Challenges healthcare faces in making patient data actionable: A. Automating chart review for quality measures, medical necessity review. B. Categorizing patient risk for appropriate reimbursement in capitated payment models C. Enhancing diagnostics, enabling differential diagnosis D. Discovering correlations with predictive analytics E. Automating administrative functions, such as scheduling, follow-up care

1. Leveraging AI to Solve Common
Healthcare Challenges: Hear
from the Experts
SARAH BRYAN | Wolters Kluwer
CHRIS FUNK | Wolters Kluwer
JOHN LANGTON | Wolters Kluwer

2. Today’s Speakers
2
SARAH BRYAN
Director of Product Management,
Health Language
Wolters Kluwer
JOHN LANGTON, Ph.D
Director of Applied Sciences
Wolters Kluwer
CHRIS FUNK, Ph.D
Sr. Medical Informaticist, Health
Language
Wolters Kluwer

3. 3
Agenda
1. Challenges healthcare organizations face in making patient data
actionable
2. How AI can address these challenges by optimizing staff
productivity and efficiency
3. Looking to the future: examples of the value AI can bring to the
healthcare industry and how to get started

4. 4
Data
DATA
INFORMATION
KNOWLEDGE
WISDOM
VALUE
Transforming Data Into Value

5. 5
BILLING DATA
ICD-10, CPT®, HCPCS,
DRGs
EMERGING DATA
Telehealth, Genomics, Lifestyle,
Social Determinants of Health
CLINICAL DATA
EHR Data, Labs, Drugs, Imaging,
Behavioral Health, Pathology,
Clinical Notes
MAXIMIZE VALUE BY LEVERAGING ALL DATA SOURCES
Healthcare (Big) Data

6. Healthcare Data is Disparate
6
Cerner EHR
Radiology Information
Systems (RIS)
Pharmacy Information
System (PIS) Lab Information
Systems (LIS)
Epic EHR
Ambulatory
EHR
eClinical
Works EHR
HEALTH PROVIDERS CLINICS HOMES
Telehealth

7. Types of Healthcare Data
7
STRUCTURED
CLAIMS DATA
CPT®, ICD-10, MS-DRG, HCPCS
UNSTRUCTURED
FREE TEXT
Clinician Notes, Clinical Documents,
Pathology Reports, Patient Charts
DISCRETE CLINICAL DATA
EHR Data, Local Labs, Medications
SEMI-STRUCTURED OTHER
“?”
Social Determinants of Health,
Genomics, Patient-generated Data,
Telehealth

8. Step 1: Consolidate, Standardize & Enrich Your Data
8
STANDARDIZE
Normalize your data to
interoperability standards such as
SNOMED, RxNorm, LOINC, and HL7
CONSOLIDATE
Consolidate your patient data from
across the various data sources
incorporating all data types
ENRICH
Enrich your data by extracting and
normalizing clinical concepts locked in
unstructured notes and categorizing
data into clinical concepts

9. Survey Question #1
9
What areas do you feel most excited about when thinking about adopting AI
technology within your organization? Select all that apply
A. Automating chart review for quality measures, medical necessity review, etc.
B. Categorizing patient risk for appropriate reimbursement in capitated payment models
C. Enhancing diagnostics, enabling differential diagnosis
D. Discovering correlations with predictive analytics
E. Automating administrative functions, such as scheduling, follow-up care, etc.
F. Other

10. 10
Agenda
1. Challenges healthcare organizations face in making patient
data actionable
2. How AI can address these challenges by optimizing staff
productivity and efficiency
3. Looking to the future: examples of the value AI can bring to
the healthcare industry and how to get started

11. Artificial Intelligence Continuum
11
WEAK STRONG
Supports human decisions
Examples: Natural language
processing, computer vision
Automates simple tasks
Example: Lane
correction systems in
cars
Makes autonomous
decisions
Example: Self-driving cars
Mimics human behavior
Examples:
HAL 9000 (2001: A Space Odyssey)
Data (Star Trek: TNG)
COGNITIVE INTELLIGENCEAUTONOMOUS INTELLIGENCEAUGMENTED INTELLIGENCEASSISTED INTELLIGENCE
HEALTHCARE
IS PRIMARILY
HERE

12. Artificial Intelligence, Machine Learning, and Natural
Language Processing
12
ARTIFICIAL INTELLIGENCE
EXPERT SYSTEMS
PLANNING
ROBOTICS
Speech to Text
Text to Speech
SPEECH
Image Recognition
Machine Vision
VISIONNATURAL LANGUAGE
PROCESSING
Text Generation
Text Analytics
Question Answering
Context Extraction
Classification
Machine Translation
MACHINE
LEARNING
Deep Learning
Unsupervised
Supervised

13. Artificial Intelligence Applied Across Healthcare
13
CLINICIAN WORKFLOW
INTEROPERABILITY
FINANCIAL MANAGEMENT
REPORTING & ANALYTICSKNOWLEDGE MANAGEMENT
CLINICAL DECISION SUPPORT

