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GenAI-Assisted Rheumatoid Arthritis Bone Erosion Risk Assessment and DMARD Selection (RABER)

Prospective Evaluation of Generative AI-Augmented Clinical Decision-Making for Rheumatoid Arthritis Bone Erosion Risk (RABER) Assessment and DMARD Selection in Routine Care

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by progressive joint damage and bone erosion. Accurate assessment of RA bone erosion risk (RABER) and appropriate disease-modifying antirheumatic drug (DMARD) selection are critical to preventing irreversible disability.

Recent advances in generative artificial intelligence (GenAI), particularly large language models (LLMs), enable integration of heterogeneous clinical data and may improve clinical decision-making. However, there is limited real-world evidence (RWE) evaluating whether GenAI can meaningfully augment physician decision-making in routine care.

This prospective, single-arm, nonrandomized interventional study evaluates the feasibility and impact of GenAI-assisted clinical decision support on physician assessment of rheumatoid arthritis bone erosion risk and disease-modifying antirheumatic drug selection in routine inpatient care. Treating physicians document their initial risk assessments and treatment decisions before reviewing patient-specific GenAI reports and subsequently record their assessments and decisions after reviewing the reports. The study evaluates changes in physician clinical decision-making within a learning health system framework. Final treatment decisions remain under the responsibility of the treating physicians. The study does not evaluate long-term patient clinical outcomes.

研究概览

地位

招聘中

条件

干预/治疗

研究类型

介入性

注册 (估计的)

100

阶段

  • 不适用

联系人和位置

本节提供了进行研究的人员的详细联系信息,以及有关进行该研究的地点的信息。

学习联系方式

学习地点

    • Beijing Municipality
      • Beijing、Beijing Municipality、中国、100053
        • 招聘中
        • Guang'anmen Hospital of China Academy of Chinese Medical Sciences
        • 接触:
        • 首席研究员:
          • Quan Jiang, MD

参与标准

研究人员寻找符合特定描述的人,称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。

资格标准

适合学习的年龄

18年 及以上 (成人、年长者)

接受健康志愿者

不

有资格学习的性别

全部

描述

Inclusion Criteria:

  • Newly diagnosed RA patients meeting American College of Rheumatology / European League Against Rheumatism 2010 classification criteria
  • Consecutive inpatients admitted during study period

Exclusion Criteria:

  • Patients subsequently determined not to have RA
  • Incomplete clinical data preventing risk or treatment assessment

学习计划

本节提供研究计划的详细信息,包括研究的设计方式和研究的衡量标准。

研究是如何设计的?

设计细节

  • 主要用途:卫生服务研究
  • 分配:不适用
  • 介入模型:单组作业
  • 屏蔽:无(打开标签)

武器和干预

参与者组/臂
干预/治疗
实验性的:GenAI-Assisted RA Bone Erosion Risk Assessment and DMARD Selection

This arm will include at least 100 consecutive adult inpatients with rheumatoid arthritis (RA) receiving routine clinical care. For each patient, the treating physician will first independently assess bone erosion risk and select a disease-modifying antirheumatic drug (DMARD) treatment strategy based on available clinical information (pre-AI assessment). The physician will then review a GenAI-generated clinical decision support report containing predicted bone erosion risk levels and DMARD recommendations and record an updated risk assessment and treatment decision (post-AI assessment). Pre-AI and post-AI physician decisions will be compared against independent expert reference standards to evaluate the impact of GenAI-assisted decision support. Treating physicians retain full responsibility for final clinical decisions.

The intervention consists of a GenAI-generated clinical decision support report provided to treating physicians during routine RA care. Using patient clinical information available at the time of assessment, the GenAI system generates predicted bone erosion risk probabilities (0-100%), risk levels (0-3), and DMARD treatment recommendations. Physicians review the report after completing their independent pre-AI assessments and may revise their risk assessments and treatment decisions based on the additional information. The GenAI system provides decision support only and does not autonomously make or implement treatment decisions.

研究衡量的是什么?

