Abstract
The time a patient spends waiting to be seen by a healthcare professional is an important determinant of patient satisfaction in outpatient care. Hence, it is crucial to identify parameters that affect the waiting time and optimize it accordingly. First, statistical analysis was used to validate the effective parameters. However, no parameters were found to have significant effects with respect to the entire outpatient department or to each department. Therefore, we studied the improvement of patient waiting times by analyzing and optimizing effective parameters for each physician. Queueing theory was used to calculate the probability that patients would wait for more than 30 min for a consultation session. Using this result, we built metamodels for each physician, formulated an effective method to optimize the problem, and found a solution to minimize waiting time using a non-dominated sorting genetic algorithm (NSGA-II). On average, we obtained a 30% decrease in the probability that patients would wait for a long period. This study shows the importance of customized improvement strategies for each physician.
Original language | English |
---|---|
Article number | 2073 |
Journal | International journal of environmental research and public health |
Volume | 19 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2022 Feb 1 |
Bibliographical note
Funding Information:Acknowledgments: This research was supported by the Brain Korea 21 FOUR project funded by the National Research Foundation (NRF) of Korea, Yonsei University College of Nursing. This research was supported by the Brain Korea 21 FOUR project funded by the National Research Foundation (NRF) of Korea, School of Mechanical Engineering, Yonsei University.
Funding Information:
Funding: This work was funded by a 2018 Multidisciplinary Joint Research Fund from Mo-Im Kim Nursing Research Institute, Yonsei University College of Nursing and College of Engineering (No. 6-2018-0119; No. 6-2018-0189).
Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
All Science Journal Classification (ASJC) codes
- Pollution
- Public Health, Environmental and Occupational Health
- Health, Toxicology and Mutagenesis