14. 80% of Patient Data is Locked in Unstructured Text
14
NOTE
Admission Date: 2015-12-25
New Patient
The patient is a 59-year-old female, who was experiencing
mild chest pain on the left side. The patient states that she
slipped on a newly waxed floor and fell on her tailbone and
low back region.
She would like to lose some weight through dieting.
Complains of dry mouth even though drinking plenty of
water.
The pt is allergic to Morphine Sulfate.
No fh of colorectal CA. Last colonoscopy in Kansas City, May
2012.
Past Medical History:
• DM2
• Asthma
Shx:
Pt has a remote 10 pack-year smoking history. Occasional
ETOH drink.
Height 173 cm, weight 78.8 kg, blood pressure 114/80
Lab data:
2014-6-10 03:20PM GlycoHgB A1c - 7.0*
2014-6-10 03:20PM Creatinine, 24 hour urine g/24 h-1.2

15. 15
Current Process for Extracting Valuable Patient Information
Time Consuming
• Manually browsing multiple documents
• Searching for key words
• Pasting key findings into separate forms
Expensive
• Review process is commonly done by subject matter
experts
Risk of Inaccuracy
• Hard for humans to consistently maintain high level
of accuracy due to volume and demand of review

16. Leverage cNLP
to Make Your
Data Actionable
Admission Date: 2015-12-25
New Patient
The patient is a 59-year-old female, who was
experiencing mild chest pain on the left side. The
patient states that she slipped on a newly waxed
floor and fell on her tailbone and low back region.
She would like to lose some weight through
dieting. Complains of dry mouth even though
drinking plenty of water.
The pt is allergic to Morphine Sulfate.
No fh of colorectal CA. Last colonoscopy in Kansas
City, May 2012.
Past Medical History:
• DM2
• Asthma
Shx:
Pt has a remote 10 pack-year smoking history.
Occasional ETOH drink.
Height 173 cm, weight 78.8 kg, blood pressure
114/80
Lab data:
2014-6-10 03:20PM GlycoHgB A1c - 7.0*
2014-6-10 03:20PM Creatinine, 24 hour urine
g/24 h-1.2
Patient Problems:
29857009 (R07.9) – Chest Pain
Severity: Mild
Laterality: Left
Temporal: Present
Subject: Patient
87715008 – Dry Mouth
Temporal: Present
Subject: Patient
44054006 (E11.9) - Type 2 diabetes mellitus
Temporal: Past
Subject: Patient
195967001 (J45.909) – Asthma
Temporal: Past
Subject: Patient
Family History:
363406005 (C18.9) - Malignant tumor of colon
Subject: Family member
Negated: Yes
Social History:
77176002 (F17.200) – Smoker
Subject: Patient
219006 (Z72.89) – Drinks alcohol
Subject: Patient
Labs:
41995-2 – Hemoglobin A1c [Mass/volume] in Blood
Value: 7.0
Date: 2014-06-10
14684-5 - Creatinine [Moles/time] in 24 hour Urine
Value: 1.2
Date: 2014-06-10
Demographic:
397669002 - Age
Value: 59
263495000 – Gender
Value: Female
Vitals:
50373000 - Height
Value: 173 cm
726527001 - Weight
Value: 78.8kg
75367002 - Blood pressure
Value: 114/80
Allergy:
RX 30236 - Morphine Sulfate
Procedure:
73761001 – Colonoscopy
Date: 5/2012
Admission Date: 2015-12-25
New Patient
The patient is a 59-year-old female, who was
experiencing mild chest pain on the left side. The
patient states that she slipped on a newly waxed
floor and fell on her tailbone and low back region.
She would like to lose some weight through
dieting. Complains of dry mouth even though
drinking plenty of water.
The pt is allergic to Morphine Sulfate.
No fh of colorectal CA. Last colonoscopy in Kansas
City, May 2012.
Past Medical History:
• DM2
• Asthma
Shx:
Pt has a remote 10 pack-year smoking history.
Occasional ETOH drink.
Height 173 cm, weight 78.8 kg, blood pressure
114/80
Lab data:
2014-6-10 03:20PM GlycoHgB A1c - 7.0*
2014-6-10 03:20PM Creatinine, 24 hour urine
g/24 h-1.2

17. Reviewing Patient Risk
UNSTRUCTURED TEXT
Patient Problems:
44054006 (E11.9) - Type 2 diabetes mellitus
Temporal: Past
Subject: Patient
[HCC19] Diabetes without Complication
DATA ELEMENTS
Admission Date: 2015-12-25
New Patient
The patient is a 59-year-old female, who was
experiencing mild chest pain on the left side. The
patient states that she slipped on a newly waxed
floor and fell on her tailbone and low back region.
She would like to lose some weight through dieting.
Complains of dry mouth even though drinking plenty
of water.
The pt is allergic to Morphine Sulfate.
No fh of colorectal CA. Last colonoscopy in Kansas
City, May 2012.
Past Medical History:
• DM2
• Asthma
Shx:
Pt has a remote 10 pack-year smoking history.
Occasional ETOH drink.
Height 173 cm, weight 78.8 kg, blood pressure
114/80
Lab data:
2014-6-10 03:20PM GlycoHgB A1c - 7.0*
2014-6-10 03:20PM Creatinine, 24 hour urine g/24 h-
1.2
Patient Problems:
87715008 – Dry Mouth
Temporal: Present
Subject: Patient
(E11.638) - Type 2 diabetes mellitus with other
oral complications
[HCC18] Diabetes with Chronic Complications

