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Hu Z, Wang A, Duan Y, Zhou J, Hu W, Wu S. Toward Better Semantic Interoperability of Data Element Repositories in Medicine: Analysis Study. JMIR Med Inform 2024; 12:e60293. [PMID: 39348178 DOI: 10.2196/60293] [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: 05/11/2024] [Revised: 07/07/2024] [Accepted: 07/21/2024] [Indexed: 10/01/2024] Open
Abstract
BACKGROUND Data element repositories facilitate high-quality medical data sharing by standardizing data and enhancing semantic interoperability. However, the application of repositories is confined to specific projects and institutions. OBJECTIVE This study aims to explore potential issues and promote broader application of data element repositories within the medical field by evaluating and analyzing typical repositories. METHODS Following the inclusion of 5 data element repositories through a literature review, a novel analysis framework consisting of 7 dimensions and 36 secondary indicators was constructed and used for evaluation and analysis. RESULTS The study's results delineate the unique characteristics of different repositories and uncover specific issues in their construction. These issues include the absence of data reuse protocols and insufficient information regarding the application scenarios and efficacy of data elements. The repositories fully comply with only 45% (9/20) of the subprinciples for Findable and Reusable in the FAIR principle, while achieving a 90% (19/20 subprinciples) compliance rate for Accessible and 67% (10/15 subprinciples) for Interoperable. CONCLUSIONS The recommendations proposed in this study address the issues to improve the construction and application of repositories, offering valuable insights to data managers, computer experts, and other pertinent stakeholders.
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Affiliation(s)
- Zhengyong Hu
- Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Anran Wang
- Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Yifan Duan
- Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Jiayin Zhou
- Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Wanfei Hu
- Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Sizhu Wu
- Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
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Ahmadi N, Zoch M, Guengoeze O, Facchinello C, Mondorf A, Stratmann K, Musleh K, Erasmus HP, Tchertov J, Gebler R, Schaaf J, Frischen LS, Nasirian A, Dai J, Henke E, Tremblay D, Srisuwananukorn A, Bornhäuser M, Röllig C, Eckardt JN, Middeke JM, Wolfien M, Sedlmayr M. How to customize common data models for rare diseases: an OMOP-based implementation and lessons learned. Orphanet J Rare Dis 2024; 19:298. [PMID: 39143600 PMCID: PMC11325822 DOI: 10.1186/s13023-024-03312-9] [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] [Received: 11/29/2023] [Accepted: 08/06/2024] [Indexed: 08/16/2024] Open
Abstract
BACKGROUND Given the geographical sparsity of Rare Diseases (RDs), assembling a cohort is often a challenging task. Common data models (CDM) can harmonize disparate sources of data that can be the basis of decision support systems and artificial intelligence-based studies, leading to new insights in the field. This work is sought to support the design of large-scale multi-center studies for rare diseases. METHODS In an interdisciplinary group, we derived a list of elements of RDs in three medical domains (endocrinology, gastroenterology, and pneumonology) according to specialist knowledge and clinical guidelines in an iterative process. We then defined a RDs data structure that matched all our data elements and built Extract, Transform, Load (ETL) processes to transfer the structure to a joint CDM. To ensure interoperability of our developed CDM and its subsequent usage for further RDs domains, we ultimately mapped it to Observational Medical Outcomes Partnership (OMOP) CDM. We then included a fourth domain, hematology, as a proof-of-concept and mapped an acute myeloid leukemia (AML) dataset to the developed CDM. RESULTS We have developed an OMOP-based rare diseases common data model (RD-CDM) using data elements from the three domains (endocrinology, gastroenterology, and pneumonology) and tested the CDM using data from the hematology domain. The total study cohort included 61,697 patients. After aligning our modules with those of Medical Informatics Initiative (MII) Core Dataset (CDS) modules, we leveraged its ETL process. This facilitated the seamless transfer of demographic information, diagnoses, procedures, laboratory results, and medication modules from our RD-CDM to the OMOP. For the phenotypes and genotypes, we developed a second ETL process. We finally derived lessons learned for customizing our RD-CDM for different RDs. DISCUSSION This work can serve as a blueprint for other domains as its modularized structure could be extended towards novel data types. An interdisciplinary group of stakeholders that are actively supporting the project's progress is necessary to reach a comprehensive CDM. CONCLUSION The customized data structure related to our RD-CDM can be used to perform multi-center studies to test data-driven hypotheses on a larger scale and take advantage of the analytical tools offered by the OHDSI community.
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Affiliation(s)
- Najia Ahmadi
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany.
