This presentation explores applications of NLP to medical text understanding.

1. © 2019 Change Healthcare LLC and/or one of its subsidiaries. All Rights Reserved.© 2019 Change Healthcare LLC and/or one of its subsidiaries. All Rights Reserved.
Alex Ermolaev, Director of AI
05.24.2019
CAN AI UNDERSTAND
DOCTOR’S NOTES?

2. © 2018 Change Healthcare LLC and/or its subsidiaries or affiliates. All Rights Reserved.
• Extensive product/services, expertise,
data, and connectivity
• Tying our success to our customer:
providers, payers and consumers
• Independent, unbiased, & relentless drive
towards better healthcare
• Delivering practical innovation through
technology and partners
2,100
Payer Connections
1 in 5
US Patient
Records
$1 trillion
Healthcare Claims
Nearly 14 billion
Healthcare Transactions
5,500
Hospitals & Health Systems
900,000
Physicians117,000
Dentists
600
Laboratoriesh
Pervasive across healthcare
The Change Healthcare Intelligent Healthcare NetworkTM

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3
Change Healthcare initial AI focus areas
With much more to come
Claims Lifecycle Management helping payers and providers create
efficiencies and reduce the cost burden in claims processing.
Medical Text Understanding helping to improve and streamline
medical coding, reduce errors, fraud, waste and abuse.
Workflow Prioritization and Optimization helping make our own and our
customer’s work smarter and more efficient.
Patient/Member Engagement helping to increase consumer/patient
experience and engagement by pairing AI with behavioral science.

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• Process around 50K PDF pages per day, which is
almost 20 millions over the last year
• The documents are often in bad shape: badly
scanned, typos, incomplete, mishandled
• Finding relevant information (including codes) to
make decisions on behalf of our insurance partners
Medical text understanding at scale

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Example

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Solution
Blocks ID/Class
Models:
TensorFlow
Keras
Output
Reorder
R to L
B to T

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Medical Record Pre-Processing
Header ✓
Footer ✓
David A. Smith, M.D. ✓
Legalese ✓
MR Section ✓
Likely diagnoses ✓
Member info ✓

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We found the relevant
pieces of text.
Throw everything else away.
Medical Record Pre-processing
(continued)

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Diagnosis Probability
C79.9: Secondary malignant
neoplasm of unspecified site.
94.3%
A81.2 Progressive multifocal
leukoencephalopathy.
91.2%
J44.9 Chronic obstructive
pulmonary disease, unspecified.
92.3%
ICD-10 Modeling

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10
Change Healthcare AI project examples
EMS
Coding
Radiology
Coding
Risk & HEDIS
Coding
Revenue
Optimization
Audit
Payment
Integrity Audit
Full AI coding AI as a component
Dual
Eligibility
HEDIS is a registered trademark of the National Committee for Quality Assurance (NCQA)

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● Finding codes in large charts – up to 400 pages each
● More precise labeling
● De-noising the records
● Recreating a sequence of events
● Building a language model
● Reconciling mismatch between structured and unstructured
Achieving High Model Accuracy for Medical Text

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12
AI in Healthcare Challenges
Embrace
Variety
Respect
Data
Understand
Workflow

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13
“In order to understand trust in the relationship
between humans and automation, we have to
explore trust in two dimensions: trust in the
technology and trust in the innovating firm.”
Source: https://hbr.org/2017/04/to-get-consumers-to-trust-ai-show-them-its-benefits

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