Analyzing the factors that are involved in length of inpatient stay at the hospital for diabetes patients
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916592937336832 |
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| author | Lam, Jorden Xu, Kunpeng |
| author_facet | Lam, Jorden Xu, Kunpeng |
| contents | The paper investigates the escalating concerns surrounding the surge in diabetes cases, exacerbated by the COVID-19 pandemic, and the subsequent strain on medical resources. The research aims to construct a predictive model quantifying factors influencing inpatient hospital stay durations for diabetes patients, offering insights to hospital administrators for improved patient management strategies. The literature review highlights the increasing prevalence of diabetes, emphasizing the need for continued attention and analysis of urban-rural disparities in healthcare access. International studies underscore the financial implications and healthcare burden associated with diabetes-related hospitalizations and complications, emphasizing the significance of effective management strategies. The methodology involves a quantitative approach, utilizing a dataset comprising 10,000 observations of diabetic inpatient encounters in U.S. hospitals from 1999 to 2008. Predictive modeling techniques, particularly Generalized Linear Models (GLM), are employed to develop a model predicting hospital stay durations based on patient demographics, admission types, medical history, and treatment regimen. The results highlight the influence of age, medical history, and treatment regimen on hospital stay durations for diabetes patients. Despite model limitations, such as heteroscedasticity and deviations from normality in residual analysis, the findings offer valuable insights for hospital administrators in patient management. The paper concludes with recommendations for future research to address model limitations and explore the implications of predictive models on healthcare management strategies, ensuring equitable patient care and resource allocation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05189 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Analyzing the factors that are involved in length of inpatient stay at the hospital for diabetes patients Lam, Jorden Xu, Kunpeng Applications Artificial Intelligence The paper investigates the escalating concerns surrounding the surge in diabetes cases, exacerbated by the COVID-19 pandemic, and the subsequent strain on medical resources. The research aims to construct a predictive model quantifying factors influencing inpatient hospital stay durations for diabetes patients, offering insights to hospital administrators for improved patient management strategies. The literature review highlights the increasing prevalence of diabetes, emphasizing the need for continued attention and analysis of urban-rural disparities in healthcare access. International studies underscore the financial implications and healthcare burden associated with diabetes-related hospitalizations and complications, emphasizing the significance of effective management strategies. The methodology involves a quantitative approach, utilizing a dataset comprising 10,000 observations of diabetic inpatient encounters in U.S. hospitals from 1999 to 2008. Predictive modeling techniques, particularly Generalized Linear Models (GLM), are employed to develop a model predicting hospital stay durations based on patient demographics, admission types, medical history, and treatment regimen. The results highlight the influence of age, medical history, and treatment regimen on hospital stay durations for diabetes patients. Despite model limitations, such as heteroscedasticity and deviations from normality in residual analysis, the findings offer valuable insights for hospital administrators in patient management. The paper concludes with recommendations for future research to address model limitations and explore the implications of predictive models on healthcare management strategies, ensuring equitable patient care and resource allocation. |
| title | Analyzing the factors that are involved in length of inpatient stay at the hospital for diabetes patients |
| topic | Applications Artificial Intelligence |
| url | https://arxiv.org/abs/2406.05189 |