Clinical Research Directory

Clinical Validation of an Artificial Intelligence System for the Interpretation of Laboratory Test Results Combined With a Structured Medical History

This study evaluates a certified artificial intelligence (AI) based software system that automatically interprets laboratory test results in combination with a structured, dynamically generated medical history questionnaire. The purpose of the study is to determine how accurately and how safely the system assigns a patient to a category of urgency of medical contact, and how closely the interpretations produced by the system correspond to the assessment of an expert physician. Adults treated at two university hospitals in Katowice, Poland, who are referred for laboratory testing, complete an electronic medical history questionnaire after their laboratory results become available. The system then generates an interpretation for each laboratory result, including a category of urgency of medical contact, a suggested medical specialty and suggested further laboratory tests. The attending physician independently records a clinical assessment of the same laboratory results and medical history without access to the output of the system. An independent expert physician subsequently establishes the reference assessment, blinded to the authorship of the assessments being compared. The interpretation report is released to the participant only after the assessment of the attending physician has been recorded and locked. The study is conducted in two phases. The first phase (609 participants) is exploratory and uses the initial version of the system. The second phase (290 participants) is confirmatory and uses the final, frozen version of the system. All outcome measures are assessed in both phases; the pre-specified confirmatory hypothesis test applies to the second phase. The endpoints of this study are properties of the software system, namely the concordance of its output with an expert reference assessment. Health outcomes of participants are not measured.

Optimization of Low-Value Medical Testing Practices in Hospitalized Patients in Argentina

Overuse of laboratory testing in hospitalized patients is a common form of low-value care and may contribute to patient discomfort, unnecessary downstream testing, inefficient use of healthcare resources, and increased costs. Although diagnostic stewardship interventions have reduced unnecessary laboratory testing in several high-income settings, evidence from Latin America remains limited, particularly regarding implementation processes and sustainability. OPTIMIZAR is a multicenter, effectiveness-implementation hybrid type 2 study evaluating a multicomponent diagnostic stewardship intervention in internal medicine wards and intensive care units across public and private tertiary hospitals in Argentina. The intervention targets laboratory ordering practices through workflow redesign, operational support, clinician education, and audit and feedback, with local leadership teams supporting implementation at each site. Effectiveness will be evaluated using an interrupted time series design with aggregated unit-level data collected before and after implementation. The primary outcome is monthly laboratory testing intensity, measured as Argentine Standardized Biochemical Units per occupied bed-day. Secondary outcomes include in-hospital mortality and hospital length of stay. Implementation outcomes will be assessed descriptively using the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework.