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Shooting the Moon: IT Infrastructure for Data-Sharing NetworksSession PM5, February 19, 2017

Jonathan Hirsch, Founder & President, Syapse & Paul Tittel, Systems Director, Providence St. Joseph’s

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Speaker Introduction

Jonathan Hirsch, MSci

Founder & President, Syapse

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Conflict of Interest

Employed by and equity in Syapse Inc.

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Agenda

•Trends in Cancer Care

• Overview and Aims of the Oncology Precision Network (OPeN)

•IT Requirements for Data-Sharing

• OPeN Membership and Traction

• Why Data Sharing is Critical

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Cancer Care is Entering a New Era

Cancer patients actively seek out care personalized for them

90% of cancer drugs in late phase trials target a

molecular pathway

Real-world evidence will improve outcomes and justify reimbursement

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Pooled, Real-World Evidence Leads to Better Treatment Decisions

• Aggregating real-world evidence on molecularly-defined cohorts can inform treatment decisions for precision medicines.

• Using molecular data to stratify the populations leads to small samples, limiting our ability to improve care.

• It is critical to pool real-world data across multiple institutions to draw large-scale, statistically powerful treatment insights.

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IT Solutions Enable Precision Medicine

Understand

Integrated Clinical and

Molecular Data

Decide

Decision Support and

Best Practice Automation

Improve

Clinical Analytics &

Learning Health System

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What is the Oncology Precision Network (OPeN)?

• OPeN is a trusted network of renowned community health systems and academic medical centers

• Members share aggregated clinical, molecular, treatments and outcomes data

• Access insights from aggregated data to improve cancer care

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IT Requirements for Data-Sharing

In order to enable data sharing, an IT platform must:

• Integrate and aggregate data from individual health systems

• Standardize and normalize data for comparisons across multiple

health systems

• Maintain data privacy and security to build a trusted network

• Provide a point-of-care application so health systems providing

data can learn and improve

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Step 1: Source System Integration• Health systems use the IT platform to integrate data

across multiple systems and labs

Data-Sharing Platform

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Step 2: Semantic Normalization Across Systems

• Choose a set of data elements that are clinically actionable and meaningful

• Emphasize data elements that can be automatically captured from existing

systems, except for data elements that require re-engineering data capture

workflows

– i.e. tumor histology

• Use vocabulary standards

• Automate the normalization process after the schema and standards have

been established

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Step 3: Federated Architecture Allows for a Secure, Trusted Network

Step 4: Filter Real-World Data to View Insights on Clinically and

Molecularly-Similar Patients

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OPeN Data Scope• Demographics: age, sex, gender, race, ethnicity

• Cancer diagnosis: primary site, histological diagnosis, stage

• Tumor genomics: gene, alteration

• Tumor markers: biomarker tests

• Treatments: next line of treatment after tumor genomic profile (chemo, targeted therapies)

• Outcomes: duration of therapy, survival, quality of life

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OPeN Membership

Founding Members

Anticipated Future Members

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Anticipated Reach of OPeN

136,000 New Cancer Cases Per Year

598Oncologists

241Hospitals

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OPeN is Part of VP Biden’s Cancer Moonshot

• Vice President Biden announced the OPeN Network in his address at the Cancer Moonshot Summit in June 2016

• The effort aims to double the rate of progress in clinical care and cancer research over the next 5 years

• The initiative encourages health systems to come together in a national effort to share data

• VP Biden acknowledged the importance of OPeN to the future of cancer care

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Benefits of Data-Sharing

• Provide clinicians with real-world, aggregated patient data to support treatment decisions and quality improvement

• Develop real-world evidence for existing therapies in new indications

• Support payer reimbursement efforts by referencing multi-institutional outcomes data

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Why Share Data Now?

• The future of providing cancer care will be highly collaborative, evidence-based, and individualized

• Real-world evidence will increasingly guide treatment decisions and support payer reimbursement

• Join a national effort of innovator health systems to share insights and improve cancer care for all

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Speaker Introduction

Paul Tittel, MHA

Systems Director, Providence St. Joseph

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Conflict of Interest

Consulting Fees: Providence Health & Services

Swedish Health Services

Immunexpress Inc.

No other conflicts to report.

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Agenda

• Challenges to data sharing

• Strategies for mitigating data-sharing challenges

• OPeN governance & data use provisions

• Legal, privacy, & compliance considerations

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Challenges to Data Sharing

Technical & informatics challenges

• EHR landscape & IT ecosystems – lots of complexity

• Data standardization & semantic harmonization

• Lab heterogeneity & genomic data complexity

Legal, privacy & compliance considerations

• Legal framework: Consortium data sharing agreement

• Data “ownership” & use provisions

• Privacy & compliance

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Challenge: Clinical Data HarmonizationDisparate clinical systems:

• Intermountain: HELP2 & Cerner EMRs

• Stanford: Epic EMR

• Swedish / Providence: Epic EMRs 5 different instances, 3 distinct “builds”

Mitigating strategies:

1. Leverage enterprise data warehouse (EDW) sources

• EDW data already partially normalized & harmonized within member orgs.

2. Focus on discrete, structured data elements

3. Map to standardized clinical data ontologies

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Leveraging EDW Assets

26 distinct

cancer

registry

systems

Swedish

Ent. Data

Warehouse

Providence

WA/MT

Providence

OR/CA

Providence

AK

Kadlec

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Example: Medications Data Harmonization

RxNorm: standardized ontology for medications from UMLS / NLM

• Normalized drug names for automated decision support, system interoperability, quality reporting, healthcare research & reimbursement analyses

• Supports multiple levels of descriptions & relationships

• Links to 11 distinct external drug vocabularies

• National Drug File Reference Terminology (NDF-RT) integration

– Metadata on clinical uses, therapeutic categories, mechanism of action, contraindications, known drug interactions, etc.

