Reducing Waiting Times to Improve Patient Satisfaction: A Hybrid Strategy for Decision Support Management

Jenny Morales, Fabián Silva-Aravena () and Paula Saez
Additional contact information
Jenny Morales: Facultad de Ciencias Sociales y Económicas, Universidad Católica del Maule, Avenida San Miguel 3605, Talca 3460000, Chile
Fabián Silva-Aravena: Facultad de Ciencias Sociales y Económicas, Universidad Católica del Maule, Avenida San Miguel 3605, Talca 3460000, Chile
Paula Saez: Facultad de Ciencias Sociales y Económicas, Universidad Católica del Maule, Avenida San Miguel 3605, Talca 3460000, Chile

Mathematics, 2024, vol. 12, issue 23, 1-15

Abstract: Patient satisfaction and operational efficiency are critical in healthcare. Long waiting times negatively affect patient experience and hospital performance. Addressing these issues requires accurate system time predictions and actionable strategies. This paper presents a hybrid framework combining predictive modeling and optimization to reduce system times and enhance satisfaction, focusing on registration, vitals, and doctor consultation. We evaluated three predictive models: multiple linear regression (MLR), log-transformed regression (LTMLR), and artificial neural networks (ANN). The MLR model had the best performance, with an R 2 of 0.93, an MAE of 7.29 min, and an RMSE of 9.57 min. MLR was chosen for optimization due to its accuracy and efficiency, making it ideal for implementation. The hybrid framework combines the MLR model with a simulation-based optimization system to reduce waiting and processing times, considering resource constraints like staff and patient load. Simulating various scenarios, the framework identifies key bottlenecks and allocates resources effectively. Reducing registration and doctor consultation wait times were identified as primary areas for improvement. Efficiency factors were applied to optimize waiting and processing times. These factors include increasing staff during peak hours, improving workflows, and automating tasks. As a result, registration wait time decreased by 15%, vitals by 20%, and doctor consultation by 25%. Processing times improved by 10–15%, leading to an average reduction of 22.5 min in total system time. This paper introduces a hybrid decision support system that integrates predictive analytics with operational improvements. By combining the MLR model with simulation, healthcare managers can predict patient times and test strategies in a risk-free, simulated environment. This approach allows real-time decision-making and scenario exploration without disrupting operations. This methodology highlights how reducing waiting times has a direct impact on patient satisfaction and hospital operational efficiency, offering an applicable solution that does not require significant structural changes. The results are practical and implementable in resource-constrained healthcare environments, allowing for optimized staff management and patient flow.

Keywords: patient satisfaction; healthcare operations efficiency; predictive modeling; simulation-based decision support; wait-time optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/23/3743/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/23/3743/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:23:p:3743-:d:1531516

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().