Improving Quality and Safety In Your Endoscopy Unit: On-Demand | October 2022 OLQUALOCT22-04_Keswani

04_Keswani_Data Automation

Pdf Summary

This document discusses the current limitations of data collection and analysis in the field of endoscopy and proposes approaches to automate these processes. The limitations of quality metrics in endoscopy, such as the need for high volume and cumbersome calculations, are highlighted. The document emphasizes that lower volumes of procedures limit the metrics that can be measured. It also points out the challenges of measuring outcomes and the controversial nature of reporting them.<br /><br />Various methods for data collection and analysis are discussed, including manual chart review, data registries, data warehouses, natural language processing, and video assessment. The advantages and disadvantages of each method are outlined. For example, manual chart review is time-consuming and requires a large number of calculations, but it can be used to confirm automated reviews or as a spot check for concerning clinicians.<br /><br />The use of data registries, such as GIQuIC, is recommended for comparing facilities and physician performance to peers. These registries provide immediate feedback and are compatible with most endoscopy reporting systems. The document also mentions the use of data warehouses to integrate data from multiple resources into one location, as well as the application of natural language processing for more complex calculations.<br /><br />The role of artificial intelligence, particularly machine learning, in measuring and improving endoscopy quality is highlighted. Examples include the use of deep learning algorithms in computer-aided polyp detection and classification during colonoscopy. The document suggests that the future of data collection lies in artificial intelligence, which has the potential to reduce barriers and automate the process.<br /><br />The document concludes by discussing the need for better registries and novel measures in endoscopy quality assessment. It emphasizes the importance of risk-adjusted outcomes and national benchmarks, as well as automated or semi-automated data transfer. Overall, the document provides an overview of the current limitations in data collection and analysis in endoscopy and proposes potential solutions to improve efficiency and effectiveness.

Keywords

limitations

data collection

data analysis

endoscopy

automation

quality metrics

procedures

outcomes

data registries

data warehouses