A Comprehensive Review of Resource-Constrained Encryption Design with Hospital Information Systems: Security Models, Optimization Techniques, and Emerging Computing Applications
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".