Case report: evaluation of an open-source synthetic data platform for simulation studies

Meeker, Daniella; Kallem, Crystal; Heras, Yan; Garcia, Stéphanie; Thompson, Casey

doi:10.1093/jamiaopen/ooac067

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…Recently, the EMA endorsed a joint statement calling for international collaboration to enable the generation and use of RWE for regulatory decision making by the ICMRA 3 . As synthetic data are nonidentifiable and may be more cost‐effective, utilization of synthetic data compared with that of RWD could significantly bolster RWE generation where privacy or cost concerns precluded their use in the past 38,39 . In addition to minimizing privacy concerns, synthetic data have the ability to augment the sample size of an original dataset.…”

Section: Overview Of Synthetic Data Methodsmentioning

confidence: 99%

“…3 As synthetic data are nonidentifiable and may be more cost-effective, utilization of synthetic data compared with that of RWD could significantly bolster RWE generation where privacy or cost concerns precluded their use in the past. 38,39 In addition to minimizing privacy concerns, synthetic data have the ability to augment the sample size of an original dataset. Synthetic data from datasets originating from other countries can complement the original dataset, and therefore, the newly generated dataset will be able to capture different populations.…”

Section: Current Use Of Synthetic Data and Ongoing Initiatives In The...mentioning

confidence: 99%

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A Case for Synthetic Data in Regulatory Decision‐Making in Europe

Alloza,

Knox,

Raad

et al. 2023

Clin Pharma and Therapeutics

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Regulators are faced with many challenges surrounding health data usage, including privacy, fragmentation, validity, and generalizability, especially in the European Union (EU), for which synthetic data may provide innovative solutions. Synthetic data, defined as data artificially generated rather than captured in the real world, are increasingly being used for healthcare research purposes as a proxy to real‐world data (RWD). Currently, there are barriers particularly challenging in Europe, where sharing patient's data is strictly regulated, costly, and time consuming, causing delays in evidence generation and regulatory approvals. Recent initiatives are encouraging the use of synthetic data in regulatory decision‐making and health technology assessment to overcome these challenges, but synthetic data have still to overcome realistic obstacles before their adoption by researchers and regulators in Europe. Thus, the emerging use of RWD and synthetic data by pharmaceutical and medical device industries calls regulatory bodies to provide a framework for proper evidence generation and informed regulatory decision‐making. As the provision of data becomes more ubiquitous in scientific research, so will innovations in artificial intelligence, machine learning, and generation of synthetic data, making the exploration and intricacies of this topic all the more important and timely.In this review, we discuss the potential merits and challenges of synthetic data in the context of decision‐making in the European regulatory environment. We explore the current uses of synthetic data and ongoing initiatives, the value of synthetic data for regulatory purposes, and realistic barriers to the adoption of synthetic data in healthcare.

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Section: Overview Of Synthetic Data Methodsmentioning

confidence: 99%

Section: Current Use Of Synthetic Data and Ongoing Initiatives In The...mentioning

confidence: 99%

A Case for Synthetic Data in Regulatory Decision‐Making in Europe

Alloza,

Knox,

Raad

et al. 2023

Clin Pharma and Therapeutics

View full text Add to dashboard Cite

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A framework for sharing of clinical and genetic data for precision medicine applications

Elhussein,

Baymuradov,

Phatnani

et al. 2024

Nat Med

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Precision medicine has the potential to provide more accurate diagnosis, appropriate treatment and timely prevention strategies by considering patients’ biological makeup. However, this cannot be realized without integrating clinical and omics data in a data-sharing framework that achieves large sample sizes. Systems that integrate clinical and genetic data from multiple sources are scarce due to their distinct data types, interoperability, security and data ownership issues. Here we present a secure framework that allows immutable storage, querying and analysis of clinical and genetic data using blockchain technology. Our platform allows clinical and genetic data to be harmonized by combining them under a unified framework. It supports combined genotype–phenotype queries and analysis, gives institutions control of their data and provides immutable user access logs, improving transparency into how and when health information is used. We demonstrate the value of our framework for precision medicine by creating genotype–phenotype cohorts and examining relationships within them. We show that combining data across institutions using our secure platform increases statistical power for rare disease analysis. By offering an integrated, secure and decentralized framework, we aim to enhance reproducibility and encourage broader participation from communities and patients in data sharing.

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MIMIC-IV on FHIR: converting a decade of in-patient data into an exchangeable, interoperable format

Bennett

Ulrich

Damme

et al. 2023

Journal of the American Medical Informatics Association

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Objective Convert the Medical Information Mart for Intensive Care (MIMIC)-IV database into Health Level 7 Fast Healthcare Interoperability Resources (FHIR). Additionally, generate and publish an openly available demo of the resources, and create a FHIR Implementation Guide to support and clarify the usage of MIMIC-IV on FHIR. Materials and Methods FHIR profiles and terminology system of MIMIC-IV were modeled from the base FHIR R4 resources. Data and terminology were reorganized from the relational structure into FHIR according to the profiles. Resources generated were validated for conformance with the FHIR profiles. Finally, FHIR resources were published as newline delimited JSON files and the profiles were packaged into an implementation guide. Results The modeling of MIMIC-IV in FHIR resulted in 25 profiles, 2 extensions, 35 ValueSets, and 34 CodeSystems. An implementation guide encompassing the FHIR modeling can be accessed at mimic.mit.edu/fhir/mimic. The generated demo dataset contained 100 patients and over 915 000 resources. The full dataset contained 315 000 patients covering approximately 5 840 000 resources. The final datasets in NDJSON format are accessible on PhysioNet. Discussion Our work highlights the challenges and benefits of generating a real-world FHIR store. The challenges arise from terminology mapping and profiling modeling decisions. The benefits come from the extensively validated openly accessible data created as a result of the modeling work. Conclusion The newly created MIMIC-IV on FHIR provides one of the first accessible deidentified critical care FHIR datasets. The extensive real-world data found in MIMIC-IV on FHIR will be invaluable for research and the development of healthcare applications.

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Case report: evaluation of an open-source synthetic data platform for simulation studies

Cited by 6 publications

References 9 publications

A Case for Synthetic Data in Regulatory Decision‐Making in Europe

A Case for Synthetic Data in Regulatory Decision‐Making in Europe

A framework for sharing of clinical and genetic data for precision medicine applications

MIMIC-IV on FHIR: converting a decade of in-patient data into an exchangeable, interoperable format

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