Secure and Scalable Digital Health Framework with Blockchain and Software Design Principles
Bibliographic record
Abstract
Digital health frameworks, particularly Remote Health Monitoring Systems (RHMS), are crucial in helping real-time patient care, especially for aging or remote populations and those with chronic health conditions. However, current RHMS solutions usually struggle with scalability, maintainability, and security challenges. In this paper, we propose a framework that directly addresses these issues by employing contemporary solutions such as software design patterns: Observer, Strategy, Composite, Decorator, Protection Proxy, Factory, and Singleton, within an MVVM architecture. Furthermore, we leverage cloud services (Google Firebase), blockchain (for immutable record keeping), IoT messaging (MQTT), and data standardization. The system supports the acquisition of real-time patient data, fault tolerance, and secure communication while meeting the HIPAA and GDPR requirements. Quantitative evaluations demonstrate a 40% improvement in data throughput and a 35% reduction in system response times compared to legacy models. This approach uniquely combines established software design patterns and principles with technologies such as blockchain and cloud services to ensure security, scalability, and auditability, thereby addressing critical challenges in RHMS for robust patient monitoring.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".