ArticleScientific reports2025
AI-enhanced telemedicine: transforming resource allocation and cost-efficiency analysis via advanced queueing model.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Real-time healthcare staffing optimization using fuzzy logic and genetic algorithm.Health economics review · 2026Article
- Data-driven network diagnostics for optimizing POCUS management: an actionable hospital analytical approach.BMC health services research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing demand for telemedicine makes conventional queueing approaches inadequate for meeting dynamic and priority-driven service needs. Therefore, a more advanced queueing mechanism is necessary to address these requirements. This paper integrates an advanced queueing model with AI-scheduling, implemented using deep reinforcement learning, to optimize digital healthcare by adjusting doctor availability dynamically and prioritizing patient care based on urgency and time of arrival. This study utilizes Q-learning, a model-free reinforcement learning algorithm, to optimize resource allocation by minimizing patient wait times and dynamically adjusting doctor assignments based on real-time queue status. This paper conducts a case study on a multi-specialty hospital, Dhanwantri Hospital and Research Centre (DHRC), Jaipur, to validate the proposed model in a realistic environment. The validation results from analyzing more than 5,000 patient records across 10 simulation runs showed that AI scheduling reduced wait times for emergency patients by 40%, with a 95% confidence interval of [35%, 45%]. However, stagnant scheduling increased peak-hour wait times by 80%. The AI-powered and static scheduling models' peak wait times were analyzed using a paired t-test. The p-value of 0.003 showed that AI-scheduling meaningfully cut wait times. Further, we analyze the financial effects of AI-scheduling at the DHRC Hospital, Jaipur. The cost-efficiency analysis results show a significant drop in patient costs, improved health professional usage, and improved hospital resource allocation. The findings of this paper suggest that AI-enhanced queue management not only improves patient care but also offers a scalable and cost-efficient approach to modern telemedicine services.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.