Using Large Language Models Such As GPT-4 to Assess Guideline Adherence in Patients With Chronic Obstructive Pulmonary Disease (IMPL-AI-MENT)
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.
Study Overview
Study Type
Enrollment (Actual)
Phase
- Not Applicable
Contacts and Locations
Study Locations
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State of Berlin
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Berlin, State of Berlin, Germany, 10117
- Charite University
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Description
Inclusion Criteria:
- Diagnosis of COPD
- Consent
- Discharge after hospitalization
Exclusion Criteria:
- Lack of Consent
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Treatment
- Allocation: Randomized
- Interventional Model: Parallel Assignment
- Masking: Triple
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
|---|---|
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No Intervention: No Intervention
This arm will be treated as usual.
No intervention will be performed.
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Active Comparator: LLM Assessement
During hospital stay and after written informed consent, an LLM is asked to indicate if the treatment the patient receives is guideline-concordant.
The information is ascertained by two study physicians (human-in-the-loop) and later provided to the treating physician who can recommend a change in therapy to the patient (outside of the study).
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A LLM-based comparison between treatment and guideline.
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What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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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
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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.
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From date of admission (which is enrollment) to the date of discharge, assessed up to one month
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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
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This primary endpoint will assess the percentage of guideline-concordant treatments in each study arm.
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From date of admission (which is enrollment) to the date of discharge, assessed up to one month
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Collaborators and Investigators
Investigators
- Principal Investigator: Matthias Gröschel, MD PhD, Charite
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- EA2/322/23
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
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