The document introduces Amazon Comprehend Medical, a new machine learning service that can extract medical information from unstructured text with high accuracy. It discusses how Comprehend Medical can help analyze large amounts of clinical notes and reports by identifying entities, relationships, and protected health information. Two customer use cases are presented: Fred Hutchinson Cancer Research Center uses it to gain insights from clinical trials, and Roche Diagnostics uses it to enhance data quality and clinical decision support.

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Amazon Comprehend Medical
Arun Ravi
Sr. Product Manager
AWS AI/ML
Taha Kass-Hout, MD, MS
Amazon
Introducing

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Amazon Comprehend Medical
A new HIPAA eligible service that uses machine learning to extract medical information with high accuracy,
reducing the cost, time and effort of processing large amounts of unstructured medical text

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Agenda
The customer problem of analyzing unstructured text in healthcare
What is Amazon Comprehend Medical?
How does Amazon Comprehend Medical Work?
Where can I use Amazon Comprehend Medical?
Customer –1 (Fred Hutchinson Cancer Research Center)
Customer – 2 (Roche Diagnostics Information Solutions)
Call to action

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Customer Problems
1.2 B unstructured clinical documents created per year
Critical information “trapped” in these documents
Difficult to extract insights

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Amazon Comprehend Medical
Entities
• Medication
• Medical condition
• Test, Treatments and Procedures
• Anatomy
• Protected Health Information (PHI)
Relationship Extraction
• Medication and dosage
• Test and result
• Many more
Entity Traits
• Negation
• Diagnosis, Sign or
Symptom
Protected Health Information Identification
(PHId API)
Distill a complex process into a simple API call
Medical Named Entity and Relationship
Extraction (NERe API)

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Protected Health Information (PHI)
Mr. Smith: Name
63: Age
Comprehend Medical - NERe API
Mr. Smith is a 63-year-old gentleman with coronary
artery disease and hypertension. CURRENT
MEDICATIONS: taking a dose of LIPITOR 20 mg once
daily
aws comprehend-medical detect-entities --region us-
east-1 --text “<Insert Text Here>”
Named Entities

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Comprehend Medical – NERe API
CURRENT MEDICATIONS: taking a dose of LIPITOR 20
mg once daily
aws comprehend-medical detect-entities --region us-
east-1 --text “<Insert Text Here>”
Relationship Extraction

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Amazon Comprehend – NERe API
What are Entity Traits?
(1) Negation
(2) Diagnosis, sign or symptom
Rash
Discontinue Abraxane, patient denies taking
Tylenol 325 mg and is not taking calcium
carbonate, patient also stopped taking
cholecalciferol 1,000 units PO
Entity Traits

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Amazon Comprehend – PHId API
Mr. Smith is a 63-year-old gentleman with coronary
artery disease and hypertension. He currently lives in
Seattle and works as a teacher. His PCP, Dr. John,
works at the University of Washington
aws comprehend-medical detect-phi --region us-east-1 -
-text “<Insert Text Here>”
In addition to extracting PHI, the PHId
API identifies relevant patient identifiers
described in HIPAA Safe Harbor method
of de-identification
Protected Health Information

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Where is NLP leveraged in Healthcare?
Patient & population
health analytics
Clinical Trial
managementRevenue cycle
management (Medical
Coding)
Pharmacovigilance
PHI Compliance
What else?

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Patient & Population Health Analytics
Challenges Amazon Comprehend
Medical
Outcomes
Unstructured data is difficult to mine
Example: Clinical team in the ICU makes over
120 decisions about care per day, how do you
keep up?
Create ”single-lens” on a single
patient

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Revenue Cycle Management – Medical Coding
Challenges Amazon Comprehend
Medical
Outcomes
Process of coding or classifying patient
records according to the International
Classification of Diseases (ICD) is one of
the most complex transactions
Impact coding efficiency and
reduce burden on clinical staff

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Clinical Trial Management
Challenges Amazon Comprehend
Medical
Outcomes
Identify the right patients for
clinical trials quickly
Allow for quick and accurate
indexing across large
patient populations

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Pharmacovigilance
Challenges Amazon Comprehend
Medical
Outcomes
Multiple avenues of reporting
adverse drug reactions or
adverse events
Decreased burden on staff and
improved throughput

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PHI Compliance
Challenges Amazon Comprehend
Medical
Outcomes
Difficult to maintain HIPAA
compliance and technical
requirements for PHI
Accurate way to create
inventory of sensitive PHI

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Fred Hutchinson Cancer Research Center
Hutch Data Commonwealth
Matthew Trunnell
Chief Information Officer and Vice President of Information Technology Executive
Director, Hutch Data Commonwealth
Customer Story

