Incentivizing the Sharing of Healthcare Data in the AI Era
Open accessAbstract
This article contributes to the policy dialogue about how to govern healthcare data in the AI era and how to incentivize patients to share their data. Existing approaches to data-sharing restrict the flow of data. Yet, as healthcare AI technologies rely on data in enhancing their scope, such lack of data hinders the creation of future applications and diminishes the need for data to furnish them. We shift attention to a GDPR based policy that does not restrict data flows and argue that the existing experience in monetizing digitalized copyright material such as music can offer a practical and well tested solution.
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The risk of privacy infringement due to misuse of pseudo-anonymized patient data is a primary concern.
“The risk of privacy infringement due to the misuse of pseudo-anonymized patient data [7,8] is a primary concern.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [8]
Sophisticated techniques can potentially re-engineer anonymized data to identify individuals, thus breaching patient confidentiality.
“Sophisticated techniques can potentially re-engineer anonymized data to identify individuals, thus breaching patient confidentiality.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [8]
Including diverse datasets in AI model training is essential for minimizing biases and promoting equitable health outcomes.
“Including diverse datasets in AI model training is essential for minimizing biases and promoting equitable health outcomes [8,22].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [8]
Data integrity is essential for ensuring reliable healthcare outcomes.
“Additionally, the authors of [7,8] emphasize the essential role of data integrity in ensuring reliable healthcare outcomes.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [8]
Existing experiences in monetizing digitalized content, such as music, offer a practical solution to incentivize patients to share their data without restricting data flows.
“They suggest that existing experiences in monetizing digitalized content, such as music, offer a practical solution to incentivize patients to share their data without restricting data flows.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [8]
Transaction costs associated with data trade often exceed the value of the exchange itself, making negotiation and licensing of data unprofitable.
“However, transaction costs associated with data trade often exceed the value of the exchange itself, making the negotiation and licensing of data unprofitable.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [8]
Restrictions on data flow could hinder the development of future AI applications and diminish the need for data to train them.
“Such restrictions could hinder the development of future AI applications and diminish the need for data to train them.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [8]
Initiatives like Health Data Hub, SOLID, and DECODE aim to control data flow and induce artificial scarcity.
“The article outlines various initiatives, like the Health Data Hub, SOLID, and DECODE, which aim to control data flow and induce artificial scarcity.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [8]
AI is 'data-hungry', so the artificial scarcity approach may not apply to AI.
“However, this may not apply to AI, as AI is 'data-hungry'.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [8]
CDM, as proposed by [8], offers a pathway to balance the need for data to fuel AI development with the rights of individuals to control and benefit from their data.
“The concept of CDM, as proposed by [8], offers a pathway to balance the need for data to fuel AI development with the rights of individuals to control and benefit from their data.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [8]
Less data lead to fewer AI applications, thus limiting the demand for data to train and furnish these applications.
“Less data lead to fewer AI applications, thus limiting the demand for data to train and furnish these applications [8].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [8]
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- Authors
- Panagopoulos, A., Minssen, T., Sideri, K., Yu, H., & Compagnucci, M. C.
- Affiliations
- University of Crete (GR)
- University of Copenhagen (DK)
- Panteion University (GR)
- Corresponding author
- Panagopoulos, Andreas
- Venue
- Computer Law & Security Review
- Publisher
- Elsevier BV
- Published
- 2022-07
- Volume
- 45
- Pages
- 105670
- ISSN
- 2212-473X (ISSN-L)
- OpenAlex
- W4225377292
- Cited by
- 39
- References
- 62
- Citations by year
- 2026: 1 · 2025: 13 · 2024: 19 · 2023: 5 · 2022: 1
- FWCI
- 58.62
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computer law & security review (publishedVersion, cc-by)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by-nc-nd)
- Cronfa (Swansea University) (submittedVersion, cc-by)
- Funders
- Novo Nordisk Fonden (NNF17SA0027784, AI DLV-794604)
- Universiteit Gent
- Novo Nordisk
- Topics
- Social Sciences › Law › Digitalization, Law, and Regulation
- Physical Sciences › Computer Science › Information Systems › Thoreau and American Literature
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- health data sharing, artificial intelligence in healthcare, data governance, GDPR, data monetization, copyright licensing, patient information, data flow
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