1
|
Franklin JB, Marra C, Abebe KZ, Butte AJ, Cook DJ, Esserman L, Fleisher LA, Grossman CI, Kass NE, Krumholz HM, Rowan K, Abernethy AP. Modernizing the Data Infrastructure for Clinical Research to Meet Evolving Demands for Evidence. JAMA 2024:2822037. [PMID: 39102333 DOI: 10.1001/jama.2024.0268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/07/2024]
Abstract
Importance The ways in which we access, acquire, and use data in clinical trials have evolved very little over time, resulting in a fragmented and inefficient system that limits the amount and quality of evidence that can be generated. Observations Clinical trial design has advanced steadily over several decades. Yet the infrastructure for clinical trial data collection remains expensive and labor intensive and limits the amount of evidence that can be collected to inform whether and how interventions work for different patient populations. Meanwhile, there is increasing demand for evidence from randomized clinical trials to inform regulatory decisions, payment decisions, and clinical care. Although substantial public and industry investment in advancing electronic health record interoperability, data standardization, and the technology systems used for data capture have resulted in significant progress on various aspects of data generation, there is now a need to combine the results of these efforts and apply them more directly to the clinical trial data infrastructure. Conclusions and Relevance We describe a vision for a modernized infrastructure that is centered around 2 related concepts. First, allowing the collection and rigorous evaluation of multiple data sources and types and, second, enabling the possibility to reuse health data for multiple purposes. We address the need for multidisciplinary collaboration and suggest ways to measure progress toward this goal.
Collapse
Affiliation(s)
| | | | - Kaleab Z Abebe
- Center for Biostatistics & Qualitative Methodology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Atul J Butte
- Bakar Computational Health Sciences Institute, University of California, San Francisco
- Center for Data-Driven Insights and Innovation, University of California Health, Oakland
| | - Deborah J Cook
- Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Laura Esserman
- Departments of Surgery and Radiology and Institute for Health Policy Studies, University of California, San Francisco
| | - Lee A Fleisher
- Anesthesiology and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia
| | | | - Nancy E Kass
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | - Kathy Rowan
- National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research Programme, London, United Kingdom
- Intensive Care National Audit & Research Centre (ICNARC), London, United Kingdom
| | | |
Collapse
|
2
|
Angus DC, Huang AJ, Lewis RJ, Abernethy AP, Califf RM, Landray M, Kass N, Bibbins-Domingo K. The Integration of Clinical Trials With the Practice of Medicine: Repairing a House Divided. JAMA 2024; 332:153-162. [PMID: 38829654 DOI: 10.1001/jama.2024.4088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
Importance Optimal health care delivery, both now and in the future, requires a continuous loop of knowledge generation, dissemination, and uptake on how best to provide care, not just determining what interventions work but also how best to ensure they are provided to those who need them. The randomized clinical trial (RCT) is the most rigorous instrument to determine what works in health care. However, major issues with both the clinical trials enterprise and the lack of integration of clinical trials with health care delivery compromise medicine's ability to best serve society. Observations In most resource-rich countries, the clinical trials and health care delivery enterprises function as separate entities, with siloed goals, infrastructure, and incentives. Consequently, RCTs are often poorly relevant and responsive to the needs of patients and those responsible for care delivery. At the same time, health care delivery systems are often disengaged from clinical trials and fail to rapidly incorporate knowledge generated from RCTs into practice. Though longstanding, these issues are more pressing given the lessons learned from the COVID-19 pandemic, heightened awareness of the disproportionate impact of poor access to optimal care on vulnerable populations, and the unprecedented opportunity for improvement offered by the digital revolution in health care. Four major areas must be improved. First, especially in the US, greater clarity is required to ensure appropriate regulation and oversight of implementation science, quality improvement, embedded clinical trials, and learning health systems. Second, greater adoption is required of study designs that improve statistical and logistical efficiency and lower the burden on participants and clinicians, allowing trials to be smarter, safer, and faster. Third, RCTs could be considerably more responsive and efficient if they were better integrated with electronic health records. However, this advance first requires greater adoption of standards and processes designed to ensure health data are adequately reliable and accurate and capable of being transferred responsibly and efficiently across platforms and organizations. Fourth, tackling the problems described above requires alignment of stakeholders in the clinical trials and health care delivery enterprises through financial and nonfinancial incentives, which could be enabled by new legislation. Solutions exist for each of these problems, and there are examples of success for each, but there is a failure to implement at adequate scale. Conclusions and Relevance The gulf between current care and that which could be delivered has arguably never been wider. A key contributor is that the 2 limbs of knowledge generation and implementation-the clinical trials and health care delivery enterprises-operate as a house divided. Better integration of these 2 worlds is key to accelerated improvement in health care delivery.
