Kaushal Parekh, Principal Health Data Architect, Roche. Sujata Patil, Engineering Manager driving FHIR®, Roche. "Oncology data on FHIR®" Explore how Common Oncology Data Elements defined in FHIR® can help capture meaningful data for cancer patients that would result in higher quality health outcomes by providing better analytics and actionable insights.

1. FHIR Meetup
Oncology Data on FHIR

2. Kaushal Parekh is a Principal Health Data
Architect at Roche leading the FHIR Data
Models and Terminologies track. He is an
experienced leader with a demonstrated
history in the health care and life sciences
industry.
https://www.linkedin.com/in/kaushal-parekh/
Sujata Patil is an Engineering Manager at
Roche driving FHIR based Terminology
Services efforts. She is a hands-on technical
leader with a rich background in the
healthcare industry.
https://www.linkedin.com/in/sujata-patil-32b7823/
About Us...

6. Human Resources – New Skills & Talents
Looking for leaders to help drive find and develop new
capabilities
New skills in analytics &
software enginieering
Passion
Eagerness to learn
Empathetic
Collaborative
Technology-based problem
solving
Security
Cloud
Mobile
UI design
Work with purpose at critical time
in industry transformation
Unique company mission, values
Divers, creative, flexible, inspiring,
fun + industry-leading & innovative
workplace
Strong, collaborative company
culture
Professional growth opportunities
Shared office spaces: team- &
mobile work, creativity
New recruiting activities to win
talents from technology experts

7. Roche DIS
Focus on Clinical Decision Support Applications

8. Cancer care and healthcare data are more and more complex!
Choices depend on :
• Patient Demographics & Preferences
• Cancer Diagnosis
• Labs, Vitals, Biomarker/Genomic Results
• Previous Treatments & Results
• Healthcare Coverage
• Clinical Guidelines
• Eligible Clinical Trials

9. Problem: Missed Learnings from millions of treatments :-(
Standard
Data Model
Advanced
Technologies
Standard
Data Capture

10. Every Patient’s Journey improves all future care :-)

11. A Clinical Data Repository for our CDS products
Why use the FHIR standard?
• Support a CDS application ecosystem.
• One consistent source and view of clinical data.
• Future proof interoperability and other regulations.
• International cross-functional consensus process.
• Solid foundation yet flexible.

12. • Information models
• Constraints
• Terminology
• Usage
https://www.hl7.org/fhir/overview-arch.html
FHIR Overview
Resources and APIs

13. Adopting FHIR
Defining our Data Model
• FHIR Resources designed with the 80/20 rule.
• Customization and extension for specific use cases.
• Few implementation guides.
• Understanding the clinical domain.
• Solid logical modeling and profiling exercise.
https://www.hl7.org/fhir/resourceguide.html

14. Representing Cancer Disease Attributes
An (oversimplified) logical model
Patient
Cancer
Primary
tumor
Metastatic
tumor
Stage
Histologic
Type
Location
Location
Histologic
Type

15. FHIR Profiling...
Mapping use cases to FHIR resources (Oversimplified)
Patient
Condition
subject
BodyStructure
bodySite
Observation
stage
{
"coding": [
{
"system": "http://snomed.info/sct",
"code": "82711006",
"display": "infiltrating ductal carcinoma"
}
]
}
SCT#76