CHRONOS Project: The Transformation of Time-dependent Clinical Trajectories into Intelligent Ones Using an Innovative Technological Solution
Bibliographic record
Abstract
Introduction: The health system faces many challenges including the lack of personnel or resources and the overcrowding of emergency rooms. In this context, Real Time Locating Systems (RTLS) offer the possibility of improving the efficiency, safety and quality of care management. Clinical trajectories are currently very dependent on manual processes. We believe that real-time management systems that use geolocation can optimize time-dependent clinical trajectories, improve critical care and transform the health network for patients, caregivers and managers. Typically, RTLS tools require a significant investment in terms of installation, configuration, and integration. Method: The Nano Data Center (NDC) system developed by Humanitas Solutions is equipped with an advanced and low-cost IT infrastructure. It is self-deploying, self-configurable and allows geolocation and autonomous telecommunication with multiple interfaces (WIFI, Bluetooth, electrical). It requires a power source and operates without requiring access to technological infrastructures, which is the major difference with similar products based mainly on cloud computing and dependent on internet connectivity. We tested, as a pilot project, the deployment of the NDC system in a complex hospital environment (Centre intégré de la santé et des services sociaux de la Montérégie-Centre, Quebec, Canada) in order to demonstrate its potential use. Results: Using the NDC system, we were successful in establishing an autonomous communication network over several hospital floors. This innovation made it possible to support the real-time geolocalization of fictive patients and the creation of a real-time dashboard for monitoring clinical trajectories, analyzing data, and evaluating performance. Conclusion: The next development phases of the CHRONOS project include real-time notification and transformation of clinical trajectories into smart trajectories. The independence of the NDC system in terms of infrastructures would allow its deployment in low-resource environments, such as temporary installations or remote areas. Thus, its potential benefit in creating connected environments in disaster situations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".