NCT06704997

Machine Learning to Predict Factors Affecting Rehabilitation Length of Stay and Healthcare Costs for Neurological Rehabilitation

Machine Learning Predictive Analysis of Key Factors Influencing Rehabilitation Length of Stay (RLOS) and Direct Hospitalization Costs for Neurological Inpatient Rehabilitation at Tertiary Care Hospital

• Status: Active Not Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 10,000 (estimated)
• Sponsor: Tan Tock Seng Hospital · Academic / Other
• Sex: All
• Age: 21 Years – 100 Years
• Healthy volunteers: Not accepted

Summary

The aim of this retrospective study is to ascertain total direct costs, rehabilitation length of stay (RLOS) and factors associated with RLOS for neurological inpatient rehabilitation at the tertiary care hospital.

Detailed description

The aim of the study is to identify factors that influence RLOS and the correlated costs for neurological rehabilitation in tertiary rehab using data extracted from EPIC. It is also aimed to identify the median direct costs to find out the main contributors to the costs in the local population. Lastly, the study aims to utilise artificial intelligence or machine learning to analyse the compiled data to develop a predictive model. The model aspires to understand factors associated with extended RLOS and to predict RLOS of patients who require neurological rehabilitation, aiding preemptive measures.

Conditions

• Stroke
• Acquired Brain Injury
• Traumatic Brain Injury
• Brain Tumor
• Central Nervous System Infections
• Polytrauma

Timeline

• Start date: 2024-06-01
• Primary completion: 2025-12-31
• Completion: 2026-12-31
• First posted: 2024-11-26
• Last updated: 2024-11-27

Locations

1 site across 1 country: Singapore