup P, Lehmann CU, Merzweiler A, Niklas C, Pausch TM, Zental N, Ganzinger M. Next-generation study databases require FAIR, EHR-integrated, and scalable Electronic Data Capture for medical documentation and decision support. NPJ Digit Med 2024; 7:10. [PMID: 38216645 PMCID: PMC10786912 DOI: 10.1038/s41746-023-00994-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 12/11/2023] [Indexed: 01/14/2024] Open
Abstract
Structured patient data play a key role in all types of clinical research. They are often collected in study databases for research purposes. In order to describe characteristics of a next-generation study database and assess the feasibility of its implementation a proof-of-concept study in a German university hospital was performed. Key characteristics identified include FAIR access to electronic case report forms (eCRF), regulatory compliant Electronic Data Capture (EDC), an EDC with electronic health record (EHR) integration, scalable EDC for medical documentation, patient generated data, and clinical decision support. In a local case study, we then successfully implemented a next-generation study database for 19 EDC systems (n = 2217 patients) that linked to i.s.h.med (Oracle Cerner) with the local EDC system called OpenEDC. Desiderata of next-generation study databases for patient data were identified from ongoing local clinical study projects in 11 clinical departments at Heidelberg University Hospital, Germany, a major tertiary referral hospital. We compiled and analyzed feature and functionality requests submitted to the OpenEDC team between May 2021 and July 2023. Next-generation study databases are technically and clinically feasible. Further research is needed to evaluate if our approach is feasible in a multi-center setting as well.
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Affiliation(s)
- Martin Dugas
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Max Blumenstock
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Tobias Dittrich
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
- Department of Hematology, Oncology and Rheumatology, Heidelberg University Hospital, Heidelberg, Germany
| | - Urs Eisenmann
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Stephan Christoph Feder
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
- Department of General Internal Medicine and Psychosomatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Fleur Fritz-Kebede
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Lucy J Kessler
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
- Department of Ophthalmology, Heidelberg University Hospital, Heidelberg, Germany
| | - Maximilian Klass
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Petra Knaup
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Christoph U Lehmann
- Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Angela Merzweiler
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Christian Niklas
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Thomas M Pausch
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
- Department of General, Visceral, and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Nelly Zental
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
- Department of Anesthesiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Matthias Ganzinger
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany.
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Federated electronic data capture (fEDC): Architecture and prototype. J Biomed Inform 2023; 138:104280. [PMID: 36623781 DOI: 10.1016/j.jbi.2023.104280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/23/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023]
Abstract
In clinical research as well as patient care, structured documentation of findings is an important task. In many cases, this is achieved by means of electronic case report forms (eCRF) using corresponding information technology systems. To avoid double data entry, eCRF systems can be integrated with electronic health records (EHR). However, when researchers from different institutions collaborate in collecting data, they often use a single joint eCRF system on the Internet. In this case, integration with EHR systems is not possible in most cases due to information security and data protection restrictions. To overcome this shortcoming, we propose a novel architecture for a federated electronic data capture system (fEDC). Four key requirements were identified for fEDC: Definitions of forms have to be available in a reliable and controlled fashion, integration with electronic health record systems must be possible, patient data should be under full local control until they are explicitly transferred for joint analysis, and the system must support data sharing principles accepted by the scientific community for both data model and data captured. With our approach, sites participating in a joint study can run their own instance of an fEDC system that complies with local standards (such as being behind a network firewall) while also being able to benefit from using identical form definitions by sharing metadata in the Operational Data Model (ODM) format published by the Clinical Data Interchange Standards Consortium (CDISC) throughout the collaboration. The fEDC architecture was validated with a working open-source prototype at five German university hospitals. The fEDC architecture provides a novel approach with the potential to significantly improve collaborative data capture: Efforts for data entry are reduced and at the same time, data quality is increased since barriers for integrating with local electronic health record systems are lowered. Further, metadata are shared and patient privacy is ensured at a high level.
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Barros JM, Widmer LA, Baillie M, Wandel S. Rethinking clinical study data: why we should respect analysis results as data. Sci Data 2022; 9:686. [PMID: 36357430 PMCID: PMC9649650 DOI: 10.1038/s41597-022-01789-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 10/18/2022] [Indexed: 11/12/2022] Open
Abstract
The development and approval of new treatments generates large volumes of results, such as summaries of efficacy and safety. However, it is commonly overlooked that analyzing clinical study data also produces data in the form of results. For example, descriptive statistics and model predictions are data. Although integrating and putting findings into context is a cornerstone of scientific work, analysis results are often neglected as a data source. Results end up stored as "data products" such as PDF documents that are not machine readable or amenable to future analyses. We propose a solution to "calculate once, use many times" by combining analysis results standards with a common data model. This analysis results data model re-frames the target of analyses from static representations of the results (e.g., tables and figures) to a data model with applications in various contexts, including knowledge discovery. Further, we provide a working proof of concept detailing how to approach standardization and construct a schema to store and query analysis results.
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Affiliation(s)
- Joana M Barros
- Analytics, Novartis Pharma AG, Basel, Switzerland.
- Department of Biometry, Idorsia Pharmaceuticals, Allschwil, Switzerland.
| | | | - Mark Baillie
- Analytics, Novartis Pharma AG, Basel, Switzerland.
| | - Simon Wandel
- Analytics, Novartis Pharma AG, Basel, Switzerland
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Urbanowicz RJ, Holmes JH, Appleby D, Narasimhan V, Durborow S, Al-Naamani N, Fernando M, Kawut SM. A Semi-Automated Term Harmonization Pipeline Applied to Pulmonary Arterial Hypertension Clinical Trials. Methods Inf Med 2022; 61:3-10. [PMID: 34820791 PMCID: PMC9978994 DOI: 10.1055/s-0041-1739361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
OBJECTIVE Data harmonization is essential to integrate individual participant data from multiple sites, time periods, and trials for meta-analysis. The process of mapping terms and phrases to an ontology is complicated by typographic errors, abbreviations, truncation, and plurality. We sought to harmonize medical history (MH) and adverse events (AE) term records across 21 randomized clinical trials in pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension. METHODS We developed and applied a semi-automated harmonization pipeline for use with domain-expert annotators to resolve ambiguous term mappings using exact and fuzzy matching. We summarized MH and AE term mapping success, including map quality measures, and imputation of a generalizing term hierarchy as defined by the applied Medical Dictionary for Regulatory Activities (MedDRA) ontology standard. RESULTS Over 99.6% of both MH (N = 37,105) and AE (N = 58,170) records were successfully mapped to MedDRA low-level terms. Automated exact matching accounted for 74.9% of MH and 85.5% of AE mappings. Term recommendations from fuzzy matching in the pipeline facilitated annotator mapping of the remaining 24.9% of MH and 13.8% of AE records. Imputation of the generalized MedDRA term hierarchy was unambiguous in 85.2% of high-level terms, 99.4% of high-level group terms, and 99.5% of system organ class in MH, and 75% of high-level terms, 98.3% of high-level group terms, and 98.4% of system organ class in AE. CONCLUSION This pipeline dramatically reduced the burden of manual annotation for MH and AE term harmonization and could be adapted to other data integration efforts.