Collapse
Affiliation(s)
- Derek C Angus
- JAMA , Chicago, Illinois
- University of Pittsburgh Schools of the Health Sciences, Pittsburgh, Pennsylvania
| | | | - Roger J Lewis
- JAMA , Chicago, Illinois
- David Geffen School of Medicine at UCLA, Los Angeles, California
| | - Amy P Abernethy
- Verily Life Sciences, San Francisco, California
- Now with Highlander Health, Dallas, Texas
| | | | - Martin Landray
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Protas, Manchester, United Kingdom
| | - Nancy Kass
- Johns Hopkins University, Baltimore, Maryland
| | | |
Collapse
|
3
|
Tsafnat G, Dunscombe R, Gabriel D, Grieve G, Reich C. Converge or Collide? Making Sense of a Plethora of Open Data Standards in Health Care. J Med Internet Res 2024; 26:e55779. [PMID: 38593431 DOI: 10.2196/55779] [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: 12/23/2023] [Accepted: 03/13/2024] [Indexed: 04/11/2024] Open
Abstract
Practitioners of digital health are familiar with disjointed data environments that often inhibit effective communication among different elements of the ecosystem. This fragmentation leads in turn to issues such as inconsistencies in services versus payments, wastage, and notably, care delivered being less than best-practice. Despite the long-standing recognition of interoperable data as a potential solution, efforts in achieving interoperability have been disjointed and inconsistent, resulting in numerous incompatible standards, despite the widespread agreement that fewer standards would enhance interoperability. This paper introduces a framework for understanding health care data needs, discussing the challenges and opportunities of open data standards in the field. It emphasizes the necessity of acknowledging diverse data standards, each catering to specific viewpoints and needs, while proposing a categorization of health care data into three domains, each with its distinct characteristics and challenges, along with outlining overarching design requirements applicable to all domains and specific requirements unique to each domain.
Collapse
Affiliation(s)
- Guy Tsafnat
- Evidentli Pty Ltd, Surry Hills, Australia
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie Univeristy, Macquarie Park, Australia
- OHDSI OMOP + FHIR Working Group,
| | - Rachel Dunscombe
- openEHR International, St. Helens, United Kingdom
- Imperial College London, London, United Kingdom
| | - Davera Gabriel
- Evidentli Pty Ltd, Surry Hills, Australia
- OHDSI OMOP + FHIR Working Group,
- School of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Grahame Grieve
- Health Level 7 International, Ann Arbor, MI, United States
- Health Intersections Pty Ltd, Melbourne, Australia
| | - Christian Reich
- OHDSI OMOP + FHIR Working Group,
- Odysseus Data Services, Cambridge, MA, United States
| |
Collapse
|
4
|
Raycheva R, Kostadinov K, Mitova E, Iskrov G, Stefanov G, Vakevainen M, Elomaa K, Man YS, Gross E, Zschüntzsch J, Röttger R, Stefanov R. Landscape analysis of available European data sources amenable for machine learning and recommendations on usability for rare diseases screening. Orphanet J Rare Dis 2024; 19:147. [PMID: 38582900 PMCID: PMC10998425 DOI: 10.1186/s13023-024-03162-5] [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: 10/17/2023] [Accepted: 03/30/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND Patient registries and databases are essential tools for advancing clinical research in the area of rare diseases, as well as for enhancing patient care and healthcare planning. The primary aim of this study is a landscape analysis of available European data sources amenable to machine learning (ML) and their usability for Rare Diseases screening, in terms of findable, accessible, interoperable, reusable(FAIR), legal, and business considerations. Second, recommendations will be proposed to provide a better understanding of the health data ecosystem. METHODS In the period of March 2022 to December 2022, a cross-sectional study using a semi-structured questionnaire was conducted among potential respondents, identified as main contact person of a health-related databases. The design of the self-completed questionnaire survey instrument was based on information drawn from relevant scientific publications, quantitative and qualitative research, and scoping review on challenges in mapping European rare disease (RD) databases. To determine database characteristics associated with the adherence to the FAIR principles, legal and business aspects of database management Bayesian models were fitted. RESULTS In total, 330 unique replies were processed and analyzed, reflecting the same number of distinct databases (no duplicates included). In terms of geographical scope, we observed 24.2% (n = 80) national, 10.0% (n = 33) regional, 8.8% (n = 29) European, and 5.5% (n = 18) international registries coordinated in Europe. Over 80.0% (n = 269) of the databases were still active, with approximately 60.0% (n = 191) established after the year 2000 and 71.0% last collected new data in 2022. Regarding their geographical scope, European registries were associated with the highest overall FAIR adherence, while registries with regional and "other" geographical scope were ranked at the bottom of the list with the lowest proportion. Responders' willingness to share data as a contribution to the goals of the Screen4Care project was evaluated at the end of the survey. This question was completed by 108 respondents; however, only 18 of them (16.7%) expressed a direct willingness to contribute to the project by sharing their databases. Among them, an equal split between pro-bono and paid services was observed. CONCLUSIONS The most important results of our study demonstrate not enough sufficient FAIR principles adherence and low willingness of the EU health databases to share patient information, combined with some legislation incapacities, resulting in barriers to the secondary use of data.
