Evaluation of the Clinical Impact of Machine Learning-Based Risk Classification Using Blood Analysis on Iron Deficiency Detection
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 2,196
- 试验地点
- 1
- 主要终点
- Detection Rate of Iron Deficiency
研究概览
简要总结
The goal of this clinical trial is to evaluate whether an AI-based risk notification system integrated into routine clinical care can improve the clinical detection of iron deficiency in adult patients attending Internal Medicine, Family Medicine, and Hematology/Oncology clinics at China Medical University Hospital in Taiwan.
The main questions this study aims to answer are:
- Does displaying AI-generated iron deficiency risk classification to physicians increase the overall detection rate of iron deficiency at the population level?
- Does the AI-based risk notification influence physicians' diagnostic behavior by increasing the rate at which ferritin testing is ordered specifically for suspected iron deficiency?
- Among ferritin tests ordered for suspected iron deficiency, does the diagnostic yield (positivity rate) remain appropriate, reflecting efficient use of testing resources?
- Are the effects of the AI-assisted intervention consistent among patients with anemia and without anemia?
Comparison Groups Researchers will compare clinical encounters in which physicians receive AI-generated iron deficiency risk information (the Prompt Group) with encounters in which physicians receive standard laboratory results without AI risk display (the Control Group). The comparison focuses on differences in iron deficiency detection, ferritin ordering behavior for suspected iron deficiency, and diagnostic yield.
What Participants Will Experience
- No Additional Procedures:
As this is a pragmatic study embedded in routine clinical care, participants will not undergo any additional blood draws, invasive procedures, or clinic visits beyond standard care. 2. Routine Care Only:
Patients attend their scheduled outpatient visits and receive complete blood count (CBC) testing as ordered by their treating physician, independent of study participation. 3. Background Data Integration:
The AI system operates within the hospital's information system, analyzing routinely collected CBC data after results become available. No additional data entry or action is required from patients. 4. Physician Autonomy Preserved:
The AI provides a non-mandatory risk classification as decision support. For patients identified as high risk, the system may display an informational prompt suggesting consideration of iron-related testing if no recent testing is found. All diagnostic and management decisions remain entirely at the discretion of the treating physician.
详细描述
Detailed Description: Machine Learning-Based Risk Notification for Iron Deficiency
- Study Overview and Design This is a single-center, prospective, pragmatic randomized controlled trial (pRCT) conducted at China Medical University Hospital (CMUH) in Taiwan. The study aims to evaluate the clinical impact of a machine learning-based clinical decision support tool on iron deficiency detection and related diagnostic behavior in a real-world outpatient setting.
Unlike traditional explanatory trials with restrictive protocols, this pragmatic design integrates the intervention directly into the existing Electronic Health Record (EHR) system. 2. Participant Population
The study includes adult patients (aged 18 years or older) receiving outpatient care in the following departments at CMUH:
- Division of General Internal Medicine
- Department of Family Medicine
- Division of Hematology and Oncology
All eligible outpatient encounters in which a routine complete blood count (CBC) test is performed as part of standard clinical care are included. No additional tests or procedures are required for study participation. 3. AI Intervention and Clinical Workflow
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Health Services Research
- 盲法
- Double (Participant, Outcomes Assessor)
盲法说明
Participants and outcome assessors are masked to group assignment. Care providers are not masked due to the nature of the intervention.
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
- •Adults aged 18 years or older.
- •Patients attending outpatient clinics of participating departments including 2.
- •Internal Medicine, 2.2 Family Medicine 2.
- •Hematology/Oncology
- •Completion of a routine complete blood count (CBC) as part of usual clinical care during the outpatient encounter.
- •Availability of the CBC report in the institutional laboratory information system, allowing sufficient data for analysis.
排除标准
- •Encounters with missing or incomplete key identifiers (e.g., patient identification number, ) that prevent determination of exposure status (AI information displayed vs not displayed) or assessment of outcomes within the defined follow-up period.
- •Encounters without a valid department code required by the information system to trigger the randomization mechanism and AI risk display.
- •Encounters with incomplete or missing laboratory data required to activate the AI system.
- •Repeated CBC encounters from the same patient during the study period, if applicable; only the first eligible encounter will be included to avoid duplication.
研究组 & 干预措施
Control
AI display
干预措施: AI Risk Display (Other)
结局指标
主要结局
Detection Rate of Iron Deficiency
时间窗: Within 1 month after the complete blood count report is available
The primary outcome is the proportion of patients with laboratory-confirmed iron deficiency identified during routine clinical care. Iron deficiency is determined based on routine iron-related laboratory tests, such as ferritin, as ordered by the treating physician according to usual clinical practice. The detection rate is calculated as the number of patients diagnosed with iron deficiency divided by the total number of patients undergoing complete blood count testing during the study period.
次要结局
- Iron Deficiency Detection Rate in Patients With Anemia(Within 1 month after the complete blood count report becomes available)
- Iron Deficiency Detection Rate in Patients Without Anemia(Within 1 month after the complete blood count report becomes available)
- Rate of iron-related laboratory testing for suspected iron deficiency(Within 1 month after the complete blood count (CBC) report becomes available)
- Incremental number of confirmed iron deficiency diagnoses per additional iron-related laboratory test attributable to AI-assisted decision support(Within 1 month after the complete blood count (CBC) report becomes available)
- Incremental cost-effectiveness(Within 1 month after the complete blood count report is available)
研究者
Yu-Hsin Chang
Attending physician
China Medical University Hospital