  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.
