Impact of the European General Data Protection Regulation (GDPR) on Health Data Management in a European Union Candidate Country: A Case Study of Serbia
Bibliographic record
Abstract
As of May 2018, all relevant institutions within member countries of the European Economic Area are required to comply with the European General Data Protection Regulation (GDPR) or face significant fines. This regulation has also had a notable effect on the European Union (EU) candidate countries, which are undergoing the process of harmonizing their legislature with the EU as part of the accession process. The Republic of Serbia is an example of such a candidate country, and its 2018 Personal Data Protection Act mirrors the majority of provisions in the GDPR. This paper presents the impact of the GDPR on health data management and Serbia's capability to conduct international health data research projects. Data protection incidents reported in Serbia are explored to identify common underlying causes using a novel taxonomy of contributing factors across aspects and health system levels. The GDPR has an extraterritorial application for the non-EU data controllers who process the data of EU citizens and residents, which mainly affects private practices used by medical tourists from the EU, public health care institutions frequented by foreigners, as well as expatriates, dual citizens, tourists, and other visitors. Serbia generally does not have well-established procedures to support international research collaborations around its health data. For smaller projects, contractual arrangements can be made with health data providers and their ethics committees. Even then, organizations that have not previously participated in similar ventures may require approval or support from health authorities. Extensive studies that involve multisite data typically require the support of central health system institutions and relevant research data aggregators or electronic health record vendors. The lack of a framework for preparation, anonymization, and assurance of privacy preservation forces researchers to rely heavily on local expertise and support. Given the current limitation and potential issues with the legislation, it remains to be seen whether the move toward the GDPR will be beneficial for the Serbian health system, medical research, protection of personal data and privacy rights, and research capacity. Although significant progress has been made so far, a strategic approach is needed at the national level to address insufficient resources in the area of data protection and develop the personal data protection environment further. This will also require a targeted educational effort among health workers and decision makers, aiming to improve awareness and develop skills and knowledge necessary for the workforce.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".