A Prediction Algorithm to Reduce Queuing Time using Parallel Patient Treatment Methodology
Keywords:Patient Treatment Time Prediction, Hospital Queuing Recommendation, Parallel Patient Treatment
Inefficient management of the patientsâ€™ 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 patientsâ€™ 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 patientâ€™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.
 Adomavicius, G. and Tuzhilin, A. (2005). â€œToward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions.â€ IEEE transactions on knowledge and data engineering, 17(6), 734â€“749.
 Ben-Haim, Y. and Tom-Tov, E. (2010). â€œA streaming parallel decision tree algorithm.â€ Journal of Machine Learning Research, 11(Feb), 849â€“872.
 Chrysos, G., Dagritzikos, P., Papaefstathiou, I., and Dollas, A. (2013). â€œHc-cart: A parallel system implementation of data mining classification and regression tree (cart) algorithm on a multi-fpga system.â€ ACM Transactions on Architecture and Code Optimization (TACO), 9(4), 47.
 Dean, J. and Ghemawat, S. (2008). â€œMapreduce: simplified data processing on large clusters.â€ Communications of the ACM, 51(1), 107â€“113.
 Fidalgo-Merino, R. and Nunez, M. (2011). â€œSelf-adaptive induction of regression trees.â€ IEEE transactions on pattern analysis and machine intelligence, 33(8), 1659â€“ 1672.
 Li, K., Tang, X., Veeravalli, B., and Li, K. (2015). â€œScheduling precedence constrained stochastic tasks on heterogeneous cluster systems.â€ IEEE Transactions on computers, 64(1), 191â€“204.
 Wu, X., Zhu, X., Wu, G.-Q., and Ding, W. (2014). â€œData mining with big data.â€ IEEE transactions on knowledge and data engineering, 26(1), 97â€“107.
 Yang, X., Guo, Y., and Liu, Y. (2013). Bayesian-inference-based recommendation in online social networks, Vol. 24. IEEE.
 Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauley, M., Franklin, M. J., Shenker, S., and Stoica, I. (2012). â€œResilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing.â€ Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation, USENIX Association. 2â€“2.
View Full Article:
How to Cite
LicenseAuthors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under aÂ Creative Commons Attribution Licensethat allows others to share the work with an acknowledgement of the work''s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal''s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (SeeÂ The Effect of Open Access).