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Affiliation(s)
- Ryan J. Urbanowicz
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - John H. Holmes
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Dina Appleby
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Vanamala Narasimhan
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
| | - Stephen Durborow
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Nadine Al-Naamani
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
| | - Melissa Fernando
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Steven M. Kawut
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
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MedicalForms: Integrated Management of Semantics for Electronic Health Record Systems and Research Platforms. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12094322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
(1) Background: Clinical information modeling tools are software instruments designed to support the definition of semantic structures able to be implemented in health information systems. Based on the analysis of existing tools, this research developed a tool that proposes new approaches to promoting clinician involvement and supporting information modeling processes through mechanisms that ensure governance, information consistency and consensus building. (2) Method: This research developed the MedicalForms system, which is based on the requirements identified in both a Delphi study about tool requirements and the ISO/TS 13972 specifications. (3) Results: This system allows the management of projects, information structures and implementable forms related to clinical documentation. Users can easily define clinical documents in collaboration with the rest of the professionals in their team by being able to reuse previously defined forms, terminologies and information structures. The system is able to export the defined forms as interoperable specifications or as several implementable form formats compatible with multiple open source EHR systems and research platforms. End user perception of this tool was evaluated through the Technology Acceptance Questionnaire with satisfactory results. Finally, the system was applied to develop 12 research registries and 2 clinical trial research forms, 3 mobile applications and 1 decision support system.
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Dilts NA, Harrell FE, Lindsell CJ, Nwosu S, Stewart TG, Shotwell MS, Pulley JM, Edwards TL, Serdoz ES, Benhoff K, Bernard GR. Securely sharing DSMB reports to speed decision making from multiple, concurrent, independent studies of similar treatments in COVID-19. J Clin Transl Sci 2022; 6:e49. [PMID: 35656334 PMCID: PMC9120618 DOI: 10.1017/cts.2022.387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 01/22/2022] [Accepted: 04/05/2022] [Indexed: 11/11/2022] Open
Abstract
Introduction As clinical trials were rapidly initiated in response to the COVID-19 pandemic, Data and Safety Monitoring Boards (DSMBs) faced unique challenges overseeing trials of therapies never tested in a disease not yet characterized. Traditionally, individual DSMBs do not interact or have the benefit of seeing data from other accruing trials for an aggregated analysis to meaningfully interpret safety signals of similar therapeutics. In response, we developed a compliant DSMB Coordination (DSMBc) framework to allow the DSMB from one study investigating the use of SARS-CoV-2 convalescent plasma to treat COVID-19 to review data from similar ongoing studies for the purpose of safety monitoring. Methods The DSMBc process included engagement of DSMB chairs and board members, execution of contractual agreements, secure data acquisition, generation of harmonized reports utilizing statistical graphics, and secure report sharing with DSMB members. Detailed process maps, a secure portal for managing DSMB reports, and templates for data sharing and confidentiality agreements were developed. Results Four trials participated. Data from one trial were successfully harmonized with that of an ongoing trial. Harmonized reports allowing for visualization and drill down into the data were presented to the ongoing trial's DSMB. While DSMB deliberations are confidential, the Chair confirmed successful review of the harmonized report. Conclusion It is feasible to coordinate DSMB reviews of multiple independent studies of a similar therapeutic in similar patient cohorts. The materials presented mitigate challenges to DSMBc and will help expand these initiatives so DSMBs may make more informed decisions with all available information.
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Affiliation(s)
- Natalie A. Dilts
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Frank E. Harrell
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
- Executive Committee for the Coordinated Approach for Emergency Studies
| | - Christopher J. Lindsell
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
- Executive Committee for the Coordinated Approach for Emergency Studies
| | - Samuel Nwosu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Thomas G. Stewart
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Matthew S. Shotwell
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jill M. Pulley
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Terri L. Edwards
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Emily Sheffer Serdoz
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Katelyn Benhoff
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Gordon R. Bernard
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
- Executive Committee for the Coordinated Approach for Emergency Studies
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E-santé, digitalisation ou transformation numérique : impact sur les soins de support en oncologie. Bull Cancer 2022; 109:598-611. [DOI: 10.1016/j.bulcan.2021.08.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 08/10/2021] [Accepted: 08/29/2021] [Indexed: 11/19/2022]
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Asiimwe R, Lam S, Leung S, Wang S, Wan R, Tinker A, McAlpine JN, Woo MMM, Huntsman DG, Talhouk A. From biobank and data silos into a data commons: convergence to support translational medicine. J Transl Med 2021; 19:493. [PMID: 34863191 PMCID: PMC8645144 DOI: 10.1186/s12967-021-03147-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 11/16/2021] [Indexed: 11/30/2022] Open
Abstract
Background To drive translational medicine, modern day biobanks need to integrate with other sources of data (clinical, genomics) to support novel data-intensive research. Currently, vast amounts of research and clinical data remain in silos, held and managed by individual researchers, operating under different standards and governance structures; a framework that impedes sharing and effective use of data. In this article, we describe the journey of British Columbia’s Gynecological Cancer Research Program (OVCARE) in moving a traditional tumour biobank, outcomes unit, and a collection of data silos, into an integrated data commons to support data standardization and resource sharing under collaborative governance, as a means of providing the gynecologic cancer research community in British Columbia access to tissue samples and associated clinical and molecular data from thousands of patients. Results Through several engagements with stakeholders from various research institutions within our research community, we identified priorities and assessed infrastructure needs required to optimize and support data collections, storage and sharing, under three main research domains: (1) biospecimen collections, (2) molecular and genomics data, and (3) clinical data. We further built a governance model and a resource portal to implement protocols and standard operating procedures for seamless collections, management and governance of interoperable data, making genomic, and clinical data available to the broader research community. Conclusions Proper infrastructures for data collection, sharing and governance is a translational research imperative. We have consolidated our data holdings into a data commons, along with standardized operating procedures to meet research and ethics requirements of the gynecologic cancer community in British Columbia. The developed infrastructure brings together, diverse data, computing frameworks, as well as tools and applications for managing, analyzing, and sharing data. Our data commons bridges data access gaps and barriers to precision medicine and approaches for diagnostics, treatment and prevention of gynecological cancers, by providing access to large datasets required for data-intensive science. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-021-03147-z.
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Affiliation(s)
- Rebecca Asiimwe
- Department of Molecular Oncology, BC Cancer Research Centre, 675 West 10th Avenue, Vancouver, BC, V5Z 1L3, Canada.,BC Children's Hospital Research Institute, 938 West 28th Avenue, Vancouver, BC, V5Z 4H4, Canada
| | - Stephanie Lam
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of British Columbia, Vancouver, Canada.,OVCARE Research Program, Vancouver, Canada
| | - Samuel Leung
- Department of Molecular Oncology, BC Cancer Research Centre, 675 West 10th Avenue, Vancouver, BC, V5Z 1L3, Canada.,Department of Pathology and Laboratory Medicine, Faculty of Medicine, University of British Columbia, 2211 Wesbrook Mall, Vancouver, BC, V6T 2B5, Canada.,OVCARE Research Program, Vancouver, Canada
| | - Shanzhao Wang
- Department of Pathology and Laboratory Medicine, Faculty of Medicine, University of British Columbia, 2211 Wesbrook Mall, Vancouver, BC, V6T 2B5, Canada.,OVCARE Research Program, Vancouver, Canada
| | - Rachel Wan
- Department of Pathology and Laboratory Medicine, Faculty of Medicine, University of British Columbia, 2211 Wesbrook Mall, Vancouver, BC, V6T 2B5, Canada.,BC Cancer, 600 West 10th Avenue, Vancouver CentreVancouver, BC, V5Z 4E6, Canada
| | - Anna Tinker
- Department of Medicine, Faculty of Medicine, Division of Medical Oncology, University of British Columbia, 2775 Laurel Street, Vancouver, BC, V5Z 1M9, Canada.,BC Cancer, 600 West 10th Avenue, Vancouver CentreVancouver, BC, V5Z 4E6, Canada.,OVCARE Research Program, Vancouver, Canada
| | - Jessica N McAlpine
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of British Columbia, Vancouver, Canada.,OVCARE Research Program, Vancouver, Canada
| | - Michelle M M Woo
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of British Columbia, Vancouver, Canada.,OVCARE Research Program, Vancouver, Canada
| | - David G Huntsman
- Department of Molecular Oncology, BC Cancer Research Centre, 675 West 10th Avenue, Vancouver, BC, V5Z 1L3, Canada.,Department of Pathology and Laboratory Medicine, Faculty of Medicine, University of British Columbia, 2211 Wesbrook Mall, Vancouver, BC, V6T 2B5, Canada.,Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of British Columbia, Vancouver, Canada.,OVCARE Research Program, Vancouver, Canada
| | - Aline Talhouk
- OVCARE Research Program, Vancouver, Canada. .,Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of British Columbia, 5th Floor (593), 828 West 10th Ave, Vancouver, BC, V5Z 1M9, Canada.
