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

Inclusion Criteria

Inclusion Criteria: 1. Adults aged 18 years or older. 2. Patients attending outpatient clinics of participating departments including 2.1. Internal Medicine, 2.2 Family Medicine 2.3. Hematology/Oncology 3. Completion of a routine complete blood count (CBC) as part of usual clinical care during the outpatient encounter. 4. Availability of the CBC report in the institutional laboratory information system, allowing sufficient data for analysis. Exclusion Criteria: 1. 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. 2. Encounters without a valid department code required by the information system to trigger the randomization mechanism and AI risk display. 3. Encounters with incomplete or missing laboratory data required to activate the AI system. 4. Repeated CBC encounters from the same patient during the study period, if applicable; only the first eligible encounter will be included to avoid duplication.