Collapse
Affiliation(s)
- Ralitsa Raycheva
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria.
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria.
| | - Kostadin Kostadinov
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
| | - Elena Mitova
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
| | - Georgi Iskrov
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
| | - Georgi Stefanov
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
| | - Merja Vakevainen
- Pfizer Biopharmaceuticals Group, Medical Affairs, Helsinki, Finland
| | | | - Yuen-Sum Man
- Global Medical Affairs Rare Disease, Novo Nordisk Health Care AG, Zurich, Switzerland
| | - Edith Gross
- EURORDIS - Rare Diseases Europe, 96 Rue Didot, Paris, 75014, France
| | - Jana Zschüntzsch
- Department of Neurology, University Medical Center, Göttingen, Germany
| | - Richard Röttger
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Rumen Stefanov
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
| |
Collapse
|
5
|
Oliva A, Kaphle A, Reguant R, Sng LMF, Twine NA, Malakar Y, Wickramarachchi A, Keller M, Ranbaduge T, Chan EKF, Breen J, Buckberry S, Guennewig B, Haas M, Brown A, Cowley MJ, Thorne N, Jain Y, Bauer DC. Future-proofing genomic data and consent management: a comprehensive review of technology innovations. Gigascience 2024; 13:giae021. [PMID: 38837943 PMCID: PMC11152178 DOI: 10.1093/gigascience/giae021] [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: 08/14/2023] [Revised: 01/15/2024] [Accepted: 04/09/2024] [Indexed: 06/07/2024] Open
Abstract
Genomic information is increasingly used to inform medical treatments and manage future disease risks. However, any personal and societal gains must be carefully balanced against the risk to individuals contributing their genomic data. Expanding our understanding of actionable genomic insights requires researchers to access large global datasets to capture the complexity of genomic contribution to diseases. Similarly, clinicians need efficient access to a patient's genome as well as population-representative historical records for evidence-based decisions. Both researchers and clinicians hence rely on participants to consent to the use of their genomic data, which in turn requires trust in the professional and ethical handling of this information. Here, we review existing and emerging solutions for secure and effective genomic information management, including storage, encryption, consent, and authorization that are needed to build participant trust. We discuss recent innovations in cloud computing, quantum-computing-proof encryption, and self-sovereign identity. These innovations can augment key developments from within the genomics community, notably GA4GH Passports and the Crypt4GH file container standard. We also explore how decentralized storage as well as the digital consenting process can offer culturally acceptable processes to encourage data contributions from ethnic minorities. We conclude that the individual and their right for self-determination needs to be put at the center of any genomics framework, because only on an individual level can the received benefits be accurately balanced against the risk of exposing private information.
