Analyzing the factors that are involved in length of inpatient stay at the hospital for diabetes patients

TL;DR

This study addresses how to quantify drivers of inpatient length of stay for diabetes patients amid rising disease burden and pandemic-related resource pressures. It uses a quantitative approach with a Generalized Linear Model (GLM) assuming a Poisson family and a $log$ link to predict the dependent variable $days$ from predictors including age, race, admit_type_id, readmission status, number of medications, and prior diagnoses. The findings identify key drivers, including age groups, higher medication counts, and certain admission types, with some associations varying by race and prior readmission, while acknowledging model limitations like heteroscedasticity and non-normal residuals, and the dataset being dated (1999–2008). The work provides actionable insights for hospital administrators to optimize patient management and resource allocation, and suggests avenues for improved modeling with more recent data and alternate distributions.

Abstract

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.