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

Tan Tock Seng Hospital

Status: Active, not recruiting

Conditions: Traumatic Brain Injury; Stroke; Brain Tumor; Acquired Brain Injury; Central Nervous System Infections; Polytrauma

Study type: Observational

Funder types: Other

Identifiers

NCT06704997

DSRB 2023/00873

Details and patient eligibility

About

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.

Full 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.

Enrollment: 10,000 estimated patients

Sex: All

Ages: 21 to 100 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• All patients who completed inpatient rehabilitation with the index conditions in their discharge summaries

Exclusion criteria

• Did not complete inpatient rehabilitation as they are discharged against medical advice

Trial design

10,000 participants in 1 patient group

Patients with tertiary neurological rehabilitation

Description:

Patients with confirmed diagnosis of stroke, acquired brain injuries, traumatic brain injuries, brain tumours, central nervous system infections and polytrauma from acute neurological or neurosurgical units in Singapore. The cohort will be selected from the TTSH Rehabilitation Centre (TTSH RC) admissions from year 2016 to the present.