Blockchain Applications in Core Healthcare Services: Patient Data, Research, and Institutional Processes
Bibliographic record
Abstract
Objective: This research aims to systematically examine the application of blockchain technology in core primary healthcare services, with a particular focus on its ability to enhance data integrity, transparency, and operational efficiency. The objective is to identify and analyze the primary areas where blockchain is being utilized within the health sciences and to evaluate its contributions to secure patient consent, reliable data verification, and the protection of sensitive health information. Methods: This study conducted a systematic literature review of research and review articles indexed in the Web of Science Core Collection between January 10 and March 15, 2025. Articles were selected based on predefined search strings targeting blockchain applications in health sciences, as detailed in the search strategy (Figure 1). Bibliometric analysis was performed using VOSviewer and the Biblioshiny interface of R Bibliometrix to identify thematic areas, keyword co-occurrences, and research trends. Overlay and network visualizations were used to reveal temporal patterns and relational structures among keywords. To enhance the scope of the review, supplementary searches were also conducted via Google Scholar, providing additional insight into emerging topics not yet indexed in Web of Science. Results: The analysis revealed eight major thematic areas where blockchain is prominently applied: secure patient consent and data management, healthcare supply chain processes, clinical research and monitoring, legal and intellectual property concerns, disease tracking and epidemiological management including COVID-19, insurance and billing systems, organ transplantation logistics, and applications in cancer and pharmaceutical research. The data demonstrate an increasing focus on blockchain's role in enhancing transparency and accountability in both institutional and patient-centered healthcare services. Conclusions: Blockchain holds considerable promise for advancing healthcare systems. However, its effective implementation depends on a comprehensive approach that combines technological innovation with supportive policy frameworks and ethical considerations. These findings provide valuable guidance for stakeholders seeking to integrate blockchain in health service delivery.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".