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

N/AActive Not RecruitingNCT06704997

Tan Tock Seng Hospital10,000 enrolled

Overview

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.

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.

Study Type

OBSERVATIONAL

Enrollment

10,000

Conditions

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

Eligibility

Sex: ALLMin age: 21 YearsMax age: 100 Years

Medical Language ↔ Plain English

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

Outcomes

Primary Outcomes

Rehabilitation length of stay

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

Time frame: 1998-2022

Hospital bill

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

Time frame: 2012-2022

Housing type

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

Time frame: 2014-2023