Synchronization of Medical Data in a Mobile Provisioning Environment: mHealth Use Case
Bibliographic record
Abstract
The use of mobile devices such as smartphones and tablets, as well as other Information and Communications Technology (ICT) tools, to facilitate healthcare delivery in the medical landscape, known as mHealth, has risen phenomenally. In most mHealth systems, mobile devices are employed as services and health information client consumers. Meaning, physicians use these devices to access the Electronic Health Record (EHR) which resides on back-end platforms. However, in a research collaboration with the Geriatrics Ward of the City Hospital in Saskatoon, Canada, we have identified a huge potential for facilitating the mobile device as a medical data hosting node. Our approach is beneficial for collecting the EHR remotely and pushing it to the Health Information System (HIS). For instance, Geriatrics' patients who are home can be attended to outside of the health facility since their medical data is being retrieved from or pushed to the HIS remotely. However, mobile devices communicate over wireless mediums, which can experience intermittent loss of connectivity. It is also possible to visit the patient's home, and there not be any connectivity. These situations can cause an inconsistency between the data that is on the physician's mobile device and the data on the HIS. Our work therefore proposes a cloud-powered middleware architecture that facilitates the synchronization of the data between the mobile devices and the HIS in soft real-time. The middleware employs the propagation of deltas and timestamp to determine which of the data is the newest update in any direction and ensures that all the operations are in sync. The evaluation of our proposed system shows minimal latency.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".