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Big Data in Healthcare: Hype and Hope
How can we find the path to precision medicine?
Bonnie Feldman, DDS, MBA | www.drbonnie360.com | @DrBonnie360 | [email protected]
© 2014 - All rights reserved.
© 2014 - All rights reserved.
Medical Data
Patient
Clinical
Financial
R&D
Owners: Consumers, caretakersSources: Patients, providersUsers: Patients, providers, R&D, payers Examples: vitals, fitness, history
Owners: Providers, patientsSources: Patients, providersUsers: R&D, patients, providers, payers Examples: EMRs, images, Dx, Tx
Owners: Payers, Sources: Providers
Users: Payers, providers, regulatorsExamples: claims, cost, payment, utilization
Owners: Academics, pharmaSources: Providers, patientsUsers: Researchers, developersExamples: trials, screening libraries
© 2014 - All rights reserved.© 2014 - All rights reserved.
© 2014 - All rights reserved.
Different Perspectives from:•Andrew Kasarskis Co-director, Icahn Institute for
Genomics and Multiscale Biology•Colin Hill CEO of GNS Healthcare
•William King CEO Zephyr Health
•Jonathan Hirsch Founder and President Syapse
© 2014 - All rights reserved.
Icahn Institute GNS Healthcare Zephyr Health Syapse
What Integrate Big Data to build models ofbiology and thus better diagnose, treat + prevent disease
Value based Big dataanalytics 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 economicand 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 PatientsProvidersHealth Care Innovators
Payers Life Science Companies
Commercial teamMedical affairs team
Providers OncologyCardiovascularGenomic Medicine
© 2014 - All rights reserved.
Open Questions•What has worked?
•What has not worked?
•How is your business model evolving?
•Dreams for the future?
+1.310.666.5312
www.drbonnie360.com
@DrBonnie360
Bonnie Feldman DDS, MBA
Business Development for Digital Health
www.gnshealthcare.com
Colin Hill, CEO & Founder
Accelerating Intelligent Interventions
November, 2014
• 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
Big Data Analytics Accelerating Intelligent Interventions
10
Pharmacy & Medical Claims
Consumer Data
EMR Data
HRA, Labs, Geography
Emerging Data
GNS REFS™ Platform
Individual Characteristics Intervention
EconomicOutcomes
ClinicalOutcomes
Large & Diverse Data Sets
GNS Healthcare
Value-Based Inference Engine
Personalized Interventions
Value-Based vs. Rules-Based Approach
11
Poor Medication Adherence
12
Value-Based vs. Rules-Based Selection
Value based selection precisely matches individuals and maximizes overall ROI
Lucy Nora EthelAge 46 24 66
Drugs of Interest (DOIs)Cardio, Diabetes (oral), Chronic Respiratory
Cardio + Diabetes Cardio + Diabetes Cardio
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 Avoided with Increase in PCD
> $14,00025% Increase
< $20045% Increase
> $10,00010% Increase
13
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
www.gnshealthcare.com
Colin Hill, CEO & Founder
Accelerating Intelligent Interventions
GNS Healthcare1 Charles ParkCambridge, MA 02141
Big Data, the Icahn Institute, and the Mount Sinai Health System
Andrew KasarskisNYeC Digital Health ConferenceNovember 17, 2014
@IcahnInstitute
Building and Using Realistic Predictive Models of Biology
18
EMR(EPIC)
Clinical Labs
Sequencing Facility
Data Warehouse
BioBank Patient Traffic
Clinical Data
Primary Data
High-Performance Computing
Research and Clinical Queries;
Experiment Creation; etc.
Disease Model Construction and
Prediction Generation
Actionable Feedback
Using the Big Data: Benefits for Patients, Providers, and Research at Mount Sinai
Icahn Institute
New Target and Biomarker Discovery
Pathogen Surveillance Molecular
Epidemiology
Data Science Adds Value Across Constituencies
Population
Sample acquisition
Closing Thought
Electronic Medical Record
Clinical Care& Research
Personal Environmental
and Social Data
Predictive Network Model
21
Enabling Precision Medicine for Healthcare Providers
Jonathan Hirsch Founder & President Syapse
Legacy oncology practice
“Nuclear bomb” therapies
Precision cancer care
“Smart bomb” therapies
Health System’s Challenge
Providing rich genetic data and actionable information to physicians while overcoming legacy software infrastructure
Best of the 1980s: EMR, PACS, LIS, CPOE, eMAR
Legacy software
Electronic Medical Record
Can’t handle complex genomic data
No data mining, visualization
Built for billing & compliance
The precision medicine workflow… …and barriers to adoption.
Order test
Clinical workup & Review clinical history
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
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
data integration and visualization No
The precision medicine workflow… …and barriers to adoption.
Order test
Clinical workup & Review clinical history
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
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
data integration and visualization No
EMR tabs EMR records Paper reports Emails Phone calls XLS, PPT, DOC files Mental steps
8 ~50
9 4 5
12 4
Conservative estimate by users
Genomic data: EMR “Import”
A modern-day Tower of Babel
No standard schemas
No standard terminology
Unstructured or
semi-structured
Thousands of record types
Millions of property types
Integrate molecular data into clinical workflow
Tailor decision support to organization best practices
Extend expertise to affiliate network
Introducing Syapse: Enterprise software to enable precision medicine
Data integration Physician
Sequencing & Analytics
Sendout Labs
One-Time Migration
Data Ingestion
PDF Excel
PowerPoint Filemaker Pro
LIS
Data Warehouse
CPOE
EMR PACS
Drug Administration
Interfaced Systems
Oncologist dashboard
5
4
3 2
1
2 Omics data
3 Drug procurement
5 Imaging metadata
1 Structured clinical data
4 Longitudinal data
* All data included in this chart is for informational purposes only and does not include actual patient data
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
