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Evaluation of the Clinical Impact of Machine Learning-Based Risk Classification Using Blood Analysis on Iron Deficiency Detection

Status: Recruiting
Location: See location...
Intervention Type: Other
Study Type: Interventional
Study Phase: Not Applicable
SUMMARY

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.

Eligibility
Participation Requirements
Sex: All
Minimum Age: 18
Healthy Volunteers: f
View:

• 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.

Locations
Other Locations
Taiwan
China Medical University Hospital
RECRUITING
Taichung
Time Frame
Start Date: 2026-04-01
Estimated Completion Date: 2027-03
Participants
Target number of participants: 2196
Treatments
Experimental: AI display
No_intervention: Control
Related Therapeutic Areas
Sponsors
Leads: China Medical University Hospital

This content was sourced from clinicaltrials.gov