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12
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Hegselmann S, Storck M, Gessner S, Neuhaus P, Varghese J, Bruland P, Meidt A, Mertens C, Riepenhausen S, Baier S, Stöcker B, Henke J, Schmidt CO, Dugas M. Pragmatic MDR: a metadata repository with bottom-up standardization of medical metadata through reuse. BMC Med Inform Decis Mak 2021; 21:160. [PMID: 34001121 PMCID: PMC8130274 DOI: 10.1186/s12911-021-01524-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 05/09/2021] [Indexed: 11/27/2022] Open
Abstract
Background The variety of medical documentation often leads to incompatible data elements that impede data integration between institutions. A common approach to standardize and distribute metadata definitions are ISO/IEC 11179 norm-compliant metadata repositories with top-down standardization. To the best of our knowledge, however, it is not yet common practice to reuse the content of publicly accessible metadata repositories for creation of case report forms or routine documentation. We suggest an alternative concept called pragmatic metadata repository, which enables a community-driven bottom-up approach for agreeing on data collection models. A pragmatic metadata repository collects real-world documentation and considers frequent metadata definitions as high quality with potential for reuse. Methods We implemented a pragmatic metadata repository proof of concept application and filled it with medical forms from the Portal of Medical Data Models. We applied this prototype in two use cases to demonstrate its capabilities for reusing metadata: first, integration into a study editor for the suggestion of data elements and, second, metadata synchronization between two institutions. Moreover, we evaluated the emergence of bottom-up standards in the prototype and two medical data managers assessed their quality for 24 medical concepts. Results The resulting prototype contained 466,569 unique metadata definitions. Integration into the study editor led to a reuse of 1836 items and item groups. During the metadata synchronization, semantic codes of 4608 data elements were transferred. Our evaluation revealed that for less complex medical concepts weak bottom-up standards could be established. However, more diverse disease-related concepts showed no convergence of data elements due to an enormous heterogeneity of metadata. The survey showed fair agreement (Kalpha = 0.50, 95% CI 0.43–0.56) for good item quality of bottom-up standards. Conclusions We demonstrated the feasibility of the pragmatic metadata repository concept for medical documentation. Applications of the prototype in two use cases suggest that it facilitates the reuse of data elements. Our evaluation showed that bottom-up standardization based on a large collection of real-world metadata can yield useful results. The proposed concept shall not replace existing top-down approaches, rather it complements them by showing what is commonly used in the community to guide other researchers. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-021-01524-8.
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Affiliation(s)
- Stefan Hegselmann
- Institute of Medical Informatics, University of Münster, Münster, Germany.
| | - Michael Storck
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Sophia Gessner
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Philipp Neuhaus
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Julian Varghese
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Philipp Bruland
- University of Applied Sciences Ostwestfalen-Lippe, Lemgo, Germany
| | - Alexandra Meidt
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Cornelia Mertens
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Sarah Riepenhausen
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Sonja Baier
- Centre for Clinical Trials, University of Münster, Münster, Germany
| | - Benedikt Stöcker
- Centre for Clinical Trials, University of Münster, Münster, Germany
| | - Jörg Henke
- Institute of Community Medicine, University Medicine of Greifswald, Greifswald, Germany
| | | | - Martin Dugas
- Institute of Medical Informatics, University of Münster, Münster, Germany
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13
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Garza MY, Rutherford M, Myneni S, Fenton S, Walden A, Topaloglu U, Eisenstein E, Kumar KR, Zimmerman KO, Rocca M, Gordon GS, Hume S, Wang Z, Zozus M. Evaluating the Coverage of the HL7 ® FHIR ® Standard to Support eSource Data Exchange Implementations for use in Multi-Site Clinical Research Studies. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2021; 2020:472-481. [PMID: 33936420 PMCID: PMC8075534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The direct use of EHR data in research, often referred to as 'eSource', has long-been a goal for researchers because of anticipated increases in data quality and reductions in site burden. eSource solutions should rely on data exchange standards for consistency, quality, and efficiency. The utility of any data standard can be evaluated by its ability to meet specific use case requirements. The Health Level Seven (HL7 ® ) Fast Healthcare Interoperability Resources (FHIR ® ) standard is widely recognized for clinical data exchange; however, a thorough analysis of the standard's data coverage in supporting multi-site clinical studies has not been conducted. We developed and implemented a systematic mapping approach for evaluating HL7 ® FHIR ® standard coverage in multi-center clinical trials. Study data elements from three diverse studies were mapped to HL7 ® FHIR ® resources, offering insight into the coverage and utility of the standard for supporting the data collection needs of multi-site clinical research studies.
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Affiliation(s)
- Maryam Y Garza
- University of Arkansas for Medical Sciences, Little Rock, AR
- University of Texas Health Science Center at Houston, Houston, TX
| | | | - Sahiti Myneni
- University of Texas Health Science Center at Houston, Houston, TX
| | - Susan Fenton
- University of Texas Health Science Center at Houston, Houston, TX
| | - Anita Walden
- Oregon Health and Science University, Portland, OR
| | - Umit Topaloglu
- Wake Forest University School of Medicine, Winston-Salem, NC
| | - Eric Eisenstein
- Duke Clinical Research Institute, Duke University, Durham, NC
| | - Karan R Kumar
- Duke Clinical Research Institute, Duke University, Durham, NC
| | | | - Mitra Rocca
- United States Food & Drug Administration, Silver Springs, MD
| | | | - Sam Hume
- Clinical Data Interchange Standards Consortium, Austin, TX
| | - Zhan Wang
- University of Texas Health Science Center at San Antonio, San Antonio, TX
| | - Meredith Zozus
- University of Texas Health Science Center at San Antonio, San Antonio, TX
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14
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Kumuthini J, van Woerden C, Mallett A, Zass L, Chaouch M, Thompson M, Johnston K, Mbiyavanga M, Baichoo S, Mungloo-Dilmohamud Z, Patel C, Mulder N. Proposed minimum information guideline for kidney disease-research and clinical data reporting: a cross-sectional study. BMJ Open 2019; 9:e029539. [PMID: 31772086 PMCID: PMC6887010 DOI: 10.1136/bmjopen-2019-029539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVE This project aimed to develop and propose a standardised reporting guideline for kidney disease research and clinical data reporting, in order to improve kidney disease data quality and integrity, and combat challenges associated with the management and challenges of 'Big Data'. METHODS A list of recommendations was proposed for the reporting guideline based on the systematic review and consolidation of previously published data collection and reporting standards, including PhenX measures and Minimal Information about a Proteomics Experiment (MIAPE). Thereafter, these recommendations were reviewed by domain-specialists using an online survey, developed in Research Electronic Data Capture (REDCap). Following interpretation and consolidation of the survey results, the recommendations were mapped to existing ontologies using Zooma, Ontology Lookup Service and the Bioportal search engine. Additionally, an associated eXtensible Markup Language schema was created for the REDCap implementation to increase user friendliness and adoption. RESULTS The online survey was completed by 53 respondents; the majority of respondents were dual clinician-researchers (57%), based in Australia (35%), Africa (33%) and North America (22%). Data elements within the reporting standard were identified as participant-level, study-level and experiment-level information, further subdivided into essential or optional information. CONCLUSION The reporting guideline is readily employable for kidney disease research projects, and also adaptable for clinical utility. The adoption of the reporting guideline in kidney disease research can increase data quality and the value for long-term preservation, ensuring researchers gain the maximum benefit from their collected and generated data.
