2023
Prema, N. I Gusti Ngurah Agung Agni; Avilandi, P. Naufal; Fathan,; Andreswari, R.; Machado, J. M. F.
Discovery of Hospital Billing Process in a Regional Hospital Using Process Mining Proceedings Article
Em: Institute of Electrical and Electronics Engineers Inc., 2023, ISBN: 9798350303414, (cited By 0; Conference of 5th International Conference on Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2023 ; Conference Date: 2 August 2023 Through 3 August 2023; Conference Code:193233).
Resumo | Links | BibTeX | Etiquetas: Celonis; Effective solution; Event logs; Healthcare services; Hospital biling; Mining techniques; PM4Py; Process mining; Process-models; Time cost, Customer satisfaction; Data mining; Efficiency; Health care, Hospitals
@inproceedings{IGustiNgurahAgungAgniPrema2023,
title = {Discovery of Hospital Billing Process in a Regional Hospital Using Process Mining},
author = {N. I Gusti Ngurah Agung Agni Prema and P. Naufal Avilandi and Fathan and R. Andreswari and J. M. F. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175616569&doi=10.1109%2fICADEIS58666.2023.10271040&partnerID=40&md5=9da901ec00a5a466f5c8da5360d3e6d7},
doi = {10.1109/ICADEIS58666.2023.10271040},
isbn = {9798350303414},
year = {2023},
date = {2023-01-01},
journal = {ICADEIS 2023 - International Conference on Advancement in Data Science, E-Learning and Information Systems: Data, Intelligent Systems, and the Applications for Human Life, Proceeding},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Delays and complexities in the billing process for healthcare services can inconvenience patients and hinder the efficient functioning of regional hospitals. This study aims to utilize process mining techniques to analyze event logs and identify bottleneck activities within the billing process. By evaluating the process, the research aims to understand the causality behind these bottlenecks and propose effective solutions for enhancing efficiency and reducing time costs. The research employs the PM4Py open-source toolkit, including PM4Py-GPU for computationally intensive tasks. Through fitness alignments between event logs and process models, it is found that approximately 47.128 percent of traces match the process model, exhibiting a good level of conformity with an average fitness of 0.8888. Notably, the study reveals that over 3 percent of billing processes in regional hospitals exhibit repetitive occurrences of specific activities consecutively. The identification of this repetitive activity pattern prompts a deeper investigation into its root causes and implications for resource utilization and performance. By addressing these causative factors, the research aims to propose optimized approaches to streamline the billing process, thus enhancing overall efficiency and customer satisfaction levels for the hospitals. Overall, the findings of this study contribute to a comprehensive understanding of the billing process in healthcare services and provide valuable insights for hospitals to implement targeted improvements, reduce delays, and offer high-quality services to their patients. © 2023 IEEE.},
note = {cited By 0; Conference of 5th International Conference on Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2023 ; Conference Date: 2 August 2023 Through 3 August 2023; Conference Code:193233},
keywords = {Celonis; Effective solution; Event logs; Healthcare services; Hospital biling; Mining techniques; PM4Py; Process mining; Process-models; Time cost, Customer satisfaction; Data mining; Efficiency; Health care, Hospitals},
pubstate = {published},
tppubtype = {inproceedings}
}
Delays and complexities in the billing process for healthcare services can inconvenience patients and hinder the efficient functioning of regional hospitals. This study aims to utilize process mining techniques to analyze event logs and identify bottleneck activities within the billing process. By evaluating the process, the research aims to understand the causality behind these bottlenecks and propose effective solutions for enhancing efficiency and reducing time costs. The research employs the PM4Py open-source toolkit, including PM4Py-GPU for computationally intensive tasks. Through fitness alignments between event logs and process models, it is found that approximately 47.128 percent of traces match the process model, exhibiting a good level of conformity with an average fitness of 0.8888. Notably, the study reveals that over 3 percent of billing processes in regional hospitals exhibit repetitive occurrences of specific activities consecutively. The identification of this repetitive activity pattern prompts a deeper investigation into its root causes and implications for resource utilization and performance. By addressing these causative factors, the research aims to propose optimized approaches to streamline the billing process, thus enhancing overall efficiency and customer satisfaction levels for the hospitals. Overall, the findings of this study contribute to a comprehensive understanding of the billing process in healthcare services and provide valuable insights for hospitals to implement targeted improvements, reduce delays, and offer high-quality services to their patients. © 2023 IEEE.