Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomy
Abstract Background The rapid growth in the complexity of services and stringent quality requirements present a challenge to all healthcare facilities, especially from an economic perspective. The goal is to implement different strategies that allows to enhance and obtain health processes closer to...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-05-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-022-01884-9 |
| _version_ | 1811258672873996288 |
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| author | Teresa Angela Trunfio Arianna Scala Cristiana Giglio Giovanni Rossi Anna Borrelli Maria Romano Giovanni Improta |
| author_facet | Teresa Angela Trunfio Arianna Scala Cristiana Giglio Giovanni Rossi Anna Borrelli Maria Romano Giovanni Improta |
| author_sort | Teresa Angela Trunfio |
| collection | DOAJ |
| description | Abstract Background The rapid growth in the complexity of services and stringent quality requirements present a challenge to all healthcare facilities, especially from an economic perspective. The goal is to implement different strategies that allows to enhance and obtain health processes closer to standards. The Length Of Stay (LOS) is a very useful parameter for the management of services within the hospital and is an index evaluated for the management of costs. In fact, a patient's LOS can be affected by a number of factors, including their particular condition, medical history, or medical needs. To reduce and better manage the LOS it is necessary to be able to predict this value. Methods In this study, a predictive model was built for the total LOS of patients undergoing laparoscopic appendectomy, one of the most common emergency procedures. Demographic and clinical data of the 357 patients admitted at “San Giovanni di Dio e Ruggi d’Aragona” University Hospital of Salerno (Italy) had used as independent variable of the multiple linear regression model. Results The obtained model had an R2 value of 0.570 and, among the independent variables, the significant variables that most influence the total LOS were Age, Pre-operative LOS, Presence of Complication and Complicated diagnosis. Conclusion This work designed an effective and automated strategy for improving the prediction of LOS, that can be useful for enhancing the preoperative pathways. In this way it is possible to characterize the demand and to be able to estimate a priori the occupation of the beds and other related hospital resources. |
| first_indexed | 2024-04-12T18:18:12Z |
| format | Article |
| id | doaj.art-b3cfa4374ab34a89818758718ab6d815 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-04-12T18:18:12Z |
| publishDate | 2022-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-b3cfa4374ab34a89818758718ab6d8152022-12-22T03:21:32ZengBMCBMC Medical Informatics and Decision Making1472-69472022-05-012211810.1186/s12911-022-01884-9Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomyTeresa Angela Trunfio0Arianna Scala1Cristiana Giglio2Giovanni Rossi3Anna Borrelli4Maria Romano5Giovanni Improta6Department of Advanced Biomedical Sciences, University Hospital of Naples ‘Federico II’Department of Public Health, University of Naples “Federico II”University of Rome “La Sapienza”“San Giovanni di Dio e Ruggi d’Aragona” University Hospital“San Giovanni di Dio e Ruggi d’Aragona” University HospitalDepartment of Electrical Engineering and Information Technology, University of Study of Naples “Federico II”Department of Public Health, University of Naples “Federico II”Abstract Background The rapid growth in the complexity of services and stringent quality requirements present a challenge to all healthcare facilities, especially from an economic perspective. The goal is to implement different strategies that allows to enhance and obtain health processes closer to standards. The Length Of Stay (LOS) is a very useful parameter for the management of services within the hospital and is an index evaluated for the management of costs. In fact, a patient's LOS can be affected by a number of factors, including their particular condition, medical history, or medical needs. To reduce and better manage the LOS it is necessary to be able to predict this value. Methods In this study, a predictive model was built for the total LOS of patients undergoing laparoscopic appendectomy, one of the most common emergency procedures. Demographic and clinical data of the 357 patients admitted at “San Giovanni di Dio e Ruggi d’Aragona” University Hospital of Salerno (Italy) had used as independent variable of the multiple linear regression model. Results The obtained model had an R2 value of 0.570 and, among the independent variables, the significant variables that most influence the total LOS were Age, Pre-operative LOS, Presence of Complication and Complicated diagnosis. Conclusion This work designed an effective and automated strategy for improving the prediction of LOS, that can be useful for enhancing the preoperative pathways. In this way it is possible to characterize the demand and to be able to estimate a priori the occupation of the beds and other related hospital resources.https://doi.org/10.1186/s12911-022-01884-9AppendectomyMultiple linear regressionLength of stayPublic health |
| spellingShingle | Teresa Angela Trunfio Arianna Scala Cristiana Giglio Giovanni Rossi Anna Borrelli Maria Romano Giovanni Improta Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomy BMC Medical Informatics and Decision Making Appendectomy Multiple linear regression Length of stay Public health |
| title | Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomy |
| title_full | Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomy |
| title_fullStr | Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomy |
| title_full_unstemmed | Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomy |
| title_short | Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomy |
| title_sort | multiple regression model to analyze the total los for patients undergoing laparoscopic appendectomy |
| topic | Appendectomy Multiple linear regression Length of stay Public health |
| url | https://doi.org/10.1186/s12911-022-01884-9 |
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