Enhancing Risk-Adjusted EWMA Control Chart Utilizing Artificial Neural Networks
Abstract This study focuses on implementing a quality framework to improve patient quality and safety in healthcare. Evaluating the performance of healthcare services, especially under different patient health conditions poses significant challenges. We used the artificial neural networks (ANN) mode...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2024-10-01
|
| Series: | Journal of Statistical Theory and Applications (JSTA) |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44199-024-00094-8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract This study focuses on implementing a quality framework to improve patient quality and safety in healthcare. Evaluating the performance of healthcare services, especially under different patient health conditions poses significant challenges. We used the artificial neural networks (ANN) model to effectively manage and manage patient risk factors. Our proposed approach involves creating an exponentially weighted moving average (EWMA) control chart. This graph is based on residuals derived from the ANN model allowing comprehensive analysis of actual cardiac surgery patient data. We use ANN for patient assessment to create the ANN-EWMA control chart. The results show that this chart has remarkable shift detection capabilities and outperforms the effectiveness of the risk-adjusted EWMA chart suggesting potential improvements in patient care in healthcare. |
|---|---|
| ISSN: | 2214-1766 |