Blockchain and Healthcare: A Critical Analysis of Progress and Challenges in the Last Five Years
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Utilizing the fundamental characteristics of the decentralization, immutability, and transparency of blockchain technology, the healthcare industry has made notable advancements in incorporating it over the past five years. This review examines the progress and challenges encountered in this critical study by assessing 124 articles published by MDPI between 2018 and the current date. Examining blockchain’s potential uses, like safe data exchange and interoperability in supply chain management and electronic health records, provides exciting new directions for the future of healthcare. Blockchain technology can greatly increase efficiency and cost-effectiveness by guaranteeing data integrity, protecting patient privacy, and reducing administrative procedures. This paper objectively evaluates blockchain’s advancement in healthcare through a thorough analysis of real-world applications and research projects. By highlighting both its advantages and disadvantages, this analysis seeks to add to the continuing conversation about how blockchain will influence the way healthcare is managed and delivered in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it