Current Concepts in Pediatric Critical Care-Panel Discussion

Video Transcription

Video Summary

The discussion focused on ensuring AI models' validation sets are representative of diverse populations. Two approaches were outlined: creating generalizable models for use across various sites and building internally-used models tailored to specific institutions. Emphasis was placed on utilizing large, representative datasets and regular revalidation to ensure model applicability.<br /><br />Regarding challenges faced with obese patients, no optimal PEEP level for alveolar recruitment was identified, as it's patient-specific and disease severity-dependent. The use of advanced imaging and apnea oxygenation for intubation in obese patients was recommended to manage severe hypoxemia. A discussion on the need for a protocol for managing obese patients was raised, suggesting a standardized approach could improve outcomes.<br /><br />The challenges of transitioning pediatric patients, particularly those with chronic conditions, to adult care were discussed, emphasizing early and structured transition processes starting as early as age 12. Including adult care providers in transition planning and advocating for specialized training on childhood-onset conditions for adult care providers were suggested solutions to improve transition outcomes.<br /><br />Addressing data issues, garbage data input in AI models was noted as a concern, needing guardrails and understanding of data errors. Alarm fatigue due to AI predictive models was addressed with strategies like two-phase approaches and non-interruptive decision support to reduce workflow interruptions.

Keywords

AI model validation

diverse populations

obese patient management

pediatric to adult care transition

data quality in AI

alarm fatigue

advanced imaging

standardized protocols

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