Collapse
Affiliation(s)
- Adrien Oliva
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Level 3/160 Hawkesbury Rd, Westmead NSW 2145, Australia
| | - Anubhav Kaphle
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Level 3/160 Hawkesbury Rd, Westmead NSW 2145, Australia
| | - Roc Reguant
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Level 3/160 Hawkesbury Rd, Westmead NSW 2145, Australia
| | - Letitia M F Sng
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Level 3/160 Hawkesbury Rd, Westmead NSW 2145, Australia
| | - Natalie A Twine
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Level 3/160 Hawkesbury Rd, Westmead NSW 2145, Australia
| | - Yuwan Malakar
- Responsible Innovation Future Science Platform, Commonwealth Scientific and Industrial Research Organisation, Brisbane, 41 Boggo Rd, Dutton Park QLD 4102, Australia
| | - Anuradha Wickramarachchi
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Level 3/160 Hawkesbury Rd, Westmead NSW 2145, Australia
| | - Marcel Keller
- Data61, Commonwealth Scientific and Industrial Research Organisation, Level 5/13 Garden St, Eveleigh NSW 2015, Australia
| | - Thilina Ranbaduge
- Data61, Commonwealth Scientific and Industrial Research Organisation, Building 101, Clunies Ross St, Black Mountain, Canberra, ACT 2601, Australia
| | - Eva K F Chan
- NSW Health Pathology, Sydney, 1 Reserve Road, St Leonards NSW 2065, Australia
| | - James Breen
- Telethon Kids Institute, Perth, WA 6009, Australia
- National Centre for Indigenous Genomics, The John Curtin School of Medical Research, Australian National University, Canberra, ACT 2601, Australia
| | - Sam Buckberry
- Telethon Kids Institute, Perth, WA 6009, Australia
- National Centre for Indigenous Genomics, The John Curtin School of Medical Research, Australian National University, Canberra, ACT 2601, Australia
| | - Boris Guennewig
- Sydney Medical School, Brain and Mind Centre, The University of Sydney, Sydney, 94 Mallett St, Camperdown NSW 2050, Australia
| | - Matilda Haas
- Australian Genomics, Parkville, VIC 3052, Australia
- Murdoch Children’s Research Institute, Parkville, Victoria 3052, Australia
| | - Alex Brown
- Telethon Kids Institute, Perth, WA 6009, Australia
- National Centre for Indigenous Genomics, The John Curtin School of Medical Research, Australian National University, Canberra, ACT 2601, Australia
| | - Mark J Cowley
- Children’s Cancer Institute, Lowy Cancer Research Centre, Level 4, Lowy Cancer Research Centre Corner Botany & High Streets UNSW Kensington Campus UNSW Sydney, Kensington NSW 2052, Australia
- School of Clinical Medicine, UNSW Medicine & Health, Wallace Wurth Building (C27), Cnr High St & Botany St, UNSW Sydney, Kensington NSW 2052, Australia
| | - Natalie Thorne
- University of Melbourne, Melbourne, Parkville VIC 3052, Australia
- Melbourne Genomics Health Alliance, Melbourne 1G, Walter and Eliza Hall Institute/1G Royal Parade, Parkville VIC 3052, Australia
- Walter and Eliza Hall Institute, Melbourne, 1G, Walter and Eliza Hall Institute/1G Royal Parade, Parkville VIC 3052, Australia
| | - Yatish Jain
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Level 3/160 Hawkesbury Rd, Westmead NSW 2145, Australia
- Applied BioSciences, Faculty of Science and Engineering, Macquarie University, Applied BioSciences 205B Culloden Rd Macquarie University, NSW 2109, Australia
| | - Denis C Bauer
- Applied BioSciences, Faculty of Science and Engineering, Macquarie University, Applied BioSciences 205B Culloden Rd Macquarie University, NSW 2109, Australia
- Department of Biomedical Sciences, MQ Health General Practice - Macquarie University, Suite 305, Level 3/2 Technology Pl, Macquarie Park NSW 2109, Australia
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Gate 13, Kintore Avenue University of Adelaide, Adelaide SA 5000, Australia
| |
Collapse
|
6
|
Gierend K, Waltemath D, Ganslandt T, Siegel F. Traceable Research Data Sharing in a German Medical Data Integration Center With FAIR (Findability, Accessibility, Interoperability, and Reusability)-Geared Provenance Implementation: Proof-of-Concept Study. JMIR Form Res 2023; 7:e50027. [PMID: 38060305 PMCID: PMC10739241 DOI: 10.2196/50027] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 10/25/2023] [Accepted: 11/01/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND Secondary investigations into digital health records, including electronic patient data from German medical data integration centers (DICs), pave the way for enhanced future patient care. However, only limited information is captured regarding the integrity, traceability, and quality of the (sensitive) data elements. This lack of detail diminishes trust in the validity of the collected data. From a technical standpoint, adhering to the widely accepted FAIR (Findability, Accessibility, Interoperability, and Reusability) principles for data stewardship necessitates enriching data with provenance-related metadata. Provenance offers insights into the readiness for the reuse of a data element and serves as a supplier of data governance. OBJECTIVE The primary goal of this study is to augment the reusability of clinical routine data within a medical DIC for secondary utilization in clinical research. Our aim is to establish provenance traces that underpin the status of data integrity, reliability, and consequently, trust in electronic health records, thereby enhancing the accountability of the medical DIC. We present the implementation of a proof-of-concept provenance library integrating international standards as an initial step. METHODS We adhered to a customized road map for a provenance