Making Use of Natural Language Processing to Better Understand Medical Students' Self-Assessment of Clinical Skills.

Problem: Reflective practice is necessary for self-regulated learning. Helping medical students develop these skills can be challenging since they are difficult to observe. One common solution is to assign students' reflective self-assessments, which produce large quantities of narrative assessment data.

Improving Care Delivery: Location Timestamps to Enhance Process Measurement of a Clinical Workflow.

Unlabelled: Traditional quality improvement (QI) strategies to describe workflow processes rely primarily upon qualitative methods or human-driven observations. These methods may be limited in scope and accuracy when applied to time-based workflow processes. This study sought to evaluate the utility of integrating objective time measurements to augment traditional QI strategies using procedural sedation workflow in a pediatric emergency department as an archetype.

Machine Learning for Detection of Correct Peripherally Inserted Central Catheter Tip Position from Radiology Reports in Infants.

Background: In critically ill infants, the position of a peripherally inserted central catheter (PICC) must be confirmed frequently, as the tip may move from its original position and run the risk of hyperosmolar vascular damage or extravasation into surrounding spaces. Automated detection of PICC tip position holds great promise for alerting bedside clinicians to noncentral PICCs.

Objectives: This research seeks to use natural language processing (NLP) and supervised machine learning (ML) techniques to predict PICC tip position based primarily on text analysis of radiograph reports from infants with an upper extremity PICC.