| | - Michele Zoch
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany
| | - Oya Guengoeze
- Department of Internal Medicine I, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Carlo Facchinello
- Department of Internal Medicine I, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Antonia Mondorf
- Department of Internal Medicine I, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Katharina Stratmann
- Department of Internal Medicine I, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Khader Musleh
- Department of Internal Medicine I, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Hans-Peter Erasmus
- Department of Internal Medicine I, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Jana Tchertov
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany
| | - Richard Gebler
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany
| | - Jannik Schaaf
- Goethe University Frankfurt, University Hospital, Institute of Medical Informatics, Frankfurt, Germany
| | - Lena S Frischen
- University Hospital Frankfurt, Goethe University, Executive Department for Medical IT-Systems and Digitalization, Frankfurt, Germany
| | - Azadeh Nasirian
- Center of Medical Informatics, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Jiabin Dai
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany
| | - Elisa Henke
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany
| | - Douglas Tremblay
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Martin Bornhäuser
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Christoph Röllig
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Jan-Niklas Eckardt
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- Else-Kroener-Fresenius-Center for Digital Health, TUD Dresden University of Technology, Dresden, Germany
| | - Jan Moritz Middeke
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- Else-Kroener-Fresenius-Center for Digital Health, TUD Dresden University of Technology, Dresden, Germany
| | - Markus Wolfien
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, Dresden, Germany
| | - Martin Sedlmayr
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany
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Shafiee F, Sarbaz M, Marouzi P, Banaye Yazdipour A, Kimiafar K. Providing a framework for evaluation disease registry and health outcomes Software: Updating the CIPROS checklist. J Biomed Inform 2024; 149:104574. [PMID: 38101688 DOI: 10.1016/j.jbi.2023.104574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 11/27/2023] [Accepted: 12/08/2023] [Indexed: 12/17/2023]
Abstract
BACKGROUND AND AIMS Properly designed and implemented registry systems play an important role in improving health outcomes and reducing care costs, and can provide a true representation of clinical practice, disease outcomes, safety, and efficacy. Therefore, the aim of this study was to redesign and develop a checklist with items for a patient registry software system (CIPROS) Checklist. METHOD The study is descriptive-cross-sectional. The extraction of the data elements of the checklist was first done through a comprehensive review of the texts in PubMed, Science Direct and Scopus databases and receiving articles related to the evaluation of registry systems. Based on the extracted data, a five-point Likert scale questionnaire was created and 30 experts in this field were asked for their opinions using the two-step Delphi method. RESULTS A total of 100 information items were determined as a registry software evaluation checklist. This checklist included 12 groups of software architecture factors, development, interfaces and interactivity, semantics and standardization, internationality, data management, data quality and usability, data analysis, security, privacy, organizational, education and public factors. CONCLUSION By using the results of this research, it is possible to identify the defects and possible strengths of the registry software and put it at the disposal of the relevant officials to make a decision in this field. In this way, among the designers and developers of these softwares, the best and most appropriate ones are selected with the needs of the registry programs.
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Affiliation(s)
- Fatemeh Shafiee
- Department of Health Information Technology, School of Paramedical and Rehabilitation Sciences, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Masoume Sarbaz
- Department of Health Information Technology, School of Paramedical and Rehabilitation Sciences, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Parviz Marouzi
- Department of Health Information Technology, School of Paramedical and Rehabilitation Sciences, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Alireza Banaye Yazdipour
- Department of Health Information Technology, School of Paramedical and Rehabilitation Sciences, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Health Information Management and Medical Informatics, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran; Students' Scientific Research Center (SSRC), Tehran University of Medical Sciences, Tehran, Iran.
| | - Khalil Kimiafar
- Department of Health Information Technology, School of Paramedical and Rehabilitation Sciences, Mashhad University of Medical Sciences, Mashhad, Iran.
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Carrillo GA, Cohen-Wolkowiez M, D'Agostino EM, Marsolo K, Wruck LM, Johnson L, Topping J, Richmond A, Corbie G, Kibbe WA. Standardizing, Harmonizing, and Protecting Data Collection to Broaden the Impact of COVID-19 Research: The Rapid Acceleration of Diagnostics-Underserved Populations (RADx-up) Initiative. J Am Med Inform Assoc 2022; 29:1480-1488. [PMID: 35678579 PMCID: PMC9382379 DOI: 10.1093/jamia/ocac097] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 05/31/2022] [Accepted: 06/06/2022] [Indexed: 11/16/2022] Open
Abstract
Objective The Rapid Acceleration of Diagnostics-Underserved Populations (RADx-UP) program is a consortium of community-engaged research projects with the goal of increasing access to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) tests in underserved populations. To accelerate clinical research, common data elements (CDEs) were selected and refined to standardize data collection and enhance cross-consortium analysis. Materials and Methods The RADx-UP consortium began with more than 700 CDEs from the National Institutes of Health (NIH) CDE Repository, Disaster Research Response (DR2) guidelines, and the PHENotypes and eXposures (PhenX) Toolkit. Following a review of initial CDEs, we made selections and further refinements through an iterative process that included live forums, consultations, and surveys completed by the first 69 RADx-UP projects. Results Following a multistep CDE development process, we decreased the number of CDEs, modified the question types, and changed the CDE wording. Most research projects were willing to collect and share demographic NIH Tier 1 CDEs, with the top exception reason being a lack of CDE applicability to the project. The NIH RADx-UP Tier 1 CDE with the lowest frequency of collection and sharing was sexual orientation. Discussion We engaged a wide range of projects and solicited bidirectional input to create CDEs. These RADx-UP CDEs could serve as the foundation for a patient-centered informatics architecture allowing the integration of disease-specific databases to support hypothesis-driven clinical research in underserved populations. Conclusion A community-engaged approach using bidirectional feedback can lead to the better development and implementation of CDEs in underserved populations during public health emergencies.
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Affiliation(s)
- Gabriel A Carrillo
- Department of Pediatrics, Duke University School of Medicine, Durham, NC, USA.,Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - Michael Cohen-Wolkowiez
- Department of Pediatrics, Duke University School of Medicine, Durham, NC, USA.,Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - Emily M D'Agostino
- Department of Family Medicine and Community Health, Duke University School of Medicine, Durham, NC, USA.,Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, USA.,Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Keith Marsolo
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA.,Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Lisa M Wruck
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA.,Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Laura Johnson
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - James Topping
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - Al Richmond
- Community-Campus Partnerships for Health, Raleigh, NC, USA
| | - Giselle Corbie
- Center for Health Equity Research, University of North Carolina, Chapel Hill, NC, USA.,Department of Social Medicine and Department of Medicine, University of North Carolina, Chapel Hill, NC, USA.,Department of Internal Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Warren A Kibbe
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.,Duke Cancer Institute, Duke University School of Medicine, Durham, NC, USA
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