Enhancing ICU Management and Addressing Challenges in Türkiye Through AI-Powered Patient Classification and Increased Usability With ICU Placement Software

Abstract

The increasing demand for intensive care unit (ICU) admissions, and the associated rising costs have urged the need for effective management strategies. In this research, we focus on the challenges faced by (1) hospitals in report generation and in their effort to properly allocate ICU patients, and (2) insurance organizations responsible for payments. We address the issues of misclassification and financial burden on hospitals and insurance organizations that arise from inefficient and subjective application of regulations while also considering the impact on medical personnel. Through existing literature analysis, as well as extensive discussions with critical care professionals and insights gained from university hospitals, we identified the need for a supportive machine learning model for ICU level classification of patients, and furthermore, we propose an easily deployable and highly interoperability software system specifically for placement of patients in various ICU levels. We aim to support healthcare professionals in their decision-making process with the supportive machine learning model and the software system that we named “heartbeat”. This research aims to bridge the gap between hospitals and insurance institutions to ensure fair and objective patient classification and to improve the overall ICU management. The process has been tested using MIMIC-III version 1.4 dataset as a proof of concept to demonstrate the applicability and effectiveness of the developed system. Further testing using real data after official deployment and usage by various stakeholders.

Keywords

• ICU
• MIMIC-III
• supportive
• machine learning
• ICU management
• ICU level