A Parallel Patient Treatment Time Prediction Algorithm and Its Applications in Hospital Queuing-Recommendation

Abstract

Effective patient queue management to chop back patient wait delays and patient overcrowding is one
altogether the foremost challenges featured by hospitals. In essential and annoying waits for long periods result in
substantial human resource and time wastage and increase the frustration endured by patients. For each patient at
intervals the queue, the complete treatment time of all the patients before him is that the time that he ought to wait.
would possibly it’d be convenient and fascinating if the patients might receive the foremost economical treatment
organize and perceive the expected waiting time through a mobile application that updates in real time. Therefore,
we've an inclination to propose a Patient Treatment Time Prediction (PTTP) recursive to predict the waiting time for
every treatment task for a patient. We’ve an inclination to use realistic patient information from varied hospitals to
urge a patient treatment time model for each task. Supported this large-scale, realistic data-set, the treatment time for
every patient at intervals the current queue of each task is foreseen. Sustained the expected waiting time, a Hospital
Queuing Recommendation (HQR) system is developed. HQR calculates Associate in Nursing predicts a cost-effective
and convenient treatment established steered for the patient.