A series of Case Reviews to demonstrate the power that retrospective data can have at its native resolution to support clinical care and the development of patient-centered predictive analytics.

Can artificial/augmented intelligence (AI) and machine learning be employed to derive more intelligence from data? Is there a path forward to using that data to optimize care delivery for individual patients or even predict health events?

Learn how to calculate EE from VCO2 measurement as a surrogate to IC.

Hear Dr. Cha describe how the staff at Johns Hopkins monitored complex ECMO patients during the pandemic while finding ways to reduce frontline caregiver risk.

Hear how the Johns Hopkins Precision Medicine Platform was created and is used to predict patient deterioration and risk.

Find out why the D2K Lab at Rice University was created and how it is directly impacting medical research and AI.

Learn about how visual analytics tools for sedation management helped create a virtual rounding program during the pandemic.

Hear how the neonatal hemodynamics specialty is growing and evolving and what is next for research in the field.

Find out real-time & retrospective waveform data was used to build a novel detector of a common postoperative arrhythmia.

Learn how to leverage data and remote monitoring tools to manage neonates with critical congenital heart disease.

Learn about the variables that were hardest to obtain and the methods to collect the data for the multi-site study.