Multi-center Validation Study of a Large Language Model-based Intelligent Agent for Blood Cell Analysis
I. Study Background: Currently, in most medical institutions, the review of blood cell analysis still heavily relies on manual verification by laboratory staff. This process requires a comprehensive analysis of instrument parameters, alarm flags, historical comparison results, and, when necessary, microscopic examination. However, with the increasing volume of test samples and the high concentration of review tasks during peak hours, the traditional manual review model increasingly shows problems such as prolonged turnaround time (TAT), uneven workload distribution, and decreased consistency in reviews. In recent years, intelligent review systems based on Large Language Models (LLM) have shown potential in analyzing abnormal results and stratifying sample risks by integrating preset rules, clinical diagnostic information, and multi-dimensional laboratory data, which is expected to optimize the review workflow.
II. Study Objective: To evaluate the difference in overall sample review turnaround time between the experimental process and the control process during the formal study phase, and to test its superiority.
III. Subjects: The investigators need to recruit approximately 20,000 subjects, regardless of age or gender.
IV. Study Procedures: If participants agree to participate in the study, participants only need to allow us to use participants test results after participants have completed your routine blood test (CBC).
V. Risks and Benefits:
- Risks: This study poses no risk to the subjects. The investigators only use the result data of patients after participants have had their routine blood test; there is no need for patients to undergo additional blood draws.
- Benefits: It will shorten the turnaround time for routine blood test results and share the workload of doctors in reviewing these results.
VI. Privacy: All of participants information will be kept strictly confidential and will only be used for this scientific research.
研究概览
研究类型
注册 (估计的)
参与标准
资格标准
适合学习的年龄
- 孩子
- 成人
- 年长者
接受健康志愿者
取样方法
研究人群
描述
Inclusion Criteria:
- Subjects who underwent routine blood tests in the outpatient, emergency, or inpatient departments of the participating centers during the study period.
Corresponding samples must have complete instrument results, review trails, and report timestamp records.
Approved for inclusion by the Ethics Committee.
Exclusion Criteria:
- Samples collected during periods of instrument malfunction or interface transmission anomalies.
Missing key research data, particularly samples where the final review conclusion or key timestamps cannot be confirmed.
Subjects or their legal representatives explicitly refuse to participate in the study.
学习计划
研究是如何设计的?
设计细节
队列和干预
团体/队列 |
干预/治疗 |
|---|---|
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LLM-Assisted Review Group
|
This study introduces an intelligent auxiliary review system based on a medical Large Language Model (LLM), aimed at optimizing the traditional CBC report review process. The core functions and intervention mechanisms are as follows: Multi-source Data Integration: The system integrates seamlessly with the Laboratory Information System (LIS) to automatically retrieve patient demographics (age, sex), current CBC indices, historical results, and clinical diagnoses. Deep Analysis and Anomaly Detection: Unlike traditional rule-based auto-verification, this system leverages the reasoning capability of LLMs to perform multidimensional clinical logic checks. It identifies out-of-range values and interprets their clinical significance by combining them with patient history (e.g., distinguishing physiological fluctuations from pathological changes). |
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Standard Manual Review Group
|
研究衡量的是什么?
主要结果指标
结果测量 |
大体时间 |
|---|---|
|
Overall Report Turnaround Time
大体时间:one year
|
one year
|
合作者和调查者
研究记录日期
研究主要日期
学习开始 (估计的)
初级完成 (估计的)
研究完成 (估计的)
研究注册日期
首次提交
首先提交符合 QC 标准的
首次发布 (实际的)
研究记录更新
最后更新发布 (实际的)
上次提交的符合 QC 标准的更新
最后验证
更多信息
与本研究相关的术语
其他研究编号
- KY2026-084
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