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NAVIFY®:
Using NLP to enhance data quality
and enable Clinical Decision Support
Anish Kejariwal
Director of Engineering, Analytics
Roche Diagnostics Information Solutions (DIS)
Customer Story

52. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2018 F. Hoffmann-La Roche, Ltd. NAVIFY is a trademark of Roche.
This presentation is for educational purposes.
Roche is positioned to deliver cutting-edge capabilities
Decision support software with 120 years of medical innovation rooted in science
• Leading provider of cancer treatments worldwide
• 137 million patients treated with Roche medicines
• Focused on major medical indications and disease areas
• #1 in biotechnology and in vitro diagnostics
• 19 billion+ diagnostic tests performed
• Advanced scientific knowledge and technology that
increases the medical value of diagnostic solutions
NAVIFY®
Decision Support Portfolio
Workflow + Data + Analytics
Diagnostics Pharmaceuticals

53. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2018 F. Hoffmann-La Roche, Ltd. NAVIFY is a trademark of Roche.
This presentation is for educational purposes.
NAVIFY® Decision Support portfolio
A fully integrated portfolio of scalable, secure
workflow solutions and apps designed to
support care teams with analytics and
actionable insights.

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This presentation is for educational purposes.
Unstructured healthcare data challenges for NAVIFY® Portfolio
• Diverse customers distributed across the world
• Multiple languages
• Different diseases*
• Different report formats (ex: pathology, radiology)
• Different terminologies
Must unlock unstructured data to build a comprehensive,
longitudinal view of the patient, and enable both
clinical decision support and population analytics
*In development

55. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2018 F. Hoffmann-La Roche, Ltd. NAVIFY is a trademark of Roche.
This presentation is for educational purposes.
Sample TCGA
Pathology Report
• The Cancer Genome Atlas (TCGA) is a joint
effort of the NCI and the NHGRI to accelerate
our understanding of the molecular basis
of cancer
• Hosted on Amazon S3 and NCI’s Cancer
Genomics Cloud
• Pathology reports are very diverse
• Pathology reports often have tables,
key-value pairs, and hand-written notes

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This presentation is for educational purposes.
Manually curated
TCGA report
Manual curation is extremely time consuming,
expensive, and prone to error

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This presentation is for educational purposes.
Sample results from curation
Token (word) Entity Category
Mastectomy Procedure
Left Specimen Laterality
Upper outer quadrant Tumor Site
4.5 x 2.0 x 2.0 cm Size of Invasive Carcinoma
Medullary carcinoma Histologic Type
0 Number of Lymph nodes Involved in Tumor
5 Number of Lymph nodes examined
pT2 Primary Tumor (pT)
pN0(i-) Category (pN)

58. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2018 F. Hoffmann-La Roche, Ltd. NAVIFY is a trademark of Roche.
This presentation is for educational purposes.
The NAVIFY® team identified two significant needs
NLP: specialized for medical data
• Minimizes time to train NLP models
Standard Criteria:
• Scalable (support 10 million
pathology reports per year)
• Low cost
• Compliant with applicable privacy laws
• Integrates easily with AWS services
• Serverless, when possible
Text and Structured Data Extraction: extract text from
scanned documents into a machine-readable format
• Retains document structure (e.g., tabular data) to provide
context for NLP

59. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2018 F. Hoffmann-La Roche, Ltd. NAVIFY is a trademark of Roche.
This presentation is for educational purposes.
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NAVIFY® Portfolio Architecture in Consideration
Support NAVIFY®
products and analytics
Raw PDFs Text Extraction
Machine Readable
Text
Structured
Data
Data Flow
Overview* Unstructured Text
Structured
Text
from EMR
NLP
H O S P I T A L S
*In Development

60. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2018 F. Hoffmann-La Roche, Ltd. NAVIFY is a trademark of Roche.
This presentation is for educational purposes.
The use of NLP will be a journey
• Initial goal of speeding up review of
pathology reports
• Will then automate extraction
of high confidence entities
and relationships
(low hanging fruit)
• Will keep increasing automation of
NLP over time
The release of Amazon Comprehend Medical and Amazon Textract is well timed to begin the journey
of structuring data to improve data quality and better enable clinical decision support
Automated
Manual
T I M E

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Thank you!
Anish Kejariwal
Director of Engineering, Analytics
Roche Diagnostics Information Solutions (DIS)
anish.kejariwal@roche.com
We are hiring! https://www.navify.com/careers/

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Call to Action
Webpage: aws.amazon.com/comprehend/medical
Blog: https://aws.amazon.com/blogs/aws/amazon-comprehend-medical-
continuously-trained-natural-language-processing-for-healthcare-customers
Email: ravarun@amazon.com

63. Thank you!
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