Assessing Clinical Impact of AI for Iron Deficiency

NCT07394088 | Not Yet Recruiting

• 1 other identifier: CMUH115-REC2-004
• Study Type: interventional
• Target: 2,196
• Locations: 1 country
• Sites: 1

Brief 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. 1.Does displaying AI-generated iron deficiency risk classification to physicians increase the overall detection rate of iron deficiency at the population level?
2. 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. 3.Among ferritin tests ordered for suspected iron deficiency, does the diagnostic yield (positivity rate) remain appropriate, reflecting efficient use of testing resources?
4. 4.Are the effects of the AI-assisted intervention consistent among patients with anemia and without anemia?
5. 5.No Additional Procedures:
6. 6.Routine Care Only:
7. 7.Background Data Integration:
8. 8.Physician Autonomy Preserved:

Conditions

Iron Deficiency; Iron Deficiency Anemias

Keywords

machine learning; complete blood count; iron deficiency; pragmatic randomized trial; detection rate

Outcome Measures

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.

  Within 1 month after the complete blood count report is available

Secondary Outcomes (5)

• 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

Study Arms (2)

AI display

EXPERIMENTAL

Other: AI Risk Display

Control

NO INTERVENTION

Interventions

AI Risk Display OTHER

Participants 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.

AI display

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

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.

Sponsors & Collaborators

• China Medical University Hospital: lead

Study Sites (1)

China Medical University Hospital

Taichung, 404, Taiwan

Location

MeSH Terms

Conditions

Iron Deficiencies; Anemia, Iron-Deficiency

Condition Hierarchy (Ancestors)

Iron Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Anemia, Hypochromic; Anemia; Hematologic Diseases; Hemic and Lymphatic Diseases

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: DOUBLE
• Who Masked: PARTICIPANT, OUTCOMES ASSESSOR
• Masking Details: Participants and outcome assessors are masked to group assignment. Care providers are not masked due to the nature of the intervention.
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Attending physician

Model Details: This is a pragmatic, randomized, parallel-assignment interventional study. Participants are randomly allocated to parallel groups to evaluate the intervention under real-world clinical conditions.

Study Record Dates

• First Submitted: January 30, 2026
• First Posted: February 6, 2026
• Study Start: March 1, 2026
• Primary Completion (Estimated): August 1, 2026
• Study Completion (Estimated): January 1, 2027
• Last Updated: February 11, 2026

Record last verified: 2026-02

Locations

TW (1)