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Using the Electronic Health Record To Identify Children Likely To Suffer Last-Minute Surgery Cancellation
Description:
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.
Principal Investigator:
Project Dates:
September 1, 2016 to August 31, 2019
Improving Anxiety Detection in Pediatrics Using Health Information Technology
Description:
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.
Principal Investigator:
Project Dates:
September 1, 2015 to August 31, 2018
