Processing of Routine Clinical Data Using Large Language Models Such As GPT-4

COPD Clinical Trial

— IMPL-AI-MENT  

Official title:

Processing of Routine Clinical Data Using Large Language Models Such As GPT-4

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06410547
• Other study ID #: EA2/322/23
• Status: Not yet recruiting
• Phase: N/A
• Start date: May 15, 2024
• Est. completion date: September 30, 2024

Study information

• Verified date: May 2024
• Source: Charite University, Berlin, Germany
• Contact: Matthias Gröschel, MD PhD
• Phone: +4915906714937
• Email: matthias.groeschel[@]charite.de
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

According to studies in the US and the Netherlands, 33-40% of patients with chronic conditions receive care that does not follow guideline recommendations. These findings have also been demonstrated in the management of COPD. This leads to under- or over-treatment of patients and, in the case of COPD, to exacerbations and hospitalisations. These exacerbations are a significant clinical problem, affecting patient's lung function, quality of life and mortality. They are also a burden on the healthcare system. Technological advances in artificial intelligence offer the opportunity to address these issues in COPD management. In the past year, there have been remarkable innovations in the field of natural language processing, especially through large language models such as GPT-4 from OpenAI and Bard or Gemini from Google. These models offer an opportunity to improve the implementation of evidence-based care in clinical practice.

This study is a prospective, randomised trial that will compare therapy on discharge for patients with COPD. One arm will receive no intervention, while the other arm will receive a treatment recommendation from an LLM. The study will compare the percentage of patients treated according to the guideline.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 70
• Est. completion date: September 30, 2024
• Est. primary completion date: September 30, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Diagnosis of COPD

• Consent

• Discharge after hospitalization

• availabiltiy of eosinophil count

Exclusion Criteria:

• Lack of Consent

Related Conditions & MeSH terms

• COPD

Intervention

Other:
• LLM
A LLM-based comparison between treatment and guideline will, if necessary, give treatment recommendations based on the guidline.

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
Charite University, Berlin, Germany

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Adherence to treatment guidelines at the time of hospital discharge	The primary endpoint will assess whether the treatment at the time of discharge is consistent with the guidelines' recommendations. This is a binary outcome measure of yes or no.	From date of admission (which is enrollment) to the date of discharge, assessed up to one month
Primary	Percentage of patients treated in concordance with treatment guidelines at the time of hospital discharge	This primary endpoint will assess the percentage of guideline-concordant treatments in each study arm.	From date of admission (which is enrollment) to the date of discharge, assessed up to one month