Predicting Patient Length of Stay in Australian Emergency Departments Using Data Mining
Length of Stay (LOS) is an important performance metric in Australian Emergency Departments (EDs). Recent evidence suggests that an LOS in excess of 4 h may be associated with increased mortality, but despite this, the average LOS continues to remain greater than 4 h in many EDs. Previous studies ha...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/13/4968 |
| _version_ | 1797441931882528768 |
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| author | Sai Gayatri Gurazada Shijia (Caddie) Gao Frada Burstein Paul Buntine |
| author_facet | Sai Gayatri Gurazada Shijia (Caddie) Gao Frada Burstein Paul Buntine |
| author_sort | Sai Gayatri Gurazada |
| collection | DOAJ |
| description | Length of Stay (LOS) is an important performance metric in Australian Emergency Departments (EDs). Recent evidence suggests that an LOS in excess of 4 h may be associated with increased mortality, but despite this, the average LOS continues to remain greater than 4 h in many EDs. Previous studies have found that Data Mining (DM) can be used to help hospitals to manage this metric and there is continued research into identifying factors that cause delays in ED LOS. Despite this, there is still a lack of specific research into how DM could use these factors to manage ED LOS. This study adds to the emerging literature and offers evidence that it is possible to predict delays in ED LOS to offer Clinical Decision Support (CDS) by using DM. Sixteen potentially relevant factors that impact ED LOS were identified through a literature survey and subsequently used as predictors to create six Data Mining Models (DMMs). An extract based on the Victorian Emergency Minimum Dataset (VEMD) was used to obtain relevant patient details and the DMMs were implemented using the Weka Software. The DMMs implemented in this study were successful in identifying the factors that were most likely to cause ED LOS > 4 h and also identify their correlation. These DMMs can be used by hospitals, not only to identify risk factors in their EDs that could lead to ED LOS > 4 h, but also to monitor these factors over time. |
| first_indexed | 2024-03-09T12:33:23Z |
| format | Article |
| id | doaj.art-eddc0a61dde541c284732371b699ad05 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T12:33:23Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-eddc0a61dde541c284732371b699ad052023-11-30T22:28:09ZengMDPI AGSensors1424-82202022-06-012213496810.3390/s22134968Predicting Patient Length of Stay in Australian Emergency Departments Using Data MiningSai Gayatri Gurazada0Shijia (Caddie) Gao1Frada Burstein2Paul Buntine3Faculty of Information Technology, Monash University, Clayton, Melbourne, VIC 3800, AustraliaFaculty of Information Technology, Monash University, Clayton, Melbourne, VIC 3800, AustraliaFaculty of Information Technology, Monash University, Clayton, Melbourne, VIC 3800, AustraliaEastern Health Clinical School Monash University, Box Hill, Melbourne, VIC 3128, AustraliaLength of Stay (LOS) is an important performance metric in Australian Emergency Departments (EDs). Recent evidence suggests that an LOS in excess of 4 h may be associated with increased mortality, but despite this, the average LOS continues to remain greater than 4 h in many EDs. Previous studies have found that Data Mining (DM) can be used to help hospitals to manage this metric and there is continued research into identifying factors that cause delays in ED LOS. Despite this, there is still a lack of specific research into how DM could use these factors to manage ED LOS. This study adds to the emerging literature and offers evidence that it is possible to predict delays in ED LOS to offer Clinical Decision Support (CDS) by using DM. Sixteen potentially relevant factors that impact ED LOS were identified through a literature survey and subsequently used as predictors to create six Data Mining Models (DMMs). An extract based on the Victorian Emergency Minimum Dataset (VEMD) was used to obtain relevant patient details and the DMMs were implemented using the Weka Software. The DMMs implemented in this study were successful in identifying the factors that were most likely to cause ED LOS > 4 h and also identify their correlation. These DMMs can be used by hospitals, not only to identify risk factors in their EDs that could lead to ED LOS > 4 h, but also to monitor these factors over time.https://www.mdpi.com/1424-8220/22/13/4968clinical decision supportdata mining modelsemergency departmentlength of staypredictive data miningWeka |
| spellingShingle | Sai Gayatri Gurazada Shijia (Caddie) Gao Frada Burstein Paul Buntine Predicting Patient Length of Stay in Australian Emergency Departments Using Data Mining Sensors clinical decision support data mining models emergency department length of stay predictive data mining Weka |
| title | Predicting Patient Length of Stay in Australian Emergency Departments Using Data Mining |
| title_full | Predicting Patient Length of Stay in Australian Emergency Departments Using Data Mining |
| title_fullStr | Predicting Patient Length of Stay in Australian Emergency Departments Using Data Mining |
| title_full_unstemmed | Predicting Patient Length of Stay in Australian Emergency Departments Using Data Mining |
| title_short | Predicting Patient Length of Stay in Australian Emergency Departments Using Data Mining |
| title_sort | predicting patient length of stay in australian emergency departments using data mining |
| topic | clinical decision support data mining models emergency department length of stay predictive data mining Weka |
| url | https://www.mdpi.com/1424-8220/22/13/4968 |
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