18. Quality Measure Reporting
UNSTRUCTURED TEXT
Family History:
363406005 (C18.9) - Malignant tumor of colon
Subject: Family member
Negated: Yes
DATA ELEMENTS
Procedure:
73761001 – Colonoscopy
Date: 5/2012
Admission Date: 2015-12-25
New Patient
The patient is a 59-year-old female, who was
experiencing mild chest pain on the left side. The
patient states that she slipped on a newly waxed
floor and fell on her tailbone and low back region.
She would like to lose some weight through dieting.
Complains of dry mouth even though drinking plenty
of water.
The pt is allergic to Morphine Sulfate.
No fh of colorectal CA. Last colonoscopy in Kansas
City, May 2012.
Past Medical History:
• DM2
• Asthma
Shx:
Pt has a remote 10 pack-year smoking history.
Occasional ETOH drink.
Height 173 cm, weight 78.8 kg, blood pressure
114/80
Lab data:
2014-6-10 03:20PM GlycoHgB A1c - 7.0*
2014-6-10 03:20PM Creatinine, 24 hour urine g/24 h-
1.2

19. 19
Additional Areas Where cNLP Can Be Applied
JOURNAL ARTICLES PATHOLOGY REPORTS CONSUMER PAMPHLETS

20. 20
Benefits of Using cNLP
REDUCE
REVIEW
TIME
INCREASE
STAFF
EFFICIENCY
REDUCE
ADMINISTRATION
COST
INCREASE
ACCURACY
OPTIMIZE
ANALYTICS
IMPROVE
KNOWLEDGE
MANAGEMENT

21. Survey Question #2
21
Where are you in adoption of AI technology within your organization?
A. Exploration, data-gathering stage
B. Beginning to use AI and NLP to gain insights
C. Implemented but facing challenges
D. Implemented and deployed successfully

22. 22
Agenda
1. Challenges healthcare organizations face trying to make
patient data actionable
2. How AI can address these challenges by optimizing staff
productivity and efficiency
3. Looking to the future: examples of the value AI can bring to
the healthcare industry and how to get started

23. 23
Opportunities for AI in Healthcare
Building intelligent content libraries
Example: Article tagging and
pharmacovigilance use cases
Reducing readmission risk
Example: Use of post-discharge care
management data with EMR data to
assess risk and intervention opportunities
Enabling differential diagnosis
Example: Use of patient-reported
symptoms at intake to discover potential
diagnosis
Providing clinical decision support
Example: Extract family history to
customize clinical decision support
Predicting onset of disease
Example: Improve speed and accuracy of
C-Diff and Sepsis detection
Targeting population health initiatives
Example: Opioid abuse and intervention
services and support

24. 24
Emerging Opportunities to
Leverage Advancements in
Data and Technology in
Healthcare
• Telehealth
• Chatbots
• Wearables (patient-generated data)
• Genomics
• Social Determinants of Health
• Digital assistants / smart devices

25. Survey Question #3
25
What do you feel are your organization’s biggest barriers or challenges in
adopting AI technology? Select all that apply
A. Cost prohibitive
B. Difficulty in getting leadership buy-in
C. Resistance to change; lack of change management
D. Competing priorities
E. Not sure where to start

26. 26
Action Plan for Adopting AI Technology
Identify high-value areas
that could benefit the
most from improved
data quality and
optimized workflows
1
Leverage NLP, AI, and
non-AI technologies to
accelerate data extraction
and enrichment
2
Empower your machine
learning models and
other AI investments
with clean, enriched data
3

27. Health Language Solutions
27
WE BELIEVE IN THE POWER OF ACCURATE DATA
REFERENCE DATA
MANAGEMENT
Centralize and manage your clinical,
claims, administrative data, and
clinical concepts, to streamline data
governance and improve operational
efficiencies.
CNLP FOR
UNSTRUCTURED DATA
Extract amd codify clinical concepts
locked in unstructured text by
leveraging clinical synonyms and
acronyms to identify gaps-in-care and
improve quality of care.
INTEROPERABILITY /
DATA NORMALIZATION
Achieve semantic interoperability
by mapping non-standard/standard
data to standard terminologies for
accurate analytics.

28. 28
Questions?
SARAH BRYAN
Director of Product Management, Health Language
Wolters Kluwer
Sarah.Bryan@WoltersKluwer.com
CHRIS FUNK, Ph.D
Sr. Medical Informaticist, Health Language
Wolters Kluwer
Chris.Funk@WoltersKluwer.com
JOHN LANGTON, Ph.D
Director of Applied Sciences
Wolters Kluwer
John.Langton@WoltersKluwer.com

29. Thank You
www.healthlanguage.com
www.wolterskluwer.com