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

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.

Active, Not Recruiting

This study is active but is not currently recruiting participants.

Key information

Conditions

Stroke Acquired Brain Injury Brain Diseases Brain Injuries Brain Injuries, Traumatic Brain Neoplasms Brain Tumor Cardiovascular Diseases Central Nervous System Diseases Central Nervous System Infections Central Nervous System Neoplasms Cerebrovascular Disorders Craniocerebral Trauma Infections Multiple Trauma Neoplasms Neoplasms by Site Nervous System Diseases Nervous System Neoplasms Polytrauma Trauma, Nervous System Traumatic Brain Injury Vascular Diseases Wounds and Injuries

Age range

21 year–100 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Tan Tock Seng Hospital

Singapore

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

Rehabilitation length of stay

Time frame: 1998-2022

Duration of which patient is admitted to and discharge from the rehabilitation ward.
Hospital bill

Time frame: 2012-2022

Bill size of patient's stay in the rehabilitation ward, including subsidies, insurance and copayment.
Housing type

Time frame: 2014-2023

Type of housing to look at the socio-economics status of the patients.

Sponsors and collaborators

Lead sponsor

Tan Tock Seng Hospital

Other

Registry information

Official study title

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

Important dates

Study start: 2024
Primary completion: 2025
Study completion: 2026
First posted: Nov 26, 2024
Registry last updated: Nov 27, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Active, Not Recruiting