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Affiliation(s)
- Judit Kumuthini
- H3ABioNet, Centre for Proteomic & Genomic Research, Cape Town, South Africa
| | - Christiaan van Woerden
- Department of Surgery, Division of Child Urology, University of Cape Town, Cape Town, South Africa
- Global Child Health Group, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Andrew Mallett
- KidGen Collaborative and AGHA Renal Genetics Flagships, Parkville, Victoria, Australia
- Institute of Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia
| | - Lyndon Zass
- H3ABioNet, Centre for Proteomic & Genomic Research, Cape Town, South Africa
| | - Melek Chaouch
- Department of Parasitology, Institut Pasteur de Tunis, Tunis, Tunisia
| | - Michael Thompson
- National Institute of Mathematical Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Katherine Johnston
- Department of Surgery, Division of Child Urology, University of Cape Town, Cape Town, South Africa
| | - Mamana Mbiyavanga
- Department of Surgery, Division of Child Urology, University of Cape Town, Cape Town, South Africa
| | - Shakuntala Baichoo
- Department of Digital Technologies, University of Mauritius, Reduit, Mauritius
| | | | - Chirag Patel
- KidGen Collaborative and AGHA Renal Genetics Flagships, Parkville, Victoria, Australia
- Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia
| | - Nicola Mulder
- Department of Integrative Biomedical Sciences, Division of Computational Biology, University of Cape Town, Cape Town, South Africa
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15
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Proposed Guideline for Minimum Information Stroke Research and Clinical
Data Reporting. DATA SCIENCE JOURNAL 2019. [DOI: 10.5334/dsj-2019-026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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16
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Grossman RL. Data Lakes, Clouds, and Commons: A Review of Platforms for Analyzing and Sharing Genomic Data. Trends Genet 2019; 35:223-234. [PMID: 30691868 PMCID: PMC6474403 DOI: 10.1016/j.tig.2018.12.006] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 12/20/2018] [Accepted: 12/26/2018] [Indexed: 12/30/2022]
Abstract
Data commons collate data with cloud computing infrastructure and commonly used software services, tools, and applications to create biomedical resources for the large-scale management, analysis, harmonization, and sharing of biomedical data. Over the past few years, data commons have been used to analyze, harmonize, and share large-scale genomics datasets. Data ecosystems can be built by interoperating multiple data commons. It can be quite labor intensive to curate, import, and analyze the data in a data commons. Data lakes provide an alternative to data commons and simply provide access to data, with the data curation and analysis deferred until later and delegated to those that access the data. We review software platforms for managing, analyzing, and sharing genomic data, with an emphasis on data commons, but also cover data ecosystems and data lakes.
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Affiliation(s)
- Robert L Grossman
- Center for Translational Data Science, University of Chicago, 900 East 57th Street, KCBD 10142, Chicago, IL 60637, USA.
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17
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Nourani A, Ayatollahi H, Dodaran MS. A Review of Clinical Data Management Systems Used in Clinical Trials. Rev Recent Clin Trials 2019; 14:10-23. [PMID: 30251611 DOI: 10.2174/1574887113666180924165230] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2018] [Revised: 09/09/2018] [Accepted: 09/18/2018] [Indexed: 04/13/2023]
Abstract
BACKGROUND A clinical data management system is a software supporting the data management process in clinical trials. In this system, the effective support of clinical data management dimensions leads to the increased accuracy of results and prevention of diversion in clinical trials. The aim of this review article was to investigate the dimensions of data management in clinical data management systems. METHODS This study was conducted in 2017. The used databases included Web of Science, Scopus, Science Direct, ProQuest, Ovid Medline and PubMed. The search was conducted over a period of 10 years from 2007 to 2017. The initial number of studies was 101 reaching 19 in the final stage. The final studies were described and compared in terms of the year, country and dimensions of the clinical data management process in clinical trials. RESULTS The research findings indicated that none of the systems completely supported the data management dimensions in clinical trials. Although these systems were developed for supporting the clinical data management process, they were similar to electronic data capture systems in many cases. The most significant dimensions of data management in such systems were data collection or entry, report, validation, and security maintenance. CONCLUSION Seemingly, not sufficient attention has been paid to automate all dimensions of the clinical data management process in clinical trials. However, these systems could take positive steps towards changing the manual processes of clinical data management to electronic processes.
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Affiliation(s)
- Aynaz Nourani
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Haleh Ayatollahi
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
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18
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Yang U, Hsiao T, Lin C, Lee W, Lee Y, Fann YC. Integrative LHS for precision medicine research: A shared NIH and Taiwan CIMS experience. Learn Health Syst 2019; 3:e10071. [PMID: 31245594 PMCID: PMC6508774 DOI: 10.1002/lrh2.10071] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Revised: 08/30/2018] [Accepted: 09/06/2018] [Indexed: 11/11/2022] Open
Abstract
INTRODUCTION Precision medicine is an important milestone toward the attainment of personalized medicine. A learning health system (LHS) may facilitate the evidence collection and knowledge generation process for disease-based research and for the diagnosis, classification, or treatment of each disease subtype to improve patient care. METHODS The LHS design and implementation used by Taichung Veterans General Hospital (TCVGH) in Taiwan for their newly funded precision medicine research, a dementia registry study, was modeled from an LHS developed at the National Institutes of Health in the United States. This Clinical Informatics and Management System (CIMS), including its subsystems, facilitates and enhances operations associated with the institutional review board, clinical research data collection and study management, the hospital biobank, and the participating health research centers to support their precision medicine research aimed at improving patient care. RESULTS The implementation of a shared-design, full-cycle LHS with an enhanced CIMS, combined with hospital-based real-world data marts, has made the TCVGH dementia registry study a reality. The research data, including clinical assessment and genomics analysis information collected in CIMS, combined with data marts, are the foundation of the TCVGH dementia registry for outcome analyses. These high-quality datasets are useful for clinical validation, new hypotheses, and knowledge generation, leading to new clinical recommendations or guidelines for better patient treatment and care. The cyclic data flow supports the full-cycle LHS for TCVGH's dementia research to improve the care of elderly patients. CONCLUSIONS Knowledge generation requires high-quality research and health care datasets. While the details of LHS implementation methods in the United States and Taiwan may differ slightly, the LHS concept design and basic system architecture, with improved CIMSs, were proven feasible. As a result, learning health processes in support of translational research and the potential for improvement in patient care were significantly facilitated.