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RxNorm Example: Nivolumab

NIVOLUMAB

100 mg/10ml - IV Soln.

Epic med. ID: 142344

RxNorm CUI: 1597876

Thera. class: Antinoplastics

Pharm. class: Antineoplastic;

Anti-Programmed Death-1

(PD-1) mAb

Nivolumab

Monoclonal antibodies

Other Antineoplastic Agents

Antineoplastic Agents

Antineoplastic & Immunomod. AgentsIM/MIN Ingredient

BN Brand Name

nivolumab

Opdivo

SCDC Clinical Drug Component

nivolumab 10 MG/ML

SBDC Branded Drug Component

nivolumab 10 MG/ML [Opdivo]

SCD/GPCK Clinical Drug or Pack

10 ML nivolumab 10 MG/ML Injection

SCDG Clinical Dose Form Group

nivolumab Injectable product

EMR-specific content

Standard ontology references & cross-platform metadata

Programmed Death Receptor-1

Blocking Antibody

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Attribute Value

Display_Name NIVOLUMAB

code C50.416^4972^

FDA_UNII 31YO63LBSN

label NIVOLUMAB

Level Ingredient

NUI N0000191289

RxNorm_CUI 1597876

RxNorm_Name nivolumab

UMLS_CUI C3657270

VANDF_Record50.416^4972^Active/Master50.4164972Active/Ma

ster

VUID 4034032

Drug Name Interaction Description

Acetyldigitoxin Acetyldigitoxin may decrease the cardiotoxic activities of Nivolumab.

belimumabThe risk or severity of adverse effects can be increased when Nivolumab

is combined with Belimumab.

bevacizumab Bevacizumab may increase the cardiotoxic activities of Nivolumab.

cabazitaxelThe risk or severity of adverse effects can be increased when

Cabazitaxel is combined with Nivolumab.

Cyclophosphamide Cyclophosphamide may increase the cardiotoxic activities of Nivolumab.

Ouabain Ouabain may decrease the cardiotoxic activities of Nivolumab.

PaclitaxelThe risk or severity of adverse effects can be increased when Paclitaxel

is combined with Nivolumab.

trastuzumab Trastuzumab may increase the cardiotoxic activities of Nivolumab.

RxNorm Example: Nivolumab

Cross-references to other

standard drug dictionaries

Curated content on drug-drug

interactions (via DrugBank)

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Example: Cancer Case Characterization Codified primary site & histopathology ontologies

• Developed at Swedish Cancer Institute by MD clinical informatics lead on Precision Medicine Program

• Aligned with World Health Organization (WHO; ICD-O-3) & College of American Pathologists (CAP) standards

Examples:Central Nervous System (Brain / Spinal Cord)

Astrocytic Tumors

Glioblastoma (WHO grade IV)

Giant cell glioblastoma (WHO gr. IV)

Ovary

Carcinoma

Clear cell carcinoma

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Challenge: NGS Data/Lab Standardization

Again, disparate clinical sequencing & LIS / LIMS solutions

• Many NGS data management systems are “home-grown”

Mitigating strategies:

1. Rigorous enforcement of Syapse Lab Certification Program standards

• Focus: Complete, high-quality, & well-curated genomic data

• Codification of genomic metadata

2. Leverage Human Genome Variation Society (HGVS) standards

• Standardized nomenclature & descriptions for sequence variants

• Well-defined approach to reference sequences

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Example: HGVS-Compliant Variant Desc. Genome build:

HUGO gene name:

RefSeq:

HGVS genomic change: :

HVGS coding change: :

HGVS protein change:

• External DB variation IDs populated whenever possible:

TP53

NM_000546

tumor protein P53

NC_000017.10

NM_000546

g.C7577058A

c.880G>T

GRCh37 / hg19

NP_000537.3 p.E294*

dbSNP dbVAR COSMIC ClinVar

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OPeN Data Sharing AgreementCritical foundations:

• Clinical executive sponsor alignment

• Shared vision & aligned objectives

Data Sharing Agreement – legal codification

• Months of work; “working group” of institutional attorneys

– Key considerations: IP, data ownership; dissolution / exit provisions

• Each participating institution retains data “ownership” (stewardship)

• OPeN repository – fully de-identified; HIPAA risks markedly reduced

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OPeN DSA: Data Use Provisions

1. Research projects: OPeN Steering Committee must review & approve project requests involving consortium data

2. Grant development: similarly, Steering Committee review & approval

3. Publications: all publications must cite the consortium in methodology & acknowledgement sections; authorship determined by contribution of individual authors

4. IRB requirements: OPeN participants responsible for institutional IRB-approval for specific research projects

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Privacy & Compliance: Providence Perspective

Full review from privacy, compliance, & information security standpoints:

• Chief Privacy Officer, research compliance lead, & IT security analyst

• Key considerations:

– Full HIPAA de-identification

– OPeN inclusion only with patient consent (IRB-approved protocol)

Best practices:

• Transparent engagement, from the outset

• Engage Risk Mgmt. / Privacy as partners

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Questions

jon@syapse.com

www.syapse.com

@syapse

paul.tittel@providence.org

www.providence.org

@prov_health