A Prediction Algorithm to Reduce Queuing Time using Parallel Patient Treatment Methodology - ., John; Mary, Vivia; ., Gunda; Reddy, Rishik; ., Mullapudi; ., Aravind; ., Yeleti; ., Naveen (International Journal of Engineering and Technology, 2018)
Inefficient management of the patients’ queues is one of the major issues faced in medical institutions like clinics and hospitals which end up in creating large crowds at the hospital lobbies and an extended waiting time in the patients’ treatments. Waiting unnecessarily for a long period of time, ends only in loss or wastage of time, human life and hospital resources. It also increases the number or frustrated patients that are waiting to get treatment required. Every single patient has to undergo a diagnosis and then be forwarded to other departments or medical personnel for further procedures. Therefore, each patient’s waiting time is the time taken by the system to diagnose all the patients before him/her in the queue. In such a condition, the most practical decision would be to give out an efficient treatment plan to each patient. This can be implemented as a mobile application, wherein a predictable waiting time according to the diagnosis of the patient is uploaded, which then updates itself in real-time. Taking this into consideration, this paper proposes a Patient Treatment Time Prediction (PTTP) algorithm that can predict the time taken by a procedure for a particular patient. This algorithm can be applied to real-world scenarios and can be implemented in a large-scale environment. After predicting a treatment time necessary, the Hospital Queuing Recommendation (HQR) system can be developed. The job of calculating and predicting a convenient and an efficient treatment time for a particular patient can be done by the HQR system. The necessary input data for this is taken from a real world scenario like an actual doctor estimating time for a procedure at a particular hospital. This algorithm and system should work hand-in-hand generating responses of the utmost efficiency and very low latency. Once the model goes through extensive experimentation and simulation, an efficient model that demonstrates the effectiveness of this system can be recommended to other hospitals or medical institutions thus reducing waiting time per patient.