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Affiliation(s)
- Ueng‐Cheng Yang
- Institute of Biomedical InformaticsNational Yang‐Ming UniversityTaipeiTaiwan
| | - Tzu‐Hung Hsiao
- Department of Medical ResearchTaichung Veterans General HospitalTaichungTaiwan
| | - Ching‐Heng Lin
- Department of Medical ResearchTaichung Veterans General HospitalTaichungTaiwan
| | - Wei‐Ju Lee
- Center for Geriatrics and GerontologyTaichung Veterans General HospitalTaichungTaiwan
- Neurological InstituteTaichung Veterans General HospitalTaichungTaiwan
- Faculty of MedicineNational Yang‐Ming University School of MedicineTaipeiTaiwan
- Institute of Clinical MedicineNational Yang‐Ming University School of MedicineTaipeiTaiwan
| | - Yu‐Shan Lee
- Center for Geriatrics and GerontologyTaichung Veterans General HospitalTaichungTaiwan
- PhD Program in Translational MedicineNational Chung Hsing UniversityTaichungTaiwan
| | - Yang C. Fann
- Division of Intramural ResearchNational Institute of Neurological Disorders and Stroke, National Institutes of HealthBethesdaMaryland
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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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20
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Brix TJ, Bruland P, Sarfraz S, Ernsting J, Neuhaus P, Storck M, Doods J, Ständer S, Dugas M. ODM Data Analysis-A tool for the automatic validation, monitoring and generation of generic descriptive statistics of patient data. PLoS One 2018; 13:e0199242. [PMID: 29933373 PMCID: PMC6014674 DOI: 10.1371/journal.pone.0199242] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Accepted: 06/04/2018] [Indexed: 11/30/2022] Open
Abstract
INTRODUCTION A required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system and import it into statistical software like SAS software or IBM SPSS. This software requires trained users, who have to implement the analysis individually for each item. These expenditures may become an obstacle for small studies. Objective of this work is to design, implement and evaluate an open source application, called ODM Data Analysis, for the semi-automatic analysis of clinical study data. METHODS The system requires clinical data in the CDISC Operational Data Model format. After uploading the file, its syntax and data type conformity of the collected data is validated. The completeness of the study data is determined and basic statistics, including illustrative charts for each item, are generated. Datasets from four clinical studies have been used to evaluate the application's performance and functionality. RESULTS The system is implemented as an open source web application (available at https://odmanalysis.uni-muenster.de) and also provided as Docker image which enables an easy distribution and installation on local systems. Study data is only stored in the application as long as the calculations are performed which is compliant with data protection endeavors. Analysis times are below half an hour, even for larger studies with over 6000 subjects. DISCUSSION Medical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting.
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Affiliation(s)
| | - Philipp Bruland
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Saad Sarfraz
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Jan Ernsting
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Philipp Neuhaus
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Michael Storck
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Justin Doods
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Sonja Ständer
- Competence Center Chronic Pruritus, Department of Dermatology, University of Münster, Münster, Germany
| | - Martin Dugas
- Institute of Medical Informatics, University of Münster, Münster, Germany
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21
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Carrion J. Improving the Patient-Clinician Interface of Clinical Trials through Health Informatics Technologies. J Med Syst 2018; 42:120. [PMID: 29845581 DOI: 10.1007/s10916-018-0973-y] [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: 01/24/2018] [Accepted: 05/18/2018] [Indexed: 12/17/2022]
Abstract
The current state of clinical trials underscores a need for timely interventions to reduce the cost and length of the average trial. Newly developed health informatics technologies-including electronic health records, telemedicine systems, and mobile health applications-have recently been employed in a wide range of clinical trials in an effort to improve different aspects of the clinical trial process. The current review will focus on the observed benefits and drawbacks of using such technology to improve various patient-centered aspects of the clinical trial process, namely its potential to improve patient recruitment, patient retention, and data collection. Broad future challenges and opportunities in the field as a whole will also be covered.
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Affiliation(s)
- Jake Carrion
- Department of Biomedical Informatics, Columbia University, 622 W 168th St, New York, NY, 10032, USA.
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22
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Leroux H, Metke-Jimenez A, Lawley MJ. Towards achieving semantic interoperability of clinical study data with FHIR. J Biomed Semantics 2017; 8:41. [PMID: 28927443 PMCID: PMC5606031 DOI: 10.1186/s13326-017-0148-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Accepted: 09/03/2017] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Observational clinical studies play a pivotal role in advancing medical knowledge and patient healthcare. To lessen the prohibitive costs of conducting these studies and support evidence-based medicine, results emanating from these studies need to be shared and compared to one another. Current approaches for clinical study management have limitations that prohibit the effective sharing of clinical research data. METHODS The objective of this paper is to present a proposal for a clinical study architecture to not only facilitate the communication of clinical study data but also its context so that the data that is being communicated can be unambiguously understood at the receiving end. Our approach is two-fold. First we outline our methodology to map clinical data from Clinical Data Interchange Standards Consortium Operational Data Model (ODM) to the Fast Healthcare Interoperable Resource (FHIR) and outline the strengths and weaknesses of this approach. Next, we propose two FHIR-based models, to capture the metadata and data from the clinical study, that not only facilitate the syntactic but also semantic interoperability of clinical study data. CONCLUSIONS This work shows that our proposed FHIR resources provide a good fit to semantically enrich the ODM data. By exploiting the rich information model in FHIR, we can organise clinical data in a manner that preserves its organisation but captures its context. Our implementations demonstrate that FHIR can natively manage clinical data. Furthermore, by providing links at several levels, it improves the traversal and querying of the data. The intended benefits of this approach is more efficient and effective data exchange that ultimately will allow clinicians to switch their focus back to decision-making and evidence-based medicines.
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Affiliation(s)
- Hugo Leroux
- The Australian E-Health Research Centre, CSIRO Health and Biosecurity, Level 5, Health Sciences Building 901/16, Royal Brisbane and Women’s Hospital, Herston, 4029 Queensland Australia
| | - Alejandro Metke-Jimenez
- The Australian E-Health Research Centre, CSIRO Health and Biosecurity, Level 5, Health Sciences Building 901/16, Royal Brisbane and Women’s Hospital, Herston, 4029 Queensland Australia
| | - Michael J. Lawley
- The Australian E-Health Research Centre, CSIRO Health and Biosecurity, Level 5, Health Sciences Building 901/16, Royal Brisbane and Women’s Hospital, Herston, 4029 Queensland Australia
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Jenders RA. Advances in Clinical Decision Support: Highlights of Practice and the Literature 2015-2016. Yearb Med Inform 2017; 26:125-132. [PMID: 29063552 PMCID: PMC6239223 DOI: 10.15265/iy-2017-012] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Indexed: 12/30/2022] Open
Abstract
Introduction: Advances in clinical decision support (CDS) continue to evolve to support the goals of clinicians, policymakers, patients and professional organizations to improve clinical practice, patient safety, and the quality of care. Objectives: Identify key thematic areas or foci in research and practice involving clinical decision support during the 2015-2016 time period. Methods: Thematic analysis consistent with a grounded theory approach was applied in a targeted review of journal publications, the proceedings of key scientific conferences as well as activities in standards development organizations in order to identify the key themes underlying work related to CDS. Results: Ten key thematic areas were identified, including: 1) an emphasis on knowledge representation, with a focus on clinical practice guidelines; 2) various aspects of precision medicine, including the use of sensor and genomic data as well as big data; 3) efforts in quality improvement; 4) innovative uses of computer-based provider order entry (CPOE) systems, including relevant data displays; 5) expansion of CDS in various clinical settings; 6) patient-directed CDS; 7) understanding the potential negative impact of CDS; 8) obtaining structured data to drive CDS interventions; 9) the use of diagnostic decision support; and 10) the development and use of standards for CDS. Conclusions: Active research and practice in 2015-2016 continue to underscore the importance and broad utility of CDS for effecting change and improving the quality and outcome of clinical care.