framework, and examined the data integration steps across the ETL (extract, transform, and load) phases. Following a maturity model, we derived requirements for a provenance library. Using this research approach, we formulated a provenance model with associated metadata and implemented a proof-of-concept provenance class. Furthermore, we seamlessly incorporated the internationally recognized Word Wide Web Consortium (W3C) provenance standard, aligned the resultant provenance records with the interoperable health care standard Fast Healthcare Interoperability Resources, and presented them in various representation formats. Ultimately, we conducted a thorough assessment of provenance trace measurements. RESULTS This study marks the inaugural implementation of integrated provenance traces at the data element level within a German medical DIC. We devised and executed a practical method that synergizes the robustness of quality- and health standard-guided (meta)data management practices. Our measurements indicate commendable pipeline execution times, attaining notable levels of accuracy and reliability in processing clinical routine data, thereby ensuring accountability in the medical DIC. These findings should inspire the development of additional tools aimed at providing evidence-based and reliable electronic health record services for secondary use. CONCLUSIONS The research method outlined for the proof-of-concept provenance class has been crafted to promote effective and reliable core data management practices. It aims to enhance biomedical data by imbuing it with meaningful provenance, thereby bolstering the benefits for both research and society. Additionally, it facilitates the streamlined reuse of biomedical data. As a result, the system mitigates risks, as data analysis without knowledge of the origin and quality of all data elements is rendered futile. While the approach was initially developed for the medical DIC use case, these principles can be universally applied throughout the scientific domain.
Collapse
Affiliation(s)
- Kerstin Gierend
- Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Dagmar Waltemath
- Core Unit Data Integration Center and Medical Informatics Laboratory, University Medicine Greifswald, Greifswald, Germany
| | - Thomas Ganslandt
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Fabian Siegel
- Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| |
Collapse
|
7
|
Kamdje Wabo G, Prasser F, Gierend K, Siegel F, Ganslandt T. Data Quality- and Utility-Compliant Anonymization of Common Data Model-Harmonized Electronic Health Record Data: Protocol for a Scoping Review. JMIR Res Protoc 2023; 12:e46471. [PMID: 37566443 PMCID: PMC10457704 DOI: 10.2196/46471] [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: 02/13/2023] [Revised: 05/31/2023] [Accepted: 06/28/2023] [Indexed: 08/12/2023] Open
Abstract
BACKGROUND The anonymization of Common Data Model (CDM)-converted EHR data is essential to ensure the data privacy in the use of harmonized health care data. However, applying data anonymization techniques can significantly affect many properties of the resulting data sets and thus biases research results. Few studies have reviewed these applications with a reflection of approaches to manage data utility and quality concerns in the context of CDM-formatted health care data. OBJECTIVE Our intended scoping review aims to identify and describe (1) how formal anonymization methods are carried out with CDM-converted health care data, (2) how data quality and utility concerns are considered, and (3) how the various CDMs differ in terms of their suitability for recording anonymized data. METHODS The planned scoping review is based on the framework of Arksey and O'Malley. By using this, only articles published in English will be included. The retrieval of literature items should be based on a literature search string combining keywords related to data anonymization, CDM standards, and data quality assessment. The proposed literature search query should be validated by a librarian, accompanied by manual searches to include further informal sources. Eligible articles will first undergo a deduplication step, followed by the screening of titles. Second, a full-text reading will allow the 2 reviewers involved to reach the final decision about article selection, while a domain expert will support the resolution of citation selection conflicts. Additionally, key information will be extracted, categorized, summarized, and analyzed by using a proposed template into an iterative process. Tabular and graphical analyses should be addressed in alignment with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. We also performed some tentative searches on Web of Science for estimating the feasibility of reaching eligible articles. RESULTS Tentative searches on Web of Science resulted in 507 nonduplicated matches, suggesting the availability of (potential) relevant articles. Further analysis and selection steps will allow us to derive a final literature set. Furthermore, the completion of this scoping review study is expected by the end of the fourth quarter of 2023. CONCLUSIONS Outlining the approaches of applying formal anonymization methods on CDM-formatted health care data while taking into account data quality and utility concerns should provide useful insights to understand the existing approaches and future research direction based on identified gaps. This protocol describes a schedule to perform a scoping review, which should support the conduction of follow-up investigations. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/46471.