主要结果指标

结果测量
措施说明
大体时间
Percentage of Participants With a Change in Physician-Assessed Rheumatoid Arthritis Bone Erosion Risk Level After GenAI-Assisted Decision Support
大体时间:During index hospitalization, at pre-AI and post-AI physician assessments (approximately 1-7 days)

The percentage of participants for whom the treating physician changes the rheumatoid arthritis bone erosion risk level after reviewing the GenAI-generated clinical decision support report, compared with the initial assessment before reviewing the report. Risk levels are categorized from 0 to 3. A change is defined as any difference between the pre-AI and post-AI risk levels. The percentage is calculated using participants with complete paired assessments.

During index hospitalization, at pre-AI and post-AI physician assessments (approximately 1-7 days)
Cohen's Kappa Agreement Between Physician-Assessed Rheumatoid Arthritis Bone Erosion Risk Levels and the Reference Standard Before and After GenAI-Assisted Decision Support
大体时间:During index hospitalization, at pre-AI and post-AI physician assessments (approximately 1-7 days)

Agreement between physician-assessed rheumatoid arthritis bone erosion risk levels (0-3) and the reference standard will be evaluated separately before and after review of the GenAI report using Cohen's kappa coefficient. The pre-AI and post-AI kappa coefficients will be compared to assess changes in agreement following GenAI-assisted decision support. Cohen's kappa ranges from -1 to 1, with higher values indicating greater agreement beyond chance. The difference between post-AI and pre-AI kappa coefficients will be reported, with positive values indicating improved agreement. The reference classification will be established during the data analysis stage by three independent expert physicians who are not involved in patient treatment, with disagreements resolved through consensus.

During index hospitalization, at pre-AI and post-AI physician assessments (approximately 1-7 days)

次要结果测量

结果测量
措施说明
大体时间
Cohen's Kappa Agreement Between Physician DMARD Treatment Selection Categories and the Reference Standard After GenAI-Assisted Decision Support
大体时间:During index hospitalization, after physician review of the GenAI report (approximately 1-7 days)

Agreement between physician-selected disease-modifying antirheumatic drug (DMARD) treatment categories (0-4) and the reference standard will be assessed after physicians review the GenAI-generated clinical decision support report. Agreement will be quantified using Cohen's kappa coefficient, with higher values indicating greater agreement beyond chance. The analysis will include participants with complete post-AI physician treatment selections and corresponding reference standard classifications. The reference classification will be established during the data analysis stage by three independent expert physicians who are not involved in patient treatment, with disagreements resolved through consensus.

During index hospitalization, after physician review of the GenAI report (approximately 1-7 days)

合作者和调查者

在这里您可以找到参与这项研究的人员和组织。

赞助

研究记录日期

这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查,以确保它们在发布到公共网站之前符合特定的质量控制标准。

研究主要日期

学习开始 (实际的)

2026年3月1日

初级完成 (估计的)

2026年10月31日

研究完成 (估计的)

2026年12月31日

研究注册日期

首次提交

2026年10月21日

首先提交符合 QC 标准的

2026年10月28日

首次发布 (实际的)

2026年11月4日

研究记录更新

最后更新发布 (实际的)

2026年11月4日

上次提交的符合 QC 标准的更新

2026年10月28日

最后验证

2026年10月1日

更多信息

与本研究相关的术语

其他研究编号

  • 2026-667-KY-01-raber1
  • CI2023C019YL (其他赠款/资助编号:China Academy of Chinese Medical Sciences)
  • CI2026A01501 (其他赠款/资助编号:China Academy of Chinese Medical Sciences)
  • HLCMHPP2023087 (其他赠款/资助编号:China National Administration of Traditional Chinese Medicine)
  • 82230121 (其他赠款/资助编号:National Natural Science Foundation of China)

计划个人参与者数据 (IPD)

计划共享个人参与者数据 (IPD)?

不

药物和器械信息、研究文件

研究美国 FDA 监管的药品

不

研究美国 FDA 监管的设备产品

不

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