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Affiliation(s)
- R. A. Jenders
- Center for Biomedical Informatics and Department of Medicine, Charles Drew University, Los Angeles, California, USA
- Clinical and Translational Science Institute and Department of Medicine, University of California, Los Angeles, California, USA
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Neville J, Kopko S, Romero K, Corrigan B, Stafford B, LeRoy E, Broadbent S, Cisneroz M, Wilson E, Reiman E, Vanderstichele H, Arnerić SP, Stephenson D. Accelerating drug development for Alzheimer's disease through the use of data standards. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2017; 3:273-283. [PMID: 29067333 PMCID: PMC5651436 DOI: 10.1016/j.trci.2017.03.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
INTRODUCTION The exceedingly high rate of failed trials in Alzheimer's disease (AD) calls for immediate attention to improve efficiencies and learning from past, ongoing, and future trials. Accurate, highly rigorous standardized data are at the core of meaningful scientific research. Data standards allow for proper integration of clinical data sets and represent the essential foundation for regulatory endorsement of drug development tools. Such tools increase the potential for success and accuracy of trial results. METHODS The development of the Clinical Data Interchange Standards Consortium (CDISC) AD therapeutic area data standard was a comprehensive collaborative effort by CDISC and Coalition Against Major Diseases, a consortium of the Critical Path Institute. Clinical concepts for AD and mild cognitive impairment were defined and a data standards user guide was created from various sources of input, including data dictionaries used in AD clinical trials and observational studies. RESULTS A comprehensive collection of AD-specific clinical data standards consisting of clinical outcome measures, leading candidate genes, and cerebrospinal fluid and imaging biomarkers was developed. The AD version 2.0 (V2.0) Therapeutic Area User Guide was developed by diverse experts working with data scientists across multiple consortia through a comprehensive review and revision process. The AD CDISC standard is a publicly available resource to facilitate widespread use and implementation. DISCUSSION The AD CDISC V2.0 data standard serves as a platform to catalyze reproducible research, data integration, and efficiencies in clinical trials. It allows for the mapping and integration of available data and provides a foundation for future studies, data sharing, and long-term registries in AD. The availability of consensus data standards for AD has the potential to facilitate clinical trial initiation and increase sharing and aggregation of data across observational studies and among clinical trials, thereby improving our understanding of disease progression and treatment.
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Affiliation(s)
| | | | | | - Brian Corrigan
- Division of Pharmacometrics, Pfizer Global Research and Development, Groton, CT, USA
| | | | | | | | - Martin Cisneroz
- College of Medicine, The University of Arizona, Tucson, AZ, USA
| | | | - Eric Reiman
- Banner Medical institute, Arizona State University, Phoenix, AZ, USA
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Yamamoto K, Ota K, Akiya I, Shintani A. A pragmatic method for transforming clinical research data from the research electronic data capture "REDCap" to Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM): Development and evaluation of REDCap2SDTM. J Biomed Inform 2017; 70:65-76. [PMID: 28487263 DOI: 10.1016/j.jbi.2017.05.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Revised: 04/29/2017] [Accepted: 05/04/2017] [Indexed: 10/19/2022]
Abstract
The Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM) can be used for new drug application studies as well as secondarily for creating a clinical research data warehouse to leverage clinical research study data across studies conducted within the same disease area. However, currently not all clinical research uses Clinical Data Acquisition Standards Harmonization (CDASH) beginning in the set-up phase of the study. Once already initiated, clinical studies that have not utilized CDASH are difficult to map in the SDTM format. In addition, most electronic data capture (EDC) systems are not equipped to export data in SDTM format; therefore, in many cases, statistical software is used to generate SDTM datasets from accumulated clinical data. In order to facilitate efficient secondary use of accumulated clinical research data using SDTM, it is necessary to develop a new tool to enable mapping of information for SDTM, even during or after the clinical research. REDCap is an EDC system developed by Vanderbilt University and is used globally by over 2100 institutions across 108 countries. In this study, we developed a simulated clinical trial to evaluate a tool called REDCap2SDTM that maps information in the Field Annotation of REDCap to SDTM and executes data conversion, including when data must be pivoted to accommodate the SDTM format, dynamically, by parsing the mapping information using R. We confirmed that generating SDTM data and the define.xml file from REDCap using REDCap2SDTM was possible. Conventionally, generation of SDTM data and the define.xml file from EDC systems requires the creation of individual programs for each clinical study. However, our proposed method can be used to generate this data and file dynamically without programming because it only involves entering the mapping information into the Field Annotation, and additional data into specific files. Our proposed method is adaptable not only to new drug application studies but also to all types of research, including observational and public health studies. Our method is also adaptable to clinical data collected with CDASH at the beginning of a study in non-standard format. We believe that this tool will reduce the workload of new drug application studies and will support data sharing and reuse of clinical research data in academia.
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Affiliation(s)
- Keiichi Yamamoto
- REDCap Group, Department of Medical Innovation, Osaka University Hospital, Osaka 565-0871, Japan.
| | - Keiko Ota
- REDCap Group, Department of Medical Innovation, Osaka University Hospital, Osaka 565-0871, Japan
| | | | - Ayumi Shintani
- REDCap Group, Department of Medical Innovation, Osaka University Hospital, Osaka 565-0871, Japan; Department of Clinical Epidemiology and Biostatistics, Graduate School of Medicine, Osaka University, Osaka 565-0871, Japan
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Bruland P, Dugas M. S2O - A software tool for integrating research data from general purpose statistic software into electronic data capture systems. BMC Med Inform Decis Mak 2017; 17:3. [PMID: 28061771 PMCID: PMC5219713 DOI: 10.1186/s12911-016-0402-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Accepted: 11/22/2016] [Indexed: 11/28/2022] Open
Abstract
Background Data capture for clinical registries or pilot studies is often performed in spreadsheet-based applications like Microsoft Excel or IBM SPSS. Usually, data is transferred into statistic software, such as SAS, R or IBM SPSS Statistics, for analyses afterwards. Spreadsheet-based solutions suffer from several drawbacks: It is generally not possible to ensure a sufficient right and role management; it is not traced who has changed data when and why. Therefore, such systems are not able to comply with regulatory requirements for electronic data capture in clinical trials. In contrast, Electronic Data Capture (EDC) software enables a reliable, secure and auditable collection of data. In this regard, most EDC vendors support the CDISC ODM standard to define, communicate and archive clinical trial meta- and patient data. Advantages of EDC systems are support for multi-user and multicenter clinical trials as well as auditable data. Migration from spreadsheet based data collection to EDC systems is labor-intensive and time-consuming at present. Hence, the objectives of this research work are to develop a mapping model and implement a converter between the IBM SPSS and CDISC ODM standard and to evaluate this approach regarding syntactic and semantic correctness. Results A mapping model between IBM SPSS and CDISC ODM data structures was developed. SPSS variables and patient values can be mapped and converted into ODM. Statistical and display attributes from SPSS are not corresponding to any ODM elements; study related ODM elements are not available in SPSS. The S2O converting tool was implemented as command-line-tool using the SPSS internal Java plugin. Syntactic and semantic correctness was validated with different ODM tools and reverse transformation from ODM into SPSS format. Clinical data values were also successfully transformed into the ODM structure. Conclusion Transformation between the spreadsheet format IBM SPSS and the ODM standard for definition and exchange of trial data is feasible. S2O facilitates migration from Excel- or SPSS-based data collections towards reliable EDC systems. Thereby, advantages of EDC systems like reliable software architecture for secure and traceable data collection and particularly compliance with regulatory requirements are achievable. Electronic supplementary material The online version of this article (doi:10.1186/s12911-016-0402-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Philipp Bruland
- Institute of Medical Informatics, University of Münster, 48149, Münster, Germany.
