ASCO’s CancerLinQ: Big Data

for Quality Benchmarking and

Discovery

Robert S. Miller MD, FACP, FASCOAmerican Society of Clinical Oncology

Vice President and Medical Director

CancerLinQ

UNC Lineberger

Comprehensive Cancer Center

Cancer Outcomes Research Seminar

January 24, 2017

CancerLinQ Mission Statement

“Empowering the oncology community

to improve quality of care and patient

outcomes through transformational data

analytics.”

ORIGINS AND RATIONALE

4

The promise of a rapid learning health system

“… a system in which science, informatics, incentives, and culture are aligned for continuous improvement and innovation—with best practices seamlessly embedded in the delivery process and new knowledge captured as a by-product of the delivery experience”

Best Care at Lower Cost: The Path to Continuously Learning Health Care in America – September 6, 2012

5

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1. Big Data – The Transformation of Cancer Care through

Health IT

2. Cancer “-omics” – Precision Medicine Realized

3. Resources – From Cost to Value in Cancer Care

7

In the age of too much information…

Source: Journal of Clinical Oncology 2010

Increase in clinical data relative to human cognitive capacity

8

Getting to the data

97%of patient data

locked away in unconnected

files and servers

1.7people diagnosed with

cancer in the US

MM

9

What if…

We could bring all the electronic data that is collected from the every day care of every cancer patient into

one rapid learning network?

Mar: ASCO BOD establishes strategic principles for Rapid Learning System

Jun: Quality Department established

Sep: RLS Advisory Group

Feb: Branding as “CancerLinQ”

May: RLS business plan

written

Dec: Breast caprototype built & presented at

QCS

Apr: Data governance & advisory committees

created

Aug: ASCO BOD approves budget for full

build

Sep: Requirements

gathering begins

Apr: RFP for CLQ build issue

Jul: Vendor meetings and

scope adjustment

Dec: SAP selected to build CLQ

CLQ origins: timeline

2011

2012

2013

2014

11

12

CancerLinQ regulatory underpinnings

Compliant with HIPAA, HITECH, and the Common Rule

HIPAA permits disclosure of PHI under “healthcare

operations” including quality assessment and improvement,

evaluation of outcomes, and development and maintenance

of clinical practice guidelines ≠ research (individual patient

consent not required)

Independent IRB determination 2013 – Since initial data

collection performed for quality assessment purposes, activity

does not constitute research

Patient opt-out at practice level

ARCHITECTURE/FEATURES/

CURRENT STATE

14

ASCO & CancerLinQ

Leading professional

organization representing

physicians caring for those with

cancer

>42,000 members from 100+

countries

Mission: Conquering cancer

though research, education,

and promotion of the highest

quality patient care

Not-for-profit subsidiary of ASCO

Dedicated staff and governing

board

Mission: Empowering the

oncology community to improve

quality of care and patient

outcomes through

transformational data analytics

15

SAP: Key stats

$22.2B+ SAP revenue worldwide

#1Enterprise software

261,000customers in 190 countries

68,000+employees worldwide

74%world’s transaction revenue

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SAP partnership

ASCO

• Overall development of CancerLinQ

• Control over the data, services,

and products that stem from CancerLinQ

• Oncology subject matter expertise

SAP

• Access to SAP healthcare technical platform

• Customized tools unique to CancerLinQ’s

needs

• Engineering, development, and other

technical support

• World class secure hosting facility

ASCO and SAP have engaged in a strategic technology

partnership to develop and deploy the CancerLinQ platform

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How CancerLinQ works

17

2

3

4

1

18

CancerLinQ data architecture

Collaborator

specific

private

clouds

Third

party data

19

Key functions & capabilities

Quality performance indicators: real-time clinical quality metrics,

prospective opportunities to improve performance

CancerLinQ Insights: valuable insights and trends from the

aggregated, de-identified database

Visualized timeline: a longitudinal view of oncologic milestones in a

patient’s clinical event history, to construct a patient’s story

Powerful analytic reports: suite of analytic reports for quick

observations and insights of the practice patient population at a glance

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New emphasis on quality measurement and reporting

• Measurement and reporting requirements placing great stress and

burden on clinicians

Quality Payment Program (QPP)

Merit-based Incentive Payment System (MIPS)

Value Modifier

PQRS

MACRA

EHR Incentive Program

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CLQ for quality reporting

Based on ASCO QOPI measures

CLQ Quality Performance Indicators = eCQMs run

against practice EHR data (no manual data abstraction)

eCQMs are SQL representations/calculations built using

SAP CML app (“Clinical Measurement Library”) and

displayed using SAP CMA app (“Clinical Measurement

Analysis”)

% concordance scores generated at individual and

practice levels

Capture My Priority Patients (“actionable patients” who

require an intervention within a specified time frame)

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CLQ e-measures - current

Measure QOPI #MIPS (Oncology

Measure Set)QCDR 2017

Submissions

HER2/neu Test for Breast Cancer Breast 54 x

Her2 Negative and No Trastuzumab Breast 56a x x

% of patients with AJCC Stage IA (T1c) and IB-III ER or PR Positive

Breast cancer receiving Tamoxifen or AI within 1 year of diagnosis

(adapted from NQF #0220 and #0387)

