Agentic AI for Clinical Urgency Mapping and Queue Optimization in High-Volume Outpatient Departments: A Simulation-Based Evaluation
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917375879675904 |
|---|---|
| author | Gupta, Ravish Kumar, Saket Dang, Maulik |
| author_facet | Gupta, Ravish Kumar, Saket Dang, Maulik |
| contents | Outpatient departments (OPDs) in Indian public hospitals face severe overcrowding, with daily volumes reaching 200--8,000 patients~\cite{aiims2020annual}. The prevailing First-Come-First-Served (FCFS) token system treats all patients equally regardless of clinical urgency, leading to dangerous delays for critical cases. We present an agentic AI framework integrating six components: voice-based multilingual symptom capture (modeled), LLM-powered severity prediction, load-aware physician assignment, adaptive queue optimization with urgency drift detection, a multi-objective orchestrator, and a Patient Memory System for longitudinal context-aware triage. Evaluated through discrete-event simulation of a District Hospital in Jabalpur (Madhya Pradesh) with 368 synthetic patients over 30 runs, the framework achieves 94.2\% critical patients seen within 10 minutes (vs.~30.8\% under FCFS), detects $\sim$236 simulated urgency drift events per session (modeled via stochastic deterioration probabilities), identifies $\sim$11.9 additional hidden-critical cases via patient memory, and recomposes queue urgency distribution from 13/36/158/161 (Critical/High/Medium/Low) to $\sim$25/178/115/50 through continuous reassessment, while maintaining comparable throughput ($\sim$40.4 patients/hour). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_00215 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Agentic AI for Clinical Urgency Mapping and Queue Optimization in High-Volume Outpatient Departments: A Simulation-Based Evaluation Gupta, Ravish Kumar, Saket Dang, Maulik Systems and Control J.3; I.2.1 Outpatient departments (OPDs) in Indian public hospitals face severe overcrowding, with daily volumes reaching 200--8,000 patients~\cite{aiims2020annual}. The prevailing First-Come-First-Served (FCFS) token system treats all patients equally regardless of clinical urgency, leading to dangerous delays for critical cases. We present an agentic AI framework integrating six components: voice-based multilingual symptom capture (modeled), LLM-powered severity prediction, load-aware physician assignment, adaptive queue optimization with urgency drift detection, a multi-objective orchestrator, and a Patient Memory System for longitudinal context-aware triage. Evaluated through discrete-event simulation of a District Hospital in Jabalpur (Madhya Pradesh) with 368 synthetic patients over 30 runs, the framework achieves 94.2\% critical patients seen within 10 minutes (vs.~30.8\% under FCFS), detects $\sim$236 simulated urgency drift events per session (modeled via stochastic deterioration probabilities), identifies $\sim$11.9 additional hidden-critical cases via patient memory, and recomposes queue urgency distribution from 13/36/158/161 (Critical/High/Medium/Low) to $\sim$25/178/115/50 through continuous reassessment, while maintaining comparable throughput ($\sim$40.4 patients/hour). |
| title | Agentic AI for Clinical Urgency Mapping and Queue Optimization in High-Volume Outpatient Departments: A Simulation-Based Evaluation |
| topic | Systems and Control J.3; I.2.1 |
| url | https://arxiv.org/abs/2604.00215 |