Revolutionizing Healthcare Management: Architecture of a Web-based Medical Triage Service
Bibliographic record
Abstract
During the COVID-19 pandemic, the traditional emergency healthcare systems faced unprecedented strain due to the sharp rise in demands for urgent care, scarcity of resources, and increased risks of people getting infected while waiting at the emergency care facility. We present Triage-Bot, an online medical triage provisioning service, that can revolutionize emergency care by decreasing the load on emergency departments (ED), reducing healthcare expenses, and improving the quality of care. Empowered by artificial intelligence and natural language processing, the Triage-Bot service assesses and prioritizes patients' needs based on symptoms, medical history, and perceived conditions from multimodal video, audio, and text data captured during patients' interactions. The captured summarized information with a severity ranking is sent to a human expert to suggest the next action on the user's part. The diverse data types used by the Triage-Bot in communication, authentication, data collection, storage, and analytics requires a robust and scalable system architecture for online service provisioning. In this paper, we specifically focus on the system design and architecture of the Triage-Bot for emergency healthcare settings. With integrated electronic medical records (EMR) and online platforms, the bot fosters collaboration among healthcare professionals and enables swift and informed decision-making even in the face of crises. By partially automating and offering a hybrid triage process, the Triage-Bot improves resource allocation, reduces healthcare management costs for emergency care, minimizes patient waiting times, and improves wellbeing. To address the complexities and demands of healthcare data management, our proposed system incorporates MongoDB database for flexibility, scalability, and versatility in supporting different types of data. Additionally, we implement a data linking and analytics pipeline utilizing a data Lakehouse system to effectively ingest, manage, process, and generate knowledge from heterogeneous data sources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".