Decision-Making Based on Predictive Process Monitoring of Patient Treatment Processes: A Case Study of Emergency Patients

This paper investigates predictive process monitoring problems in emergency treatment by combining the fields of process management and artificial intelligence. The objective is to predict the next activity and its timestamp in the treatment of emergency patients who have undergone surgery at the ga...

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Vydáno v:Advances in Operations Research Ročník 2023; s. 1 - 10
Hlavní autoři: Aravazhi, Agaraoli, Helgheim, Berit I., Aadahl, Petter
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Hindawi 28.09.2023
John Wiley & Sons, Inc
Témata:
Algorithms
Analysis
Artificial intelligence
Boolean
Business performance management
Case studies
Decision making
Emergency medical care
Emergency medicine
Health care reform
Health services
Hospital patients
Hospitals
Machine learning
Medical personnel
Medical research
Medicine, Experimental
Monitoring
Neural networks
Operations research
Patients
Performance measurement
Process management
Resource allocation
Streamlining
Surgery
Telemedicine
Urology
Norway
Germany
Taiwan
ISSN:1687-9147, 1687-9155
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Shrnutí:This paper investigates predictive process monitoring problems in emergency treatment by combining the fields of process management and artificial intelligence. The objective is to predict the next activity and its timestamp in the treatment of emergency patients who have undergone surgery at the gastroenterology or urology surgery units in a hospital in Norway. To achieve this goal, three models were developed using different algorithms, and the best performing model was identified using various performance metrics. The results demonstrate the potential of predictive process monitoring to accurately forecast the outcome of patient treatments. By leveraging the insights gained from predictive process monitoring, hospitals can make more informed decisions. The findings of this study suggest that predictive process monitoring holds significant promise as a tool for improving the efficiency and effectiveness of emergency patient treatment processes. This research has significant implications for the field of decision sciences, particularly regarding resource allocation, reducing waiting times, and improving patient outcomes. The ability to predict the outcomes of patient treatment processes has important implications for hospitals, allowing the streamlining and acceleration of the treatment process. Overall, this study provides a promising framework for predicting patient treatment processes by using the predictive process monitoring method. This could be expanded upon in future research, ultimately leading to improved patient outcomes and better decision-making in healthcare.
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ISSN:1687-9147
1687-9155
DOI:10.1155/2023/8867057