Hospital Queuing Recommendation by Using Patient Treatment Time Prediction Model

Abstract

Now a days, to minimize patient wait delays and patient overcrowding is one of the major challenges faced by hospitals, mostly used Effective patient queue management. Annoying waits for long periods result in substantial human resource and time wastage and which increase the frustration endured by patients. The total treatment time of all the patients before him is the time that he must wait for each patient in the queue. It would be favorable and excellent if the patients could receive the most efficient treatment plan and know the adumbrated waiting time through a mobile application that updates in real time. Hence, we propose a Patient Treatment Time Prediction (PTTP) algorithm to predict the waiting time for each treatment task for a patient. To develop such idea, we use realistic patient data from various hospitals to obtain a patient treatment time model for each task. The treatment time for each patient in the current queue of each task is anticipated which us based on this large-scale, realistic dataset, similarly based on the predicted waiting time, a Hospital Queuing Recommendation (HQR) system is developed. HQR appraises and calls an adequate and convenient treatment plan recommended for the patient. Realistic dataset and the requirement for real-time response, which is result of the large-scale, the PTTP algorithm and HQR system decree competence and low-latency response. We use an Apache Spark-based cloud implementation to achieve the aforementioned goals. Extensive experimentation and simulation results establish the effectiveness and appropriateness of our proposed model to recommend an effective treatment plan for patients to curtail their wait times in hospitals.