As the author of “Big Data in Healthcare Hype and Hope,” Dr. Feldman has interviewed over 180 emerging tech and healthcare companies, always asking, “How can your new approach help patients?” Her research shows that data, as an enabling tool, has the power to give us critical new insights into not only what causes disease, but what comprises normal. Despite this promise, few patients have reaped the benefits of personalized medicine. A panel of leading big data innovators will discuss the evolving health data ecosystem and how big data is being leveraged for research, discovery, clinical trials, genomics, and cancer care. Case studies and real-life examples of what’s working, what’s not working, and how we can help speed up progress to get patients the right care at the right time will be explored and debated. • Bonnie Feldman, DDS, MBA - Chief Growth Officer, @DrBonnie360 • Colin Hill - CEO, GNS Healthcare • Jonathan Hirsch - Founder & President, Syapse • Andrew Kasarskis, PhD - Co-Director, Icahn Institute for Genomics & Multiscale Biology; Associate Professor, Genetics & Genomic Studies, Icaahn School of Medicine at Mt. Sinai • William King - CEO, Zephyr Health New York eHealth Collaborative Digital Health Conference November 18, 2014

1. Big Data in Healthcare: Hype and Hope
How can we find the path to precision medicine?
Bonnie Feldman, DDS, MBA | www.drbonnie360.com | @DrBonnie360 | drbonnie360@gmail.com

2. © 2014 - All rights reserved.

3. Medical Data
Owners: Consumers, caretakers
Sources: Patients, providers
Users: Patients, providers, R&D, payers
Examples: vitals, fitness, history
© 2014 - All rights reserved.
Patient
Clinical
Financial
R&D
Owners: Providers, patients
Sources: Patients, providers
Users: R&D, patients, providers, payers
Examples: EMRs, images, Dx, Tx
Owners: Payers,
Sources: Providers
Users: Payers, providers, regulators
Examples: claims, cost, payment, utilization
Owners: Academics, pharma
Sources: Providers, patients
Users: Researchers, developers
Examples: trials, screening libraries

4. © 2014 - All rights reserved.

5. Different Perspectives from:
•Andrew Kasarskis Co-director, Icahn Institute for
© 2014 - All rights reserved.
Genomics and Multiscale Biology
•Colin Hill CEO of GNS Healthcare
•William King CEO Zephyr Health
•Jonathan Hirsch Founder and President Syapse

6. Icahn Institute GNS Healthcare Zephyr Health Syapse
What Integrate Big Data to
build models of
biology and thus
better diagnose,
treat + prevent
disease
© 2014 - All rights reserved.
Value based Big data
analytics for
personalized
interventions that
deliver better
population health
Organizes health
information that
makes it useful and
accessible for anyone
Precision medicine
platform that
enables healthcare
providers
How Aggregation and
mining of clinical,
preclinical + basic
research data,
molecular + other
profiling tech, EMR
and other data
sources
Value- based
analytics that
combine economic
and clinical models
to predict the right
interventions targets
for best outcomes
Integrates health
data from thousands
of disparate source
lets users find
insights by viewing
data in context
Semantic computing
based Precision
Medicine Platform
aggregates genomic,
molecular, outcomes
and cost data
For Whom Patients
Providers
Health Care
Innovators
Payers Life Science
Companies
Commercial team
Medical affairs team
Providers
Oncology
Cardiovascular
Genomic Medicine

7. Open Questions
•What has worked?
•What has not worked?
•How is your business model evolving?
•Dreams for the future?
© 2014 - All rights reserved.

8. +1.310.666.5312
drbonnie360@gmail.com
www.drbonnie360.com
@DrBonnie360
Bonnie Feldman
DDS, MBA
Business Development
for Digital Health

9. Accelerating Intelligent Interventions
Colin Hill, CEO & Founder
November, 2014
www.gnshealthcare.com

10. Big Data Analytics Accelerating Intelligent Interventions
• Team of 50 (25 PhD’s)
• Physicists
•Health & Computer scientists
•Health Epidemiologists
•Health Actuaries
•Mathematicians
•Statisticians
• Founded in 2000
• Cambridge, MA
• Solutions for
• Payers
• Providers
• Pharma
10

11. GNS Healthcare
Emerging
Data
HRA, Labs,
Geography
EMR Data
Consumer Data
Pharmacy & Medical
Claims
GNS
REFS™ Platform
Individual
Characteristics Intervention
Economic
Outcomes
Clinical
Outcomes
Large & Diverse
Data Sets
Value-Based Inference
Engine
Personalized
Interventions
Value-Based vs. Rules-Based Approach
11

12. Poor Medication Adherence
12

13. Value-Based vs. Rules-Based Selection
Value based selection precisely matches individuals and
maximizes overall ROI
Lucy Nora Ethel
Age 46 24 66
Drugs of Interest (DOIs)
Cardio + Diabetes Cardio + Diabetes Cardio
Cardio, Diabetes (oral), Chronic Respiratory Current PDC to DOIs 44% 29% 82%
# Unique Pharmacies 2 1 2
Prior Condition-Related Events? Yes No No
Event Costs That Could ‘ve Been
> $14,000
< $200
Avoided with Increase in PCD
25% Increase
45% Increase
> $10,000
10% Increase
13

