SCCM Resource Library-Deploying AI for Critical Care: Learning to Make a Difference

Video Transcription

Video Summary

In this talk, the speaker discusses the challenges and considerations when implementing machine learning models in clinical care. Although there is a growing number of machine learning models being developed, very few are actually being implemented clinically. The speaker highlights several barriers to implementation, including data privacy and security, data quality and availability, lack of standardization, ethical considerations, model interpretability, and adoption and integration challenges. To address these challenges, the speaker presents a framework for successful implementation, which involves exploring the problem, establishing a multidisciplinary team, designing and testing the machine learning model, and implementing and evaluating the system. The speaker also emphasizes the importance of ensuring that the model adds value to clinical practice, understanding the workflow and usability of the model, and measuring usage and outcomes. The talk concludes by suggesting that accurate machine learning models that prompt the right actions at the right time have the potential to improve patient outcomes.

Asset Subtitle

Professional Development and Education, 2023

Asset Caption

Type: year in review | Year in Review: Anesthesiology (SessionID 2000001)

Meta Tag

Content Type Presentation

Knowledge Area Professional Development and Education

Membership Level Professional

Membership Level Select

Tag Innovation

Year 2023

Keywords

machine learning models

clinical care

implementation challenges

data privacy and security

improve patient outcomes

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