Assessing Clinical Impact of AI for Iron Deficiency
Trial statusRecruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age18+
SponsorChina Medical University Hospital
About this trial
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 criteria
This trial does not accept healthy volunteersQualifiers
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.
Disqualifiers
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.
Trial design
Design model
Parallel
Treatments tested in this trial
AI Risk Display
Other interventionParticipants in this group receive AI-generated information showing their high or low risk of iron deficiency to assist clinical decision-making. For participants identified as high-risk, the system automatically checks for iron-related tests performed in the past 30 days and alerts the physician if no recent tests are found. The final decision to order any tests remains with the physician.
Treatment groups
2,196 Participants
are divided into 2 treatment groupsGroup A: AI displayExperimental treatment 1 intervention
Group B: ControlNo intervention 0 interventions
Trial outcomes
Primary outcomes
1
Detection Rate of Iron Deficiency
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.
Time frame
Within 1 month after the complete blood count report is availableSecondary outcomes
1
Iron Deficiency Detection Rate in Patients With Anemia
The proportion of patients with anemia who are subsequently identified as having iron deficiency based on standard iron-related laboratory tests ordered during routine clinical care.
Time frame
Within 1 month after the complete blood count report becomes available2
Iron Deficiency Detection Rate in Patients Without Anemia
The proportion of patients without anemia who are identified as having iron deficiency based on standard iron-related laboratory tests ordered during routine clinical care.
Time frame
Within 1 month after the complete blood count report becomes available3
Rate of iron-related laboratory testing for suspected iron deficiency
The proportion of eligible encounters in which iron-related laboratory testing is ordered with the indication "to confirm iron deficiency" within 1 month after the CBC report becomes available.
Time frame
Within 1 month after the complete blood count (CBC) report becomes available4
Incremental number of confirmed iron deficiency diagnoses per additional iron-related laboratory test attributable to AI-assisted decision support
The incremental diagnostic yield was defined as the difference in the number of confirmed iron deficiency cases between the AI-assisted group and the control group, divided by the difference in the number of iron-related laboratory tests ordered between the two groups. This measure reflects the average additional number of diagnoses obtained per additional test attributable to AI-assisted risk prompting.
Time frame
Within 1 month after the complete blood count (CBC) report becomes availableSponsors and contacts
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Source ClinicalTrials.gov