Breast 59

Adj Chemo for Stage III Colon Colon 68 x

Rectal Cancer Chemotherapy Colon 72

Staging Documented within 31 Days of 1st Office Visit Core 2 x

Pain Assessment Core 3

Pain Intensity Quantified Core 4a x x

Smoking/Tobacco Documented in Past Year Core 21aa x x

GCSF with Chemotherapy Stage IV Core 25e x

Chemotherapy Administration during Last 2 Weeks EoL 48 x x

HepB Ag/Core Ab test before Rituximab NHL 78

Combination Chemotherapy Received for Breast Cancer Breast 53 x

Trastuzumab Received Breast 57 x

Pain Intensity Last 2 Visits Before Death Eol 36a x

Corticosteroids + Serotonin Antagonists Prescribed SympTox 27

Anti-emetic for High-Risk Chemotherapy SympTox 29a

Anti-emetic for Medium-Risk Chemotherapy SympTox 29c

Inst

alle

d S

et

Implementation philosophy

No connection charges or annual fees = FREE

No data entry requirements for participating practices

Source system agnostic – any EHR

Data retrieved “as is;” we provide the rules and ontology

services to normalize data

CancerLinQ provides all resources for project management,

training, testing, and application support

Our focus is to have minimal impact on your practice

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CLQ system integrations

Allscripts

Cerner

CureMD

Epic (Clarity, CDA)

GE Centricity

IKnowMed

Intrinsiq

MOSAIQ

NextGen

OncoEMR

Aria/Varian

Proprietary/Other

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Data ingestion

Patient Demographics Care Plans

Provider Characteristics Medications

Encounters Radiology

Diagnosis Radiation Therapy

Staging Surgical Procedures

Pathology Post-therapy Care and Surveillance

Physical Exams and Assessments Notes and Documents

Laboratory Tests

CancerLinQ collects a broad range of clinical data

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CancerLinQ clinical user portal

My Favorites

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Quality Performance Indicators

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CancerLinQ Insights (CLQI)

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CancerLinQ Patient Timeline

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CancerLinQ progress to date

78practices/

cancer centers

~1800oncologists

15 EHRs

represented>1.5M

patient records

in data lake

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Bringing together the leading institutions in the field…

…to improve quality and care for all

CANCERLINQ COMMUNITY:

3RD PARTY DATA ACCESS

CancerLinQ as convener of the oncologycommunity

Data

SourcesPractice

Structured &

Unstructured

Clinical Data

Additional

Structured

Data

PRO Data

Radonc data

Claims Data

Genomic

Sequencing

Data

Sister

Societies,

Oncology

Initiatives,

Federal;

State

International

Data

Patients &

Clinicians

Beneficiaries

Life Sciences

Sister

Societies

Payors

Fed & State

Agencies

Research &

Academic

Institutions

International

Cancer

Centers

Shaping the Future of

Cancer Care Through

Data Driven Decisions

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New era of data-sharing

“CancerLinQ needs to go faster.”

-VP Biden, ASCO 2016

“CancerLinQ supports ASCO’s

mission to deliver quality care to a

broad range of patients by rapidly

extracting and learning from

everyday records.”

Clifford Hudis, MD, ASCO CEO

Jim Young/Reuters

Assembling the coalition

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PT IDMD ID

Location

Approved Cohort

CLQ Analytic Database

Approved Cohort

CLQ Approved Query Tool Custom Created

Special Purpose Sub-Cohorts

CancerLinQ Discovery ™ Data Access Policy

External Queries

1. All data is statistically de-identified and anonymized

2. Access to the entire database is forbidden

3. Access is granted only to approved sub-cohorts; only minimum-necessary data provided

4. CLQ provides eithera. Analytic reports; orb. Controlled, cloud-based access

to defined cohorts using approved analytics software (R, SAS, Python scripts, custom, etc.)

5. All queries approved by Research and Publications Committee

6. Detailed clinical data never passes out of CLQ control

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Research & Publications Committee reviews and approves all

3rd party data access per policy

R&P Committee reviews the resulting abstract or paper to be

sure it conforms to the approved query

• Not peer review; no validation of the research

Research and publication review

R&P Committee ReviewsResearch Requests

Criteria: further mission, data fit for purpose, qualified analytic

team, dissemination plan

Research Study

R&P Committee ReviewsFirst Research Publication

Criteria: consistent with original proposal, data accurately

described, no endorsement

THE CHALLENGES OF BIG DATA

AND RWE

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Big data “V’s”

Volume: Millions of rows, thousands of columns

exceed traditional relational database capacity

Variety: data types (structure, standardization,

ontologies)

Velocity: data refresh frequency (real-time, timeliness)

Veracity: data quality (accuracy, completeness)

Value: add-value of data (cost, workflow, feasibility,

governance)

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Data is organized by individual patient

