Using Large Language Models Such As GPT-4 to Assess Guideline Adherence in Patients With Chronic Obstructive Pulmonary Disease
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 78
- 试验地点
- 1
- 主要终点
- Percentage of patients treated in concordance with treatment guidelines at the time of hospital discharge
研究概览
简要总结
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Treatment
- 盲法
- Triple (Participant, Care Provider, Outcomes Assessor)
入排标准
- 年龄范围
- 18 Years 至 —(Adult, Older Adult)
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Diagnosis of COPD
- •Discharge after hospitalization
排除标准
- •Lack of Consent
结局指标
主要结局
Percentage of patients treated in concordance with treatment guidelines at the time of hospital discharge
时间窗: 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.
Adherence to treatment guidelines at the time of hospital discharge
时间窗: 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.
次要结局
未报告次要终点
研究者
Matthias Gröschel
Principal Investigator
Charite University, Berlin, Germany
