Shooting the Moon: IT Infrastructure for Data- Sharing Networks Session PM5, February 19, 2017 Jonathan Hirsch, Founder & President, Syapse & Paul Tittel, Systems Director, Providence St. Joseph’s

1 Speaker Introduction

Jonathan Hirsch, MSci

Founder & President, Syapse

2 Conflict of Interest

Employed by and equity in Syapse Inc.

3 Agenda

•Trends in Care • Overview and Aims of the Oncology Precision Network (OPeN) •IT Requirements for Data-Sharing • OPeN Membership and Traction • Why Data Sharing is Critical

4 Cancer Care is Entering a New Era

Cancer patients 90% of cancer drugs in Real-world evidence will actively seek out care late phase trials target a improve outcomes and personalized for them molecular pathway justify reimbursement

5 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.

6 IT Solutions Enable Precision Medicine

Understand Decide Improve

Integrated Clinical and Decision Support and Clinical Analytics & Molecular Data Best Practice Automation Learning Health System

7 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

8 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

9 Step 1: Source System Integration • Health systems use the IT platform to integrate data across multiple systems and labs

Data-Sharing Platform

10 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

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

12 Step 4: Filter Real-World Data to View Insights on Clinically and Molecularly-Similar Patients 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

14 OPeN Membership

Founding Members

Anticipated Future Members

15 Anticipated Reach of OPeN

136,000 598 241 New Cancer Cases Per Year Oncologists Hospitals

16 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

17 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

18 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

19 Speaker Introduction

Paul Tittel, MHA

Systems Director, Providence St. Joseph

20 Conflict of Interest

Consulting Fees: Providence Health & Services Swedish Health Services Immunexpress Inc. No other conflicts to report.

21 Agenda

• Challenges to data sharing • Strategies for mitigating data-sharing challenges • OPeN governance & data use provisions • Legal, privacy, & compliance considerations

22 Challenges to Data Sharing Legal, privacy & compliance Technical & informatics challenges considerations • EHR landscape & IT ecosystems – lots of • Legal framework: Consortium data sharing complexity agreement • Data standardization & semantic • Data “ownership” & use provisions harmonization • Privacy & compliance • Lab heterogeneity & genomic data complexity

23 Challenge: Clinical Data Harmonization Disparate 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

24 Leveraging EDW Assets

Swedish

26 distinct Providence cancer WA/MT Ent. Data registry systems Warehouse Providence OR/CA

Providence AK

Kadlec 25 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.

26 RxNorm Example: Nivolumab

Antineoplastic & Immunomod. Agents IM/MIN Ingredient nivolumab

NIVOLUMAB BN Brand Name 100 mg/10ml - IV Soln. Antineoplastic Agents Opdivo

Epic med. ID: 142344 SCDC Clinical Drug Component RxNorm CUI: 1597876 Other Antineoplastic Agents nivolumab 10 MG/ML Thera. class: Antinoplastics SBDC Branded Drug Component Pharm. class: Antineoplastic; nivolumab 10 MG/ML [Opdivo] Anti-Programmed Death-1 Monoclonal antibodies (PD-1) mAb SCD/GPCK Clinical Drug or Pack 10 ML nivolumab 10 MG/ML Injection Programmed Death Receptor-1 EMR-specific content Blocking Antibody SCDG Clinical Dose Form Group nivolumab Injectable product Nivolumab

Standard ontology references27 & cross-platform metadata RxNorm Example: Nivolumab

Attribute Value Cross-references to other Display_Name NIVOLUMAB standard drug dictionaries code C50.416^4972^ FDA_UNII 31YO63LBSN Drug Name Interaction Description label NIVOLUMAB Acetyldigitoxin Acetyldigitoxin may decrease the cardiotoxic activities of Nivolumab.

The risk or severity of adverse effects can be increased when Nivolumab Level Ingredient is combined with Belimumab.

NUI N0000191289 Bevacizumab may increase the cardiotoxic activities of Nivolumab.

RxNorm_CUI 1597876 The risk or severity of adverse effects can be increased when cabazitaxel Cabazitaxel is combined with Nivolumab. RxNorm_Name nivolumab Cyclophosphamide CyclophosphamideCurated may contentincrease the cardiotoxic on activities drug of Nivolumab.-drug UMLS_CUI C3657270 Ouabain Ouabaininteractions may decrease the cardiotoxic (via activities DrugBank) of Nivolumab. 50.416^4972^Active/Master50.4164972Active/Ma VANDF_Record ster The risk or severity of adverse effects can be increased when Paclitaxel Paclitaxel is combined with Nivolumab. VUID 4034032 Trastuzumab may increase the cardiotoxic activities of Nivolumab.

28 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) Ovary

Astrocytic Tumors Carcinoma

Glioblastoma (WHO grade IV) Clear cell carcinoma

Giant cell glioblastoma (WHO gr. IV) 29 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 Genome Variation Society (HGVS) standards • Standardized nomenclature & descriptions for sequence variants • Well-defined approach to reference sequences

30 Example: HGVS-Compliant Variant Desc. Genome build: GRCh37 / hg19 HUGO gene name: TP53 tumor protein P53

RefSeq: NM_000546 HGVS genomic change: NC_000017.10: g.C7577058A HVGS coding change: NM_000546 c.880G>T: HGVS protein change: NP_000537.3 p.E294*

• External DB variation IDs populated whenever possible: dbSNP dbVAR COSMIC ClinVar

31 OPeN Data Sharing Agreement Critical 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

32 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

33 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

34 Questions

[email protected] [email protected] www.syapse.com www.providence.org @syapse @prov_health

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