SmartAlert: Implementing Machine Learning-Driven Clinical Decision Support for Inpatient Lab Utilization Reduction

Repetitive laboratory testing unlikely to yield clinically useful information is a common practice that burdens patients and increases healthcare costs. Education and feedback interventions have limited success, while general test ordering restrictions and electronic alerts impede appropriate clinical care. We introduce and evaluate SmartAlert, a machine learning (ML)-driven clinical decision support (CDS) system integrated into the electronic health record that predicts stable laboratory results to reduce unnecessary repeat testing. This case study describes the implementation process, challenges, and lessons learned from deploying SmartAlert targeting complete blood count (CBC) utilization in a randomized controlled pilot across 9270 admissions in eight acute care units across two hospitals between August 15, 2024, and March 15, 2025. Results show significant decrease in number of CBC results within 52 hours of SmartAlert display (1.54 vs 1.82, p <0.01) without adverse effect on secondary safety outcomes, representing a 15% relative reduction in repetitive testing. Implementation lessons learned include interpretation of probabilistic model predictions in clinical contexts, stakeholder engagement to define acceptable model behavior, governance processes for deploying a complex model in a clinical environment, user interface design considerations, alignment with clinical operational priorities, and the value of qualitative feedback from end users. In conclusion, a machine learning-driven CDS system backed by a deliberate implementation and governance process can provide precision guidance on inpatient laboratory testing to safely reduce unnecessary repetitive testing.

April S. Liang, Fatemeh Amrollahi, Yixing Jiang, Conor K. Corbin, Grace Y.E. Kim, David Mui, Trevor Crowell, Aakash Acharya, Sreedevi Mony, Soumya Punnathanam, Jack McKeown, Margaret Smith, Steven Lin, Arnold Milstein, Kevin Schulman, Jason Hom, Michael A. Pfeffer, Tho D. Pham, David Svec, Weihan Chu, Lisa Shieh, Christopher Sharp, Stephen P. Ma, Jonathan H. Chen (1, 2, 9, and 12) __INSTITUTION_24__ Division of Hospital Medicine, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, (2) Department of Biomedical Data Science, Stanford University, Palo Alto, CA, (3) SmarterDx, New York, NY, (4) The Johns Hopkins University School of Medicine, Baltimore, MD, (5) Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, (6) Stanford Healthcare AI Applied Research Team, Stanford University School of Medicine, Palo Alto, CA, (7) Technology, Digital Solutions, Stanford Health Care, Palo Alto, CA, (8) Division of Primary Care, Population Health, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, (9) Clinical Excellence Research Center, Stanford University, Palo Alto, CA, (10) Department of Pathology, Stanford University School of Medicine, Palo Alto, CA, (11) Stanford Health Care Tri-Valley, Pleasanton, CA, (12) Stanford Center for Biomedical Informatics Research, Stanford University, Palo Alto, CA) · 2025