Improving Quality and Safety In Your Endoscopy Unit: On-Demand | October 2022 OLQUALOCT22-Approaching Automation of Data Collection and Data Analysis

Video Transcription

Video Summary

The video discussed the challenges of automating data collection and analysis in the field of endoscopy. It emphasized the need for quality metrics to improve patient care and outcomes. The limitations of data collection were highlighted, including the requirement for a large volume of procedures to accurately measure procedural quality. The video also mentioned the difficulties in calculating metrics and providing actionable feedback. It discussed the limitations of outcome metrics and the infeasibility of measuring quality in low-volume procedures. The importance of measuring outcomes using automated processes and the use of data registries and warehouses were also mentioned. The video concluded with a discussion on the potential of artificial intelligence (AI) in improving quality assessment and providing feedback. It highlighted the use of AI in polyp detection, classification, and evaluating skills during procedures. The video suggested that AI systems could automate data collection and reduce the barriers to measuring quality metrics in endoscopy. The speaker encouraged further education on AI and its potential impact on improving patient care. No specific credits were mentioned.

Asset Subtitle

Raj Keswani, MD MS

Keywords

automating data collection

endoscopy

quality metrics

patient care

artificial intelligence

improving outcomes