Data standards inconsistently utilized

Structured data often omits key oncology data

elements, e.g., stage, tumor response

Complex concepts in narrative text format

Inconsistent and variable data capture

Lack of interoperability

Information blocking

Data aggregation challenges

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• Documentation

• Incomplete, Inaccurate

• Structured v Unstructured

• Gaps in Data Mapping

• Data Translation/Harmonization

Practice

EHR

CLQ

Reports

CLQ

Connect

CLQ

“Data

Lake”

CLQ Data

Transformations

&

Harmonization

CLQ

“Analytic

Files”

CLQ data quality:

Potential points of failure

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How CancerLinQ is addressing data quality

1. Formalized data quality reviews and testing with practices

2. Working with Evidera (a leading commercial HEOR and data

analytics company) for data remediation and conversion to

the “OMOP” (Observational Medical Outcomes Partnership)

Common Data Model

3. Incorporation of natural language processing technology

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CLQ data quality reviews with practice

onboarding

1. Structural and content inventory (expected data elements &

values?)

2. Harmonization and codification to appropriate vocabularies:

• Demographics: HL7

• Diagnoses: ICD-9/ICD-10

• Staging: AJCC, others

• Pathology Assessments: ICD-O-3

• Labs and Biomarkers: LOINC

• Treatment Regimens: NCIm

• Assessments, Clinical Trials: SNOMED-CT

• Medications: RxNORM, SNOMED-CT, ATC

3. Practice data performance review (e.g., do the quality

measures return meaningful results?)

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n= 11,789

4548/11,789 =

38.6%

Care goals in structured fields

( ~12K lung ca pts) = gaps!

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Natural language processing

Addressing “gaps” in structured data by evaluating

unstructured data:

• Clinical notes, surgical pathology, imaging reports

Priority domains:• Staging

• Biomarkers

• Adverse events

• Disease progression

47© 2016 SAP SE or an SAP affiliate company. All rights reserved.

Scatter plot of sensitivity or recall results reported for group 1 studies.(Studies using an existing classification, vocabulary, or terminology system)

Mary H Stanfill et al. A systematic literature review of automated clinical coding and classification systems. J Am Med Inform Assoc2010;17:646-651

© 2010, Published by the BMJ Publishing Group Limited.

SourceEHR #4

SourceEHR #5

Staging Database

ClinicalDatabase

NLP Rules

Intermediate Database

with curated data

Use-casespecific

interface

NLP-assisted human curation

Vendor

CancerLinQ

Data feedsto

CLQClients/Partners

InformaticsTeamRules

Engine

SourceEHR #2

SourceEHR #3

SourceEHR #1

MedicalCuration

Team

50

Real World Evidence (RWE)

N Engl J Med 2016; 375:2293-229712/8/16

51

Sources of RWD

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RCT evidence

Advantages Disadvantages

Complete

Accurate

Unbiased

Specified intervention

Standardized outcome

measures

Reflects “what can work”

(efficacy)

Slow, costly to obtain

Applies only to population

studied

Uninformative for older,

sicker patients seen in

practice

Control group may not

reflect contemporary

practice

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Real world evidence

Advantages Disadvantages

Captures outcomes of

patients in usual practice

setting

Responsive to changes in

practice

Readily available, quickly

Reflects “what does work”

(effectiveness)

Subject to bias

May be incomplete

Quality uncertain

Data elements and

outcome measures not

standardized

Heterogeneous population

may mask treatment effect

Hypothesis-generating

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What to believe?

RCT RWE

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Both essential to fill information gaps!

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But RWE challenges remain

How to define and capture RWE endpoints:• OS

• PFS

• TOT

• RR (RECIST vs clinician assessment)

• AEs

• PROs

Consent issues (opt-out vs. opt-in)

What high priority content areas are well-suited to

queries using RWE >> clinical trials?

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How can CancerLinQ transform clinical research?

Hypothesis generation from observational data, e.g., off-

label use, risk stratification

Patterns of care and trend analysis

Cohort identification, frequency of target pop.

Cohort assembly, location of target pop.

Eligibility assessment, trial matching

Registry-driven RCTs

Comparative effectiveness assessments

Treatment simulations

Collection of PROs

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CER in CancerLinQ - examples

Compare non-drug interventions in similar indications, e.g.,

treatment of liver mets

Compare outcomes for drugs within indication, e.g., treatment

of kidney cancer or myeloma

Compare outcomes of drugs used in populations excluded

from clinical trials, e.g., elderly, poor organ function, co-

morbidities

Compare outcomes of treatment decisions based on LDTs vs.

CDTs

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Personalized.

Predictive.

Precise.

Powerful.

Patient-

Driven.

What makes CancerLinQ unique?

CancerLinQ is being created by oncologists – for the oncology

community worldwide – to improve the quality of patient care

CancerLinQ incorporates ASCO’s practice guidelines and

QOPI quality measures. The real world evidence and

outcomes captured as part of the learning health system will

be linked back to the same guidelines and measures and

inform their development.

The CancerLinQ dataset is evolving into the largest and most

robust source of RWE ever assembled in oncology for CER

and discovery.

CancerLinQ is guided by ASCO’s mission to support all

physicians, in every community and every setting.

Thank You