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This research applied machine learning to develop a model predicting surgical cancellations among pediatric patients, and found the feasibility in using these algorithms as a cost-effective quality-improvement measure.
This project integrated a validated anxiety-specific screening tool in an existing clinical decision support system and tested it with a randomized feasibility pilot that found the tool did not increase detection of anxiety in pediatric primary care.