PARALLEL PATIENT TREATMENT TIME PREDICTION USING EFFECTIVE HOSPITAL QUEUING-RECOMMENDATION SYSTEM

Abstract

Effective patient queue management to reduce patient wait delays and patient overcrowding is one in all the
most challenges featured by hospitals. Inessential and annoying waits for long periods result in substantial human
resource and time wastage and increase the frustration endured by patients. for every patient among the queue, the
whole treatment time of all the patients before him is that the time that he should wait. it would be convenient and
desirable if the patients might receive the foremost efficient treatment organize and understand the expected waiting time
through a mobile application that updates in real time. Therefore, we've a bent to propose a Patient Treatment Time
Prediction (PTTP) algorithmic to predict the waiting time for each treatment task for a patient. We've a tendency to use
realistic patient data from varied hospitals to induce a patient treatment time model for every task. Supported this largescale, realistic data-set, the treatment time for each patient among the present queue of every task is predicted.
Supported the expected waiting time, a Hospital Queuing Recommendation (HQR) system is developed. HQR calculates
Associate in Nursing predicts an economical and convenient treatment started suggested for the patient. As a result of the
large-scale, realistic data-set and also the demand for time period response, the PTTP algorithmic and HQR system
mandate efficiency and low-latency response. Our proposed model to recommend an efficient treatment set up for
patients to reduce their wait times in hospitals.