Capabilities ofArtificial Intelligence Models in Externation Decision of Patient Who Followed in Intensive Care Unit ()

NCT06584890 | Completed

• 1 other identifier: Externation of ICU
• Study Type: observational
• Target: 398
• Locations: 1 country
• Sites: 1

Brief Summary

This clinical study aims to evaluate the effectiveness of General Artificial Intelligence (AI) models, specifically ChatGPT and Gemini, in assisting with the decision-making process for discharging patients from the Intensive Care Unit (ICU) to a general ward or home. The timing of ICU discharge is a critical decision that significantly impacts patient outcomes and the efficient use of ICU resources. This study seeks to determine whether AI models can accurately and efficiently predict the optimal time for patient discharge, supporting clinicians in making informed decisions. The primary hypothesis is that AI models can improve the accuracy and speed of discharge decisions compared to traditional methods. The study will assess the agreement between the AI model predictions and the decisions made by ICU specialists. Additionally, the study will compare the performance of ChatGPT and Gemini AI models to identify which model offers the most reliable and timely discharge decisions. By exploring the potential of AI in clinical decision-making, this research could contribute to the development of innovative tools for ICU management, ultimately enhancing patient care and optimizing ICU operations. The findings could lead to the integration of AI models into clinical decision support systems, facilitating more accurate and efficient patient management in the ICU.

Conditions

Artificial Intelegence

Keywords

clinical decission; artificial intelegence; intensive care; discharge

Outcome Measures

Primary Outcomes (1)

• accuracy

  The primary outcome is the accuracy of the AI models (ChatGPT and Gemini) in predicting the optimal timing for ICU discharge. Accuracy will be measured by comparing the AI models\' predictions with the actual decisions made by ICU specialists. This outcome will be quantified using statistical metrics such as sensitivity, specificity, and the area under the ROC curve (AUC).

  1 year

Secondary Outcomes (2)

• models comparison

  1 year

• utilzation

  1 year

Study Arms (2)

Decision from Experts

Decisions maded by Intensive Care doctors

Other: decision

Decision from AI

Decisions maded by Artificial intelegence models

Other: decision

Interventions

decision OTHER

decision-making process regarding patient discharge from the Intensive Care Unit (ICU)

Decision from AI; Decision from Experts

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

The study population will consist of adult patients admitted to the Intensive Care Unit (ICU) at a tertiary care hospital during the 12-month study period. This hospital is a high-acuity facility that serves a diverse population, including patients from both urban and rural areas. The ICU is equipped with advanced medical technologies and staffed by experienced healthcare professionals, making it an ideal setting for evaluating the effectiveness of AI models in clinical decision-making.

You may qualify if:

• Patients aged 18 years or older.
• Patients currently admitted to the Intensive Care Unit (ICU) during the study -period.
• Patients with sufficient clinical data available in the hospital\'s information system, including demographic information, clinical indicators, and treatment history.
• Patients for whom a discharge decision (to a general ward or home) needs to be made during their ICU stay.

You may not qualify if:

• Patients younger than 18 years old. Patients with incomplete or insufficient clinical data in the hospital\'s information system, making it difficult to assess their condition accurately.
• Patients who are in the ICU for palliative care or end-of-life care, where discharge to a general ward or home is not anticipated.
• Patients who have opted out of participating in the study or whose legal representatives have declined participation.

Sponsors & Collaborators

• Kanuni Sultan Suleyman Training and Research Hospital: lead

Study Sites (1)

Health Science University İstanbul Prof Dr Cemil Taşcıoğlu City Hospital

Istanbul, Küçükçekmece, 34303, Turkey (Türkiye)

Location

Study Officials

• Engin ihsan turan

  Health Science University İstanbul Kanuni Sultan Süleyman Education and Training Hospital

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: September 3, 2024
• First Posted: September 5, 2024
• Study Start: September 15, 2024
• Primary Completion: May 26, 2025
• Study Completion: May 27, 2025
• Last Updated: May 31, 2025

Record last verified: 2025-05

Locations

TU (1)