Collapse
Affiliation(s)
- Gaetan Kamdje Wabo
- Department of Biomedical Informatics, Center for Preventive Medicine and Digital Health Baden-Württemberg, Mannheim Medical Faculty of the University of Heidelberg, Mannheim, Germany
| | - Fabian Prasser
- Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Kerstin Gierend
- Department of Biomedical Informatics, Center for Preventive Medicine and Digital Health Baden-Württemberg, Mannheim Medical Faculty of the University of Heidelberg, Mannheim, Germany
| | - Fabian Siegel
- Department of Biomedical Informatics, Center for Preventive Medicine and Digital Health Baden-Württemberg, Mannheim Medical Faculty of the University of Heidelberg, Mannheim, Germany
- Department of Urology and Urosurgery, University Medical Center Mannheim, Mannheim Medical Faculty of the University of Heidelberg, Mannheim, Germany
| | - Thomas Ganslandt
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| |
Collapse
|
8
|
Danis D, Jacobsen JOB, Wagner AH, Groza T, Beckwith MA, Rekerle L, Carmody LC, Reese J, Hegde H, Ladewig MS, Seitz B, Munoz-Torres M, Harris NL, Rambla J, Baudis M, Mungall CJ, Haendel MA, Robinson PN. Phenopacket-tools: Building and validating GA4GH Phenopackets. PLoS One 2023; 18:e0285433. [PMID: 37196000 PMCID: PMC10191354 DOI: 10.1371/journal.pone.0285433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 04/21/2023] [Indexed: 05/19/2023] Open
Abstract
The Global Alliance for Genomics and Health (GA4GH) is a standards-setting organization that is developing a suite of coordinated standards for genomics. The GA4GH Phenopacket Schema is a standard for sharing disease and phenotype information that characterizes an individual person or biosample. The Phenopacket Schema is flexible and can represent clinical data for any kind of human disease including rare disease, complex disease, and cancer. It also allows consortia or databases to apply additional constraints to ensure uniform data collection for specific goals. We present phenopacket-tools, an open-source Java library and command-line application for construction, conversion, and validation of phenopackets. Phenopacket-tools simplifies construction of phenopackets by providing concise builders, programmatic shortcuts, and predefined building blocks (ontology classes) for concepts such as anatomical organs, age of onset, biospecimen type, and clinical modifiers. Phenopacket-tools can be used to validate the syntax and semantics of phenopackets as well as to assess adherence to additional user-defined requirements. The documentation includes examples showing how to use the Java library and the command-line tool to create and validate phenopackets. We demonstrate how to create, convert, and validate phenopackets using the library or the command-line application. Source code, API documentation, comprehensive user guide and a tutorial can be found at https://github.com/phenopackets/phenopacket-tools. The library can be installed from the public Maven Central artifact repository and the application is available as a standalone archive. The phenopacket-tools library helps developers implement and standardize the collection and exchange of phenotypic and other clinical data for use in phenotype-driven genomic diagnostics, translational research, and precision medicine applications.
Collapse
Affiliation(s)
- Daniel Danis
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
| | - Julius O. B. Jacobsen
- William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Alex H. Wagner
- Departments of Pediatrics and Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH, United States of America
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH, United States of America
| | | | - Martha A. Beckwith
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
| | - Lauren Rekerle
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
| | - Leigh C. Carmody
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
| | - Justin Reese
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - Harshad Hegde
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - Markus S. Ladewig
- Department of Ophthalmology, Klinikum Saarbrücken, Saarbrücken, Germany
| | - Berthold Seitz
- Department of Ophthalmology, Saarland University Medical Center, Homburg/Saar, Germany
| | - Monica Munoz-Torres
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Nomi L. Harris
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - Jordi Rambla
- European Genome-Phenome Archive (EGA) in the Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Michael Baudis
- University of Zurich and Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Christopher J. Mungall
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - Melissa A. Haendel
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Peter N. Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
- Institute for Systems Genomics, University of Connecticut, Farmington, CT, United States of America
| |
Collapse
|