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Peer-reviewedCC BY-NC 4.0The European Journal of Public Health

Public acceptance of anonymized medical data reuse for research in data-driven healthcare

Abstract

Background: With the European Health Data Space (EHDS) implementation, secondary use of health data for research is becoming central to data-driven public health. Its legitimacy depends not only on legal safeguards, but also on public acceptance. This study aimed to assess personal acceptance of making one’s anonymized medical data available for research and to identify associated factors.

Methods: We analyzed data from a nationwide computer-assisted telephone interviewing (CATI) survey of Polish residents aged 18–75 years (N = 1,540), using a quota-controlled sample. The outcome was agreement that one’s anonymized medical data should be available for research, dichotomized as agreement versus no clear agreement. Predictors included sociodemographic and health variables, healthcare use, digital technology use, and attitudes toward IT-based healthcare services and AI in medicine. Logistic regression was used; results are reported as ORs with 95%CI. Multicollinearity was assessed using VIF. The study was funded by the National Science Centre, Poland (Grant No UMO-2021/43/B/NZ7/02140).

Results: Overall, 628 respondents (40.8%) agreed to make their anonymized medical data available for research. The model was significant (χ²(32)=461.87, p < 0.001; Nagelkerke R²=0.35). Acceptance was strongly associated with agreement that AI in medicine is beneficial for patients (OR = 6.60, 95%CI: 4.52–9.62) and that IT-based healthcare services bring more benefits than harms (OR = 2.78, 95%CI: 1.85–4.16). Higher income categories had higher odds than the lowest income group (ORs=1.52–1.85). Women had lower odds than men (OR = 0.75, 95%CI: 0.58–0.97), as did respondents reporting an emergency care episode in the previous year (OR = 0.71, 95%CI: 0.52–0.96).

Conclusions: Acceptance was moderate and mainly linked to favorable attitudes toward AI and IT-based healthcare services. Data governance should go beyond compliance by explaining data protection, access rules, and the public value of reuse.

Key messages: • Support for anonymized medical data reuse was moderate, with 40.8% accepting use of their data for research.

• Acceptance was driven mainly by positive attitudes toward AI and IT-based healthcare, underscoring the role of trust-building communication.

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BibTeX
@article{duplaga2026public,
  title = {{Public acceptance of anonymized medical data reuse for research in data-driven healthcare}},
  author = {M Duplaga and P Smoła and T Sikorski},
  journal = {The European Journal of Public Health},
  year = {2026},
  month = oct,
  doi = {10.1093/eurpub/ckag169.887},
  url = {https://doi.org/10.1093/eurpub/ckag169.887},
  note = {CC BY-NC 4.0},
}
RIS
TY  - JOUR
TI  - Public acceptance of anonymized medical data reuse for research in data-driven healthcare
AU  - M Duplaga
AU  - P Smoła
AU  - T Sikorski
PY  - 2026
DA  - 2026/10/01
JO  - The European Journal of Public Health
DO  - 10.1093/eurpub/ckag169.887
UR  - https://doi.org/10.1093/eurpub/ckag169.887
AB  - Background: With the European Health Data Space (EHDS) implementation, secondary use of health data for research is becoming central to data-driven public health. Its legitimacy depends not only on legal safeguards, but also on public acceptance. This study aimed to assess personal acceptance of making one’s anonymized medical data available for research and to identify associated factors.  Methods: We analyzed data from a nationwide computer-assisted telephone interviewing (CATI) survey of Polish residents aged 18–75 years (N = 1,540), using a quota-controlled sample. The outcome was agreement that one’s anonymized medical data should be available for research, dichotomized as agreement versus no clear agreement. Predictors included sociodemographic and health variables, healthcare use, digital technology use, and attitudes toward IT-based healthcare services and AI in medicine. Logistic regression was used; results are reported as ORs with 95%CI. Multicollinearity was assessed using VIF. The study was funded by the National Science Centre, Poland (Grant No UMO-2021/43/B/NZ7/02140).  Results: Overall, 628 respondents (40.8%) agreed to make their anonymized medical data available for research. The model was significant (χ²(32)=461.87, p < 0.001; Nagelkerke R²=0.35). Acceptance was strongly associated with agreement that AI in medicine is beneficial for patients (OR = 6.60, 95%CI: 4.52–9.62) and that IT-based healthcare services bring more benefits than harms (OR = 2.78, 95%CI: 1.85–4.16). Higher income categories had higher odds than the lowest income group (ORs=1.52–1.85). Women had lower odds than men (OR = 0.75, 95%CI: 0.58–0.97), as did respondents reporting an emergency care episode in the previous year (OR = 0.71, 95%CI: 0.52–0.96).  Conclusions: Acceptance was moderate and mainly linked to favorable attitudes toward AI and IT-based healthcare services. Data governance should go beyond compliance by explaining data protection, access rules, and the public value of reuse.  Key messages: • Support for anonymized medical data reuse was moderate, with 40.8% accepting use of their data for research.  • Acceptance was driven mainly by positive attitudes toward AI and IT-based healthcare, underscoring the role of trust-building communication.
SN  - 1101-1262
SN  - 1464-360X
ER  -