The document provides an overview of Amazon's natural language processing services including Amazon Polly, Amazon Transcribe, Amazon Translate, Amazon Comprehend, and Amazon Lex. It discusses their key features and capabilities for text to speech, speech recognition, language translation, text analysis, and conversational interfaces. Examples of using the services for various use cases like contact centers, information bots, and applications are also presented.
43. GE Healthcare &
Roche Diagnostics
A C O L L A B O R A T I O N I N A C U T E C A R E M E D I C I N E
*Disclaimer: Technology in development that represents ongoing research and development efforts. These technologies are not products and may
never become products. Not for sale. Not cleared or approved by the U.S. FDA or any other global regulator for commercial availability.
Ruth Bergman, PhD
Director of Engineering
Acute Care
50. T H E P R O B L E M : W H Y I S S E P S I S G O I N G U N D E T E C T E D ?
Reason 1:
Dark Data
Reason 2:
Trapped Clinician Potential
UNDETECTED
DETERIORATION
e.g. Sepsis / Death
51. T H E P R O B L E M
Why is DATA not helping like it should?
Dark Data
Data Slower
Than Disease
The data are captured in
snapshots, and the
syndrome or condition
changes between
measurements.
The Data Desert
Must detect patients
before the ICU… before
they’re monitored.
Timeliness
Indicators do not trigger
early enough to respond
effectively.
Sensitivity &
Specificity
The inability to correctly
identify those who have
or don't have the
syndrome.
52. T H E P R O B L E M
Trapped Clinician Potential
Why is CLINICIAN POTENTIAL trapped?
Care Complexity
How do I ask better
questions, from better
data, faster to inform
diagnosis and care plan?
EMR Time Drain
Reliable system of
record, but frustrating
system of engagement.
Patient Awareness
Staff to patient ratios can
make it difficult to notice
important patient changes.
Collective Awareness
Inability to share a
common medical view across
the care team.