Gastroenterology and Artificial Intelligence: 4th Annual Global Artificial Intelligence Summit | August 2022-Advances in AI to Improve Endoscopic Efficiency

Pdf Summary

In this document, Dr. Shyam Thakkar discusses the importance of efficiency in endoscopic units and the potential of artificial intelligence (AI) to optimize performance. The document begins by highlighting the rising healthcare costs in the United States and the significant number of procedures performed annually, including colonoscopies, EGDs, and ERCP procedures. The author emphasizes the need for improved quality indicators in endoscopy, given the high medical error rates and variation.<br /><br />Efficiency is defined as the use of resources to maximize the production of goods and services. It is identified as a key element of quality care delivery by the National Academy of Sciences Institute of Medicine. Improving efficiency enhances patients' experiences, employee workplace satisfaction, and patient access to necessary endoscopic procedures.<br /><br />Challenges to efficiency in endoscopic units include limited resources such as staff, facilities, equipment, and time. Academic medical centers often experience delays in procedures due to physician unavailability and patient flow processes. Measuring efficiency involves factors such as patient preparation, procedure time, and discharge.<br /><br />The document suggests several AI options to optimize performance, including the use of intelligent optimization systems, integration with endoscope systems and electronic health records (EHR), and the use of sensors to track patient and bed locations and scope utilization. A digital twin of the endoscopy suite is proposed as a way to capture unbiased workflow data automatically.<br /><br />The document also discusses the potential of AI in optimizing report generation and revenue cycling. Examples include automating the extraction of key terms from medical records using natural language processing (NLP) and voice annotation for real-time documentation.<br /><br />AI applications in endoscopy extend to automated detection of adenoma detection rate (ADR), cecal intubation, and bowel preparation. Deep learning models and neural networks have shown promise in accurately classifying images and determining the adequacy of bowel preparation.<br /><br />In conclusion, the document emphasizes the importance of efficiency in endoscopic units and the potential of AI to optimize performance. AI can help measure workflow deficiencies, automate tasks, and standardize endoscopic completion. NLP models can be used for report generation, while AI algorithms can assist in identifying ADR and assessing bowel preparation. Overall, AI offers opportunities for improved efficiency and better patient outcomes in endoscopy.

Asset Subtitle

Shyam Thakkar, MD

Keywords

efficiency

endoscopic units

artificial intelligence

optimization

limited resources

workflow data

report generation

adenoma detection rate

bowel preparation

patient outcomes