Assessing Clinical Impact of AI for Iron Deficiency
Recruiting now · Not applicable
Conditions studied: Iron Deficiency, Iron Deficiency Anemias
In brief
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: 1. Does displaying AI-generated iron deficiency risk classification to physicians increase the overall detection rate of iron deficiency at the population level? 2. 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? 3. Among ferritin tests ordered for suspected iron deficiency, does the diagnostic yield (positivity rate) remain appropriate, reflecting efficient use of testing resources? 4. 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 1. 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.
Key facts
- Study ID
- NCT07394088
- Run by
- China Medical University Hospital
- People needed
- 2196
- Starts
- 2026-04-01
- Expected to finish
- 2027-03-01
- Last updated by the study team
- 2026-05-26
Who can join
Age: 18 and older. Sex: any. Healthy volunteers: not accepted.
You may qualify if…
- Adults aged 18 years or older.
- Patients attending outpatient clinics of participating departments including 2.1. Internal Medicine, 2.2 Family Medicine 2.3. 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.
You may not qualify if…
- 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.
Where it is running
- China Medical University Hospital — Taichung, Taiwan (enrolling)
Full record on ClinicalTrials.gov
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