Processing of Routine Clinical Data Using Large Language Models Such As GPT-4 - Trial NCT06410547
Access comprehensive clinical trial information for NCT06410547 through Pure Global AI's free database. This phase not specified trial is sponsored by Charite University, Berlin, Germany and is currently Not yet recruiting. The study focuses on COPD. Target enrollment is 70 participants.
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Study Focus
Sponsor & Location
Charite University, Berlin, Germany
Timeline & Enrollment
N/A
May 15, 2024
Sep 30, 2024
Primary Outcome
Adherence to treatment guidelines at the time of hospital discharge,Percentage of patients treated in concordance with treatment guidelines at the time of hospital discharge
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.
ICD-10 Classifications
Data Source
ClinicalTrials.gov
NCT06410547
Non-Device Trial

