The ethics of data mining in healthcare: challenges, frameworks, and future directions
Abstract Data mining in healthcare offers transformative insights yet surfaces multilayered ethical and governance challenges that extend beyond privacy alone. Privacy and consent concerns remain paramount when handling sensitive medical data, particularly as healthcare organizations increasingly sh...
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| Format: | Article |
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BMC
2025-07-01
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| Series: | BioData Mining |
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| Online Access: | https://doi.org/10.1186/s13040-025-00461-w |
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| author | Mohamed Mustaf Ahmed Olalekan John Okesanya Majd Oweidat Zhinya Kawa Othman Shuaibu Saidu Musa Don Eliseo Lucero-Prisno III |
| author_facet | Mohamed Mustaf Ahmed Olalekan John Okesanya Majd Oweidat Zhinya Kawa Othman Shuaibu Saidu Musa Don Eliseo Lucero-Prisno III |
| author_sort | Mohamed Mustaf Ahmed |
| collection | DOAJ |
| description | Abstract Data mining in healthcare offers transformative insights yet surfaces multilayered ethical and governance challenges that extend beyond privacy alone. Privacy and consent concerns remain paramount when handling sensitive medical data, particularly as healthcare organizations increasingly share patient information with large digital platforms. The risks of data breaches and unauthorized access are stark: 725 reportable incidents in 2023 alone exposed more than 133 million patient records, and hacking-related breaches surged by 239% since 2018. Algorithmic bias further threatens equity; models trained on historically prejudiced data can reinforce health disparities across protected groups. Therefore, transparency must span three levels–dataset documentation, model interpretability, and post-deployment audit logging–to make algorithmic reasoning and failures traceable. Security vulnerabilities in the Internet of Medical Things (IoMT) and cloud-based health platforms amplify these risks, while corporate data-sharing deals complicate questions of data ownership and patient autonomy. A comprehensive response requires (i) dataset-level artifacts such as “datasheets,” (ii) model-cards that disclose fairness metrics, and (iii) continuous logging of predictions and LIME/SHAP explanations for independent audits. Technical safeguards must blend differential privacy (with empirically validated noise budgets), homomorphic encryption for high-value queries, and federated learning to maintain the locality of raw data. Governance frameworks must also mandate routine bias and robust audits and harmonized penalties for non-compliance. Regular reassessments, thorough documentation, and active engagement with clinicians, patients, and regulators are critical to accountability. This paper synthesizes current evidence, from a 2019 European re-identification study demonstrating 99.98% uniqueness with 15 quasi-identifiers to recent clinical audits that trimmed false-negative rates via threshold recalibration, and proposes an integrated set of fairness, privacy, and security controls aligned with SPIRIT-AI, CONSORT-AI, and emerging PROBAST-AI guidelines. Implementing these solutions will help healthcare systems harness the benefits of data mining while safeguarding patient rights and sustaining public trust. |
| format | Article |
| id | doaj-art-2708c0b4ea2748d294b6ac774fada744 |
| institution | Kabale University |
| issn | 1756-0381 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BioData Mining |
| spelling | doaj-art-2708c0b4ea2748d294b6ac774fada7442025-08-20T04:01:53ZengBMCBioData Mining1756-03812025-07-0118111610.1186/s13040-025-00461-wThe ethics of data mining in healthcare: challenges, frameworks, and future directionsMohamed Mustaf Ahmed0Olalekan John Okesanya1Majd Oweidat2Zhinya Kawa Othman3Shuaibu Saidu Musa4Don Eliseo Lucero-Prisno III5Faculty of Medicine and Health Sciences, SIMAD UniversityDepartment of Public Health and Maritime Transport, University of ThessalyCollege of Medicine, Hebron UniversityDepartment of Pharmacy, Kurdistan Technical InstituteSchool of Global Health, Faculty of Medicine, Chulalongkorn UniversityDepartment of Global Health and Development, London School of Hygiene and Tropical MedicineAbstract Data mining in healthcare offers transformative insights yet surfaces multilayered ethical and governance challenges that extend beyond privacy alone. Privacy and consent concerns remain paramount when handling sensitive medical data, particularly as healthcare organizations increasingly share patient information with large digital platforms. The risks of data breaches and unauthorized access are stark: 725 reportable incidents in 2023 alone exposed more than 133 million patient records, and hacking-related breaches surged by 239% since 2018. Algorithmic bias further threatens equity; models trained on historically prejudiced data can reinforce health disparities across protected groups. Therefore, transparency must span three levels–dataset documentation, model interpretability, and post-deployment audit logging–to make algorithmic reasoning and failures traceable. Security vulnerabilities in the Internet of Medical Things (IoMT) and cloud-based health platforms amplify these risks, while corporate data-sharing deals complicate questions of data ownership and patient autonomy. A comprehensive response requires (i) dataset-level artifacts such as “datasheets,” (ii) model-cards that disclose fairness metrics, and (iii) continuous logging of predictions and LIME/SHAP explanations for independent audits. Technical safeguards must blend differential privacy (with empirically validated noise budgets), homomorphic encryption for high-value queries, and federated learning to maintain the locality of raw data. Governance frameworks must also mandate routine bias and robust audits and harmonized penalties for non-compliance. Regular reassessments, thorough documentation, and active engagement with clinicians, patients, and regulators are critical to accountability. This paper synthesizes current evidence, from a 2019 European re-identification study demonstrating 99.98% uniqueness with 15 quasi-identifiers to recent clinical audits that trimmed false-negative rates via threshold recalibration, and proposes an integrated set of fairness, privacy, and security controls aligned with SPIRIT-AI, CONSORT-AI, and emerging PROBAST-AI guidelines. Implementing these solutions will help healthcare systems harness the benefits of data mining while safeguarding patient rights and sustaining public trust.https://doi.org/10.1186/s13040-025-00461-wData miningHealthcare ethicsPrivacyAlgorithmic biasData securityPatient consent |
| spellingShingle | Mohamed Mustaf Ahmed Olalekan John Okesanya Majd Oweidat Zhinya Kawa Othman Shuaibu Saidu Musa Don Eliseo Lucero-Prisno III The ethics of data mining in healthcare: challenges, frameworks, and future directions BioData Mining Data mining Healthcare ethics Privacy Algorithmic bias Data security Patient consent |
| title | The ethics of data mining in healthcare: challenges, frameworks, and future directions |
| title_full | The ethics of data mining in healthcare: challenges, frameworks, and future directions |
| title_fullStr | The ethics of data mining in healthcare: challenges, frameworks, and future directions |
| title_full_unstemmed | The ethics of data mining in healthcare: challenges, frameworks, and future directions |
| title_short | The ethics of data mining in healthcare: challenges, frameworks, and future directions |
| title_sort | ethics of data mining in healthcare challenges frameworks and future directions |
| topic | Data mining Healthcare ethics Privacy Algorithmic bias Data security Patient consent |
| url | https://doi.org/10.1186/s13040-025-00461-w |
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