Parallel Patient treatment time predication data

Abstract

Effective patient queue management to reduce patient wait delays and patient overcrowding is one in all the
main 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 each patient within 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 arrange and understand the predicted waiting time through a
mobile application that updates in real time. Therefore, we have a tendency to propose a Patient Treatment Time
Prediction (PTTP) algorithmic to predict the waiting time for every treatment task for a patient. We have a tendency to
use realistic patient information from numerous hospitals to get a patient treatment time model for each task. Supported
this large-scale, realistic data-set, the treatment time for every patient within the current queue of each task is expected.
Supported the expected waiting time, a Hospital Queuing Recommendation (HQR) system is developed. HQR calculates
Associate in Nursing predicts an economical and convenient treatment set up 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 potency and low-latency response. Our proposed model to recommend an effective treatment plan for patients to
minimize their wait times in hospitals.