Using Large Language Models Such As GPT-4 to Assess Guideline Adherence in Patients With Chronic Obstructive Pulmonary Disease

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.

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Key information

Conditions

COPD Chronic Disease Disease Attributes Lung Diseases Lung Diseases, Obstructive Pathologic Processes Pathological Conditions, Signs and Symptoms Pulmonary Disease, Chronic Obstructive Respiratory Tract Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Charité University

Berlin, State of Berlin, 10117, Germany

Who can participate

Healthy volunteers accepted: No

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

Inclusion criteria

Diagnosis of COPD
Consent
Discharge after hospitalization

Exclusion criteria

Lack of Consent

Treatment and study plan

LLM

Other

A LLM-based comparison between treatment and guideline.

Primary outcomes

Adherence to treatment guidelines at the time of hospital discharge

Time frame: From date of admission (which is enrollment) to the date of discharge, assessed up to one month

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.
Percentage of patients treated in concordance with treatment guidelines at the time of hospital discharge

Time frame: From date of admission (which is enrollment) to the date of discharge, assessed up to one month

This primary endpoint will assess the percentage of guideline-concordant treatments in each study arm.

Sponsors and collaborators

Lead sponsor

Charite University, Berlin, Germany

Other

Registry information

Acronym: IMPL-AI-MENT

Important dates

Study start: 2024
Primary completion: 2025
Study completion: 2025
First posted: May 13, 2024
Registry last updated: Mar 20, 2026

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.

Completed