| | - Martin Dugas
- Institute of Medical Informatics, University of Münster, 48149, Münster, Germany
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Design of case report forms based on a public metadata registry: re-use of data elements to improve compatibility of data. Trials 2016; 17:566. [PMID: 27899162 PMCID: PMC5129226 DOI: 10.1186/s13063-016-1691-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2015] [Accepted: 11/10/2016] [Indexed: 11/17/2022] Open
Abstract
Background Clinical trials use many case report forms (CRFs) per patient. Because of the astronomical number of potential CRFs, data element re-use at the design stage is attractive to foster compatibility of data from different trials. The objective of this work is to assess the technical feasibility of a CRF editor with connection to a public metadata registry (MDR) to support data element re-use. Results Based on the Medical Data Models portal, an ISO/IEC 11179-compliant MDR was implemented and connected to a web-based CRF editor. Three use cases were implemented: re-use at the form, item group and data element levels. Conclusions CRF design with data element re-use from a public MDR is feasible. A prototypic system is available. The main limitation of the system is the amount of available MDR content.
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Abstract
OBJECTIVES To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2015. METHOD A bibliographic search using a combination of MeSH and free terms search over PubMed on Clinical Research Informatics (CRI) was performed followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the editorial team was finally organized to conclude on the selection of best papers. RESULTS Among the 579 returned papers published in the past year in the various areas of Clinical Research Informatics (CRI) - i) methods supporting clinical research, ii) data sharing and interoperability, iii) re-use of healthcare data for research, iv) patient recruitment and engagement, v) data privacy, security and regulatory issues and vi) policy and perspectives - the full review process selected four best papers. The first selected paper evaluates the capability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) to support the representation of case report forms (in both the design stage and with patient level data) during a complete clinical study lifecycle. The second selected paper describes a prototype for secondary use of electronic health records data captured in non-standardized text. The third selected paper presents a privacy preserving electronic health record linkage tool and the last selected paper describes how big data use in US relies on access to health information governed by varying and often misunderstood legal requirements and ethical considerations. CONCLUSIONS A major trend in the 2015 publications is the analysis of observational, "nonexperimental" information and the potential biases and confounding factors hidden in the data that will have to be carefully taken into account to validate new predictive models. In addiction, researchers have to understand complicated and sometimes contradictory legal requirements and to consider ethical obligations in order to balance privacy and promoting discovery.
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Affiliation(s)
- C Daniel
- Christel Daniel, MD, PhD, INSERM UMRS 1142 - WIND-DSI, - Assistance Publique - Hôpitaux de Paris, 05 rue Santerre, 75 012 Paris, France, Tel: +33 1 48 04 20 29, E-mail: christel.daniel@ aphp.fr
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Storck M, Krumm R, Dugas M. ODMSummary: A Tool for Automatic Structured Comparison of Multiple Medical Forms Based on Semantic Annotation with the Unified Medical Language System. PLoS One 2016; 11:e0164569. [PMID: 27736972 PMCID: PMC5063379 DOI: 10.1371/journal.pone.0164569] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2016] [Accepted: 09/27/2016] [Indexed: 12/01/2022] Open
Abstract
Introduction Medical documentation is applied in various settings including patient care and clinical research. Since procedures of medical documentation are heterogeneous and developed further, secondary use of medical data is complicated. Development of medical forms, merging of data from different sources and meta-analyses of different data sets are currently a predominantly manual process and therefore difficult and cumbersome. Available applications to automate these processes are limited. In particular, tools to compare multiple documentation forms are missing. The objective of this work is to design, implement and evaluate the new system ODMSummary for comparison of multiple forms with a high number of semantically annotated data elements and a high level of usability. Methods System requirements are the capability to summarize and compare a set of forms, enable to estimate the documentation effort, track changes in different versions of forms and find comparable items in different forms. Forms are provided in Operational Data Model format with semantic annotations from the Unified Medical Language System. 12 medical experts were invited to participate in a 3-phase evaluation of the tool regarding usability. Results ODMSummary (available at https://odmtoolbox.uni-muenster.de/summary/summary.html) provides a structured overview of multiple forms and their documentation fields. This comparison enables medical experts to assess multiple forms or whole datasets for secondary use. System usability was optimized based on expert feedback. Discussion The evaluation demonstrates that feedback from domain experts is needed to identify usability issues. In conclusion, this work shows that automatic comparison of multiple forms is feasible and the results are usable for medical experts.
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Affiliation(s)
- Michael Storck
- Institute of Medical Informatics, University of Münster, Münster, Germany
- * E-mail:
| | - Rainer Krumm
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Martin Dugas
- Institute of Medical Informatics, University of Münster, Münster, Germany
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Hume S, Aerts J, Sarnikar S, Huser V. Current applications and future directions for the CDISC Operational Data Model standard: A methodological review. J Biomed Inform 2016; 60:352-62. [PMID: 26944737 PMCID: PMC4837012 DOI: 10.1016/j.jbi.2016.02.016] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Revised: 02/21/2016] [Accepted: 02/22/2016] [Indexed: 11/25/2022]
Abstract
INTRODUCTION In order to further advance research and development on the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) standard, the existing research must be well understood. This paper presents a methodological review of the ODM literature. Specifically, it develops a classification schema to categorize the ODM literature according to how the standard has been applied within the clinical research data lifecycle. This paper suggests areas for future research and development that address ODM's limitations and capitalize on its strengths to support new trends in clinical research informatics. METHODS A systematic scan of the following databases was performed: (1) ABI/Inform, (2) ACM Digital, (3) AIS eLibrary, (4) Europe Central PubMed, (5) Google Scholar, (5) IEEE Xplore, (7) PubMed, and (8) ScienceDirect. A Web of Science citation analysis was also performed. The search term used on all databases was "CDISC ODM." The two primary inclusion criteria were: (1) the research must examine the use of ODM as an information system solution component, or (2) the research must critically evaluate ODM against a stated solution usage scenario. Out of 2686 articles identified, 266 were included in a title level review, resulting in 183 articles. An abstract review followed, resulting in 121 remaining articles; and after a full text scan 69 articles met the inclusion criteria. RESULTS As the demand for interoperability has increased, ODM has shown remarkable flexibility and has been extended to cover a broad range of data and metadata requirements that reach well beyond ODM's original use cases. This flexibility has yielded research literature that covers a diverse array of topic areas. A classification schema reflecting the use of ODM within the clinical research data lifecycle was created to provide a categorized and consolidated view of the ODM literature. The elements of the framework include: (1) EDC (Electronic Data Capture) and EHR (Electronic Health Record) infrastructure; (2) planning; (3) data collection; (4) data tabulations and analysis; and (5) study archival. The analysis reviews the strengths and limitations of ODM as a solution component within each section of the classification schema. This paper also identifies opportunities for future ODM research and development, including improved mechanisms for semantic alignment with external terminologies, better representation of the CDISC standards used end-to-end across the clinical research data lifecycle, improved support for real-time data exchange, the use of EHRs for research, and the inclusion of a complete study design. CONCLUSIONS ODM is being used in ways not originally anticipated, and covers a diverse array of use cases across the clinical research data lifecycle. ODM has been used as much as a study metadata standard as it has for data exchange. A significant portion of the literature addresses integrating EHR and clinical research data. The simplicity and readability of ODM has likely contributed to its success and broad implementation as a data and metadata standard. Keeping the core ODM model focused on the most fundamental use cases, while using extensions to handle edge cases, has kept the standard easy for developers to learn and use.
