Harnessing the power of synthetic data in healthcare: innovation, application, and privacy
Abstract Data-driven decision-making in modern healthcare underpins innovation and predictive analytics in public health and clinical research. Synthetic data has shown promise in finance and economics to improve risk assessment, portfolio optimization, and algorithmic trading. However, higher stake...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-10-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-023-00927-3 |
| _version_ | 1797451311780724736 |
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| author | Mauro Giuffrè Dennis L. Shung |
| author_facet | Mauro Giuffrè Dennis L. Shung |
| author_sort | Mauro Giuffrè |
| collection | DOAJ |
| description | Abstract Data-driven decision-making in modern healthcare underpins innovation and predictive analytics in public health and clinical research. Synthetic data has shown promise in finance and economics to improve risk assessment, portfolio optimization, and algorithmic trading. However, higher stakes, potential liabilities, and healthcare practitioner distrust make clinical use of synthetic data difficult. This paper explores the potential benefits and limitations of synthetic data in the healthcare analytics context. We begin with real-world healthcare applications of synthetic data that informs government policy, enhance data privacy, and augment datasets for predictive analytics. We then preview future applications of synthetic data in the emergent field of digital twin technology. We explore the issues of data quality and data bias in synthetic data, which can limit applicability across different applications in the clinical context, and privacy concerns stemming from data misuse and risk of re-identification. Finally, we evaluate the role of regulatory agencies in promoting transparency and accountability and propose strategies for risk mitigation such as Differential Privacy (DP) and a dataset chain of custody to maintain data integrity, traceability, and accountability. Synthetic data can improve healthcare, but measures to protect patient well-being and maintain ethical standards are key to promote responsible use. |
| first_indexed | 2024-03-09T14:52:51Z |
| format | Article |
| id | doaj.art-06c39bdc05b648ef9c134bb73bdb91cb |
| institution | Directory Open Access Journal |
| issn | 2398-6352 |
| language | English |
| last_indexed | 2024-03-09T14:52:51Z |
| publishDate | 2023-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj.art-06c39bdc05b648ef9c134bb73bdb91cb2023-11-26T14:19:46ZengNature Portfolionpj Digital Medicine2398-63522023-10-01611810.1038/s41746-023-00927-3Harnessing the power of synthetic data in healthcare: innovation, application, and privacyMauro Giuffrè0Dennis L. Shung1Department of Medicine (Digestive Diseases), Yale School of Medicine, Yale UniversityDepartment of Medicine (Digestive Diseases), Yale School of Medicine, Yale UniversityAbstract Data-driven decision-making in modern healthcare underpins innovation and predictive analytics in public health and clinical research. Synthetic data has shown promise in finance and economics to improve risk assessment, portfolio optimization, and algorithmic trading. However, higher stakes, potential liabilities, and healthcare practitioner distrust make clinical use of synthetic data difficult. This paper explores the potential benefits and limitations of synthetic data in the healthcare analytics context. We begin with real-world healthcare applications of synthetic data that informs government policy, enhance data privacy, and augment datasets for predictive analytics. We then preview future applications of synthetic data in the emergent field of digital twin technology. We explore the issues of data quality and data bias in synthetic data, which can limit applicability across different applications in the clinical context, and privacy concerns stemming from data misuse and risk of re-identification. Finally, we evaluate the role of regulatory agencies in promoting transparency and accountability and propose strategies for risk mitigation such as Differential Privacy (DP) and a dataset chain of custody to maintain data integrity, traceability, and accountability. Synthetic data can improve healthcare, but measures to protect patient well-being and maintain ethical standards are key to promote responsible use.https://doi.org/10.1038/s41746-023-00927-3 |
| spellingShingle | Mauro Giuffrè Dennis L. Shung Harnessing the power of synthetic data in healthcare: innovation, application, and privacy npj Digital Medicine |
| title | Harnessing the power of synthetic data in healthcare: innovation, application, and privacy |
| title_full | Harnessing the power of synthetic data in healthcare: innovation, application, and privacy |
| title_fullStr | Harnessing the power of synthetic data in healthcare: innovation, application, and privacy |
| title_full_unstemmed | Harnessing the power of synthetic data in healthcare: innovation, application, and privacy |
| title_short | Harnessing the power of synthetic data in healthcare: innovation, application, and privacy |
| title_sort | harnessing the power of synthetic data in healthcare innovation application and privacy |
| url | https://doi.org/10.1038/s41746-023-00927-3 |
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