LOINC-Related Clinical Terminology Management in an Academic Medical Center Using Human in the Loop Machine Learning

Objective: Many institutions have reported poor mapping of laboratory results to Logical Observation Identifiers Names and Codes (LOINC). A human in the loop machine learning approach for clinical terminology management may help. Methods: Since 2002 Weill Cornell Medicine has maintained TruData, an application between reference laboratories and the electronic health record (EHR) system that predicts LOINC for unknown results using Bayesian networks among other techniques. Staff have used TruData to adjudicate LOINC predictions and manage EHR clinical content. Results: From 2002 to 2025 TruData identified 244,000 unique laboratory result concepts and enabled LOINC determination for 193,000 (79%). As of 2025 the EHR system contained 50,000 unique laboratory result components, 42,000 (84%) of which had a LOINC assignment. The EHR team used TruData to maintain results and procedures, making available in the EHR the subset of TruData content observed clinically. Reasons for result components lacking LOINC included LOINC not yet available (i.e., new result) and components not warranting LOINC (e.g., “Comment:”, “Reflex Information”). Discussion: This case report is among the first to describe human in the loop machine learning for clinical terminology management in use for more than 20 years at an academic medical center. Conclusion: Practitioners elsewhere may find human in the loop machine learning and TruData valuable for clinical terminology management.