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Affiliation(s)
- Sam Hume
- Dakota State University, College of Business and Information Systems, 820 N Washington Ave, Madison, SD 57042, United States.
| | - Jozef Aerts
- FH Joanneum University of Applied Sciences, Eggenberger Allee 11, 8020 Graz, Austria.
| | - Surendra Sarnikar
- Dakota State University, College of Business and Information Systems, 820 N Washington Ave, Madison, SD 57042, United States.
| | - Vojtech Huser
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bld 38a, Rm 9N919, Bethesda, MD 20894, United States.
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Engelgau MM, Sampson UK, Rabadan-Diehl C, Smith R, Miranda J, Bloomfield GS, Belis D, Narayan KMV. Tackling NCD in LMIC: Achievements and Lessons Learned From the NHLBI-UnitedHealth Global Health Centers of Excellence Program. Glob Heart 2016; 11:5-15. [PMID: 27102018 PMCID: PMC4843818 DOI: 10.1016/j.gheart.2015.12.016] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Accepted: 12/21/2015] [Indexed: 01/14/2023] Open
Abstract
Effectively tackling the growing noncommunicable disease (NCD) burden in low- and middle-income countries (LMIC) is a major challenge. To address research needs in this setting for NCDs, in 2009, National Heart, Lung, and Blood Institute (NHLBI) and UnitedHealth Group (UHG) engaged in a public-private partnership that supported a network of 11 LMIC-based research centers and created the NHLBI-UnitedHealth Global Health Centers of Excellence (COE) Program. The Program's overall goal was to contribute to reducing the cardiovascular and lung disease burdens by catalyzing in-country research institutions to develop a global network of biomedical research centers. Key elements of the Program included team science and collaborative approaches, developing research and training platforms for future investigators, and creating a data commons. This Program embraced a strategic approach for tackling NCDs in LMICs and will provide capacity for locally driven research efforts that can identify and address priority health issues in specific countries' settings.
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Affiliation(s)
- Michael M Engelgau
- Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Uchechukwu K Sampson
- Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Cristina Rabadan-Diehl
- Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Richard Smith
- Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jaime Miranda
- Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Gerald S Bloomfield
- Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Deshiree Belis
- Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - K M Venkat Narayan
- Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
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Obeid JS, Alexander RW, Gentilin SM, White B, Turley CB, Brady KT, Lenert LA. IRB reliance: An informatics approach. J Biomed Inform 2016; 60:58-65. [PMID: 26827623 DOI: 10.1016/j.jbi.2016.01.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2015] [Revised: 01/21/2016] [Accepted: 01/22/2016] [Indexed: 11/15/2022]
Abstract
Multi-site Institutional Review Board (IRB) review of clinical research projects is an important but complex and time-consuming activity that is hampered by disparate non-interoperable computer systems for management of IRB applications. This paper describes our work toward harmonizing the workflow and data model of IRB applications through the development of a software-as-a-service shared-IRB platform for five institutions in South Carolina. Several commonalities and differences were recognized across institutions and a core data model that included the data elements necessary for IRB applications across all institutions was identified. We extended and modified the system to support collaborative reviews of IRB proposals within routine workflows of participating IRBs. Overall about 80% of IRB application content was harmonized across all institutions, establishing the foundation for a streamlined cooperative review and reliance. Since going live in 2011, 49 applications that underwent cooperative reviews over a three year period were approved, with the majority involving 2 out of 5 institutions. We believe this effort will inform future work on a common IRB data model that will allow interoperability through a federated approach for sharing IRB reviews and decisions with the goal of promoting reliance across institutions in the translational research community at large.
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Affiliation(s)
- Jihad S Obeid
- Medical University of South Carolina, Charleston, SC, USA.
| | | | | | - Brigette White
- Medical University of South Carolina, Charleston, SC, USA
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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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Uciteli A, Groß S, Kireyev S, Herre H. An ontologically founded architecture for information systems in clinical and epidemiological research. J Biomed Semantics 2011; 2 Suppl 4:S1. [PMID: 21995847 PMCID: PMC3194168 DOI: 10.1186/2041-1480-2-s4-s1] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
This paper presents an ontologically founded basic architecture for information systems, which are intended to capture, represent, and maintain metadata for various domains of clinical and epidemiological research. Clinical trials exhibit an important basis for clinical research, and the accurate specification of metadata and their documentation and application in clinical and epidemiological study projects represents a significant expense in the project preparation and has a relevant impact on the value and quality of these studies.An ontological foundation of an information system provides a semantic framework for the precise specification of those entities which are presented in this system. This semantic framework should be grounded, according to our approach, on a suitable top-level ontology. Such an ontological foundation leads to a deeper understanding of the entities of the domain under consideration, and provides a common unifying semantic basis, which supports the integration of data and the interoperability between different information systems.The intended information systems will be applied to the field of clinical and epidemiological research and will provide, depending on the application context, a variety of functionalities. In the present paper, we focus on a basic architecture which might be common to all such information systems. The research, set forth in this paper, is included in a broader framework of clinical research and continues the work of the IMISE on these topics.
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Affiliation(s)
- Alexandr Uciteli
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Germany
| | - Silvia Groß
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Germany
- LIFE – Leipzig Research Center for Civilization Diseases, Universität Leipzig, Germany
| | - Sergej Kireyev
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Germany
| | - Heinrich Herre
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Germany
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Ganslandt T, Mate S, Helbing K, Sax U, Prokosch HU. Unlocking Data for Clinical Research - The German i2b2 Experience. Appl Clin Inform 2011; 2:116-27. [PMID: 23616864 DOI: 10.4338/aci-2010-09-cr-0051] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2010] [Accepted: 01/19/2011] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE Data from clinical care is increasingly being used for research purposes. The i2b2 platform has been introduced in some US research communities as a tool for data integration and querying by clinical users. The purpose of this project was to assess the applicability of i2b2 in Germany regarding use cases, functionality and integration with privacy enhancing tools. METHODS A set of four research usage scenarios was chosen, including the transformation and import of ontology and fact data from existing clinical data collections into i2b2 v1.4 instances. Query performance was measured in comparison to native SQL queries. A setup and administration tool for i2b2 was developed. An extraction tool for CDISC ODM data was programmed. Interfaces for the TMF privacy enhancing tools (PID Generator, Pseudonymization Service) were implemented. RESULTS Data could be imported in all tested scenarios from various source systems, including the generation of i2b2 ontology definitions. The integration of TMF privacy enhancing tools was possible without modification of the platform. Limitations were found regarding query performance in comparison to native SQL and certain temporal queries. CONCLUSIONS i2b2 is a viable platform for data query tasks in use cases typical for networked medical research in Germany. The integration of privacy enhancing tools facilitates the use of i2b2 within established data protection concepts. Entry barriers should be lowered by providing tools for simplified setup and import of medical standard formats like CDISC ODM.
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Affiliation(s)
- T Ganslandt
- Center for Medical Information and Communication, Erlangen University Hospital , Erlangen, Germany
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