Preference Card Optimization—Promise and Practical Considerations

Perez and colleagues demonstrate substantial opportunity for cost savings through systematic preference card optimization, reporting over $1.1 million in reduced waste across 3 surgical specialties over 5 months. Their machine learning approach offers an important advancement: a scalable, reproducible method that could address the pervasive problem of preference card inaccuracy. The authors acknowledge that manual review remains the gold standard while proposing automated approaches as a practical alternative given resource constraints that make manual review prohibitively time intensive.