Reducing Waiting Times to Improve Patient Satisfaction: A Hybrid Strategy for Decision Support Management
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...
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MDPI AG
2024-11-01
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| Series: | Mathematics |
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| author | Jenny Morales Fabián Silva-Aravena Paula Saez |
| author_facet | Jenny Morales Fabián Silva-Aravena Paula Saez |
| author_sort | Jenny Morales |
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| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> 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. |
| format | Article |
| id | doaj-art-01bf5592f7404689a703fb08e615acca |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-01bf5592f7404689a703fb08e615acca2025-08-20T02:38:47ZengMDPI AGMathematics2227-73902024-11-011223374310.3390/math12233743Reducing Waiting Times to Improve Patient Satisfaction: A Hybrid Strategy for Decision Support ManagementJenny Morales0Fabián Silva-Aravena1Paula Saez2Facultad de Ciencias Sociales y Económicas, Universidad Católica del Maule, Avenida San Miguel 3605, Talca 3460000, ChileFacultad de Ciencias Sociales y Económicas, Universidad Católica del Maule, Avenida San Miguel 3605, Talca 3460000, ChileFacultad de Ciencias Sociales y Económicas, Universidad Católica del Maule, Avenida San Miguel 3605, Talca 3460000, ChilePatient 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> 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.https://www.mdpi.com/2227-7390/12/23/3743patient satisfactionhealthcare operations efficiencypredictive modelingsimulation-based decision supportwait-time optimization |
| spellingShingle | Jenny Morales Fabián Silva-Aravena Paula Saez Reducing Waiting Times to Improve Patient Satisfaction: A Hybrid Strategy for Decision Support Management Mathematics patient satisfaction healthcare operations efficiency predictive modeling simulation-based decision support wait-time optimization |
| title | Reducing Waiting Times to Improve Patient Satisfaction: A Hybrid Strategy for Decision Support Management |
| title_full | Reducing Waiting Times to Improve Patient Satisfaction: A Hybrid Strategy for Decision Support Management |
| title_fullStr | Reducing Waiting Times to Improve Patient Satisfaction: A Hybrid Strategy for Decision Support Management |
| title_full_unstemmed | Reducing Waiting Times to Improve Patient Satisfaction: A Hybrid Strategy for Decision Support Management |
| title_short | Reducing Waiting Times to Improve Patient Satisfaction: A Hybrid Strategy for Decision Support Management |
| title_sort | reducing waiting times to improve patient satisfaction a hybrid strategy for decision support management |
| topic | patient satisfaction healthcare operations efficiency predictive modeling simulation-based decision support wait-time optimization |
| url | https://www.mdpi.com/2227-7390/12/23/3743 |
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