Abstract:
The study addresses the critical problem that Sri Lankan state-run hospitals have no data-driven
predictive tools of patient movement and resource allocation that have led to longer patients
waiting and poor bed utilization. This study was done in the face of problems such as long
patient waiting time and ineffective bed management owing to manual operations. The study
followed an organized machine learning pipeline, where 500 records of patients from 01/2023 to
12/2023 were used to train and test predictive models that would predict Length of Stay, Readmission
and Resource Requirement. The most important algorithms were the Random Forest,
Gradient Boosting, and XGBoost, and they were tested according to the cross-validation and
hyper parameter optimization. Findings confirmed that XGBoost was superior to the other models
in that it was able to manage the complex interactions between features effectively and the
test accuracy of 82.7% with F1-score of 0.809 indicating readmission prediction. Whereas, the
Mean Absolute Error of the model in predicting length of stay (LOS) was approximately 9 days
against a mean LOS of 15 days. It was also found that clinical and demographic factors such as
Infection condition type, age group to 41-60, and department Intensive Care Unit are the most
powerful predictors due to feature analysis, which indicated that the clinical presentation and
patient characteristics were stronger indicators of the decision-making process of bed management
than administrative characteristics. This study has shown that decision-support systems
can be utilized based on solid ground using AI predictive models to optimize work processes in
resource limited healthcare environments.
