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

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06704997

Enrollment

10000

Registered

2024-11-26

Start date

2024-06-01

Completion date

2026-12-31

Last updated

2024-11-27

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

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

Keywords

Rehabilitation, Length of Stay, Stroke, Traumatic Brain Injury, Brain tumour, Cancer, Trauma, Acquired Brain Injury, Healthcare, Hospitalisation costs

Brief 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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

21 Years to 100 Years

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

Design outcomes

Primary

Measure	Time frame	Description
Rehabilitation length of stay	1998-2022	Duration of which patient is admitted to and discharge from the rehabilitation ward.
Hospital bill	2012-2022	Bill size of patient's stay in the rehabilitation ward, including subsidies, insurance and copayment.
Housing type	2014-2023	Type of housing to look at the socio-economics status of the patients.

Countries

Singapore

Outcome results