Demystifying Workflow in an Urban Emergency Department

Channel:
Microsoft Research
Subscribers:
344,000
Published on ● Video Link: https://www.youtube.com/watch?v=OUIUHl2rZBU


Duration: 1:23:12
117 views
3

The aim of this interactive session is to deconstruct operations in a high-volume, urban Emergency Department, examining the concepts of patient and information flow and how they relate to medical decision-making in a complex system with inherent uncertainty. Existing projects with MSR in this domain will be highlighted. Discussion exploring areas for future collaborations is highly encouraged.



Other Videos By Microsoft Research


2016-09-07Emotion Technology: From Autism to Customer Experience and Decision Making
2016-09-07School of One
2016-09-07Logicomix
2016-09-07Lessons Learned from the World of LOLCats, FAILS and Other Blunders
2016-09-07Localized Optimization: Exploiting non-orthogonality to efficiently minimize the Kohn-Sham Energy
2016-09-07Jinx: Making Uncommon Concurrency Errors Commonplace in Shared Memory Programs
2016-09-07What's my teammate going to do? Reasoning about probable plans for successful teamwork
2016-09-07Beyond Trees: MRF Inference via Outer-Planar Decomposition
2016-09-07Situational Impairments and Mobile Microinteractions
2016-09-07Some Tutorial Notes on Dimension Reduction
2016-09-07Demystifying Workflow in an Urban Emergency Department
2016-09-07Natural Language Processing From Scratch
2016-09-07First cryptanalysis of the full AES
2016-09-07How Irrational Thinking Hinders Scientific Progress, Harms the Planet, and Threatens our Lives
2016-09-07Semi-supervised Learning in Gigantic Image Collections
2016-09-07Blackbox Polynomial Identity Testing for Depth 3 Circuits
2016-09-07Googled: The End of the World as We Know It
2016-09-07Can Complexity Theory Ratify the Invisible [1/19]
2016-09-07Formal proof producing decision procedures
2016-09-07Key Recovery Attacks of Practical Complexity on AES Variants With Up To 10 Rounds
2016-09-07Learn an object recognition system with millions of parameters from a small number of training sets


Tags:
microsoft research