14. Meaningful Adherence™
Rules-based Value-based
41,114 Selected individuals 42,856
$ 2.3M Eliminated events $ 3.1M
$ 1.6 M Additional Rx costs $ 0.5M
$ -13.03 Net savings/participant $ 96.75
(0.7) ROI 2.7
• Rapid Time to Value
– Personalized interventions on just the right targets
– Optimizing cost savings
– Improving clinical results
• Revolutionizing Population Health Mgt.
14

15. Accelerating Intelligent Interventions
Colin Hill, CEO & Founder
Colin@gnshealthcare.com
GNS Healthcare
1 Charles Park
Cambridge, MA 02141
www.gnshealthcare.com

16. Big Data, the Icahn Institute, and the
Mount Sinai Health System
Andrew Kasarskis
NYeC Digital Health Conference
November 17, 2014
@IcahnInstitute

18. Building and Using Realistic Predictive Models of Biology
18

19. Using the Big Data: Benefits for Patients, Providers,
and Research at Mount Sinai
BioBank Patient
EMR
(EPIC)
Clinical
Labs
Sequencing
Facility
Data
Warehouse
Traffic
Clinical Data
Primary Data
High-Performance
Computing
Research and
Clinical Queries;
Experiment
Creation; etc.
Actionable
Feedback
Disease Model
Construction and
Prediction
Generation

20. Data Science Adds Value Across Constituencies
Icahn Institute
New Target and
Biomarker Discovery
Pathogen Surveillance
Molecular
Epidemiology

21. Closing Thought
Population
Sample
acquisition
Electronic Medical
Record
Clinical Care
& Research
Personal
Environmental
and Social
Data
Predictive
Network Model
21

22. Enabling Precision Medicine
for Healthcare Providers
Jonathan Hirsch
Founder & President
Syapse

23. Legacy oncology practice
“Nuclear bomb” therapies

24. Precision cancer care
“Smart bomb” therapies

25. Health System’s Challenge
Providing rich genetic
data and actionable
information to
physicians while
overcoming legacy
software infrastructure

26. Legacy software
Best of the 1980s:
EMR, PACS, LIS, CPOE, eMAR

27. Electronic Medical Record
Can’t handle
complex genomic data
No data mining,
visualization
Built for billing
& compliance

28. The precision medicine workflow… …and barriers to adoption.
Clinical workup &
Review clinical history
Order test
Lab generates MDx test report
View clinical & MDx data
Receive decision support based on
guidelines, clinical, molecular data
Order therapy or
enroll patient in clinical trial
Process drug procurement
Monitor patient outcome
& revise care strategy
Track cost & adherence
Obtain pre-authorization
Molecular Tumor Board reviews
clinical & MDx data; delivers
guidance to physician
Obtain off-label
reimbursement authorization
Assess health outcomes &
modify care pathways
data integration No and visualization
decision support for MDx test orders
pre-authorization support
systematic decision support for
therapy or clinical trials
mechanism for sharing patient records
systematic capture of physician
decisions & patient outcomes
systematic capture of treatment costs
systematic update of care pathways
No
No
No
No
No
No
No

29. The precision medicine workflow… …and barriers to adoption.
Clinical workup &
Review clinical history
Order test
Lab generates MDx test report
View clinical & MDx data
Receive decision support based on
guidelines, clinical, molecular data
Order therapy or
enroll patient in clinical trial
Process drug procurement
Monitor patient outcome
& revise care strategy
Track cost & adherence
Obtain pre-authorization
EMR tabs
EMR records
Paper reports
Emails
Phone calls
XLS, PPT, DOC files
Mental steps
Molecular Tumor Board reviews
clinical & MDx data; delivers
guidance to physician
Obtain off-label
reimbursement authorization
Assess health outcomes &
modify care pathways
data integration No and visualization
decision support for MDx test orders
pre-authorization support
systematic decision support for
therapy or clinical trials
mechanism for sharing patient records
systematic capture of physician
decisions & patient outcomes
systematic capture of treatment costs
systematic update of care pathways
No
No
No
No
No
No
No
8
~50
9
4
5
12
4
Conservative estimate by users

30. Genomic data: EMR “Import”

31. A modern-day Tower of Babel
No standard schemas
No standard terminology
Unstructured or
semi-structured
Thousands of record types
Millions of property types

32. Introducing Syapse:
Enterprise software to enable precision medicine
Integrate molecular data into clinical workflow
Tailor decision support to organization best practices
Extend expertise to affiliate network

33. Data integration
Physician
Data Ingestion
Sequencing &
Analytics
Sendout
Labs
PDF
Excel
PowerPoint
Filemaker Pro
One-Time Migration
Interfaced Systems
PACS EMR
Data
Warehouse
LIS
CPOE
Drug
Administration

34. Oncologist dashboard
5
4
3
2
1
1 Structured clinical data
2 Omics data
3 Drug procurement
4 Longitudinal data
5 Imaging metadata
* All data included in this chart is for informational purposes
only and does not include actual patient data

35. Cancer genomics workflow enabled by Syapse
Clinical
Workup
Patient
Consent
Test Order
in EMR
Specimen
Procurement
Sequencing
& Processing
Filtering Searchable
Database
Report
Delivery
Clinical
Data Review
Molecular
Tumor Board
Syapse
Clinical
Decision