SCCM Resource Library-Thought Leader: Data Science and Critical Care

Video Transcription

Video Summary

Dr. Claremont and Dr. Chirpik delivered presentations on the use of data science and AI in critical care. Dr. Claremont emphasized the importance of multidisciplinary collaboration and the need to increase the trustworthiness of AI systems. He also highlighted the hype cycle of AI and mentioned the challenges of implementing AI in healthcare. Dr. Chirpik discussed the translation of machine learning models into clinical practice and shared insights on various aspects of a predictive modeling project. He stressed the need to work backward from the problem, involve a multidisciplinary team, and focus on implementation. Both speakers discussed the potential future applications of AI in healthcare, including the use of AI in radiology and the monitoring of patients at a distance using physiologic data. They also provided suggestions on how to widen the field of people involved in data science, including engaging with clinicians and forming collaborations. The speakers also highlighted the importance of involving local champions and conducting focus groups to gain insights from clinicians and improve the usability of AI systems. They concluded by encouraging the audience to embrace the challenges and opportunities of data science in critical care and to work toward improving patient outcomes.

Asset Subtitle

Professional Development and Education, 2022

Asset Caption

Learning Objectives: -Define data science -Describe the life cycle of artificial intelligence -Discuss potential reliability and faults of artificial intelligence

Meta Tag

Content Type Presentation

Knowledge Area Professional Development and Education

Knowledge Level Foundational

Knowledge Level Intermediate

Knowledge Level Advanced

Membership Level Select

Tag Innovation

Year 2022

Keywords

data science

AI

critical care

multidisciplinary collaboration

trustworthiness of AI systems

machine learning models

clinical practice

remote patient monitoring

patient outcomes

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