A Comprehensive Review of Resource-Constrained Encryption Design with Hospital Information Systems: Security Models, Optimization Techniques, and Emerging Computing Applications
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
The rapid digital transformation of hospital information systems (HIS) has significantly improved healthcare delivery by enabling real-time patient monitoring, electronic health record (EHR) management, and telemedicine services. However, it has also introduced critical security challenges, particularly in resource-constrained environments involving IoT-based medical sensors, wearable devices, and embedded systems. These devices require efficient encryption mechanisms that ensure data confidentiality without excessive computational overhead. Traditional cryptographic methods, although secure, often fail to meet these requirements due to high energy consumption and processing demands. This paper provides a comprehensive review of resource-constrained encryption design in HIS, focusing on security models, optimization techniques, and emerging computing applications. It examines recent research on lightweight cryptography, homomorphic encryption, and quantum-resistant frameworks tailored for healthcare systems. The study highlights key trade-offs between security strength, efficiency, and energy usage, and explores integration with technologies such as artificial intelligence, edge computing, and blockchain. While lightweight and hybrid encryption models enhance performance, challenges remain in achieving scalable, secure, and user-friendly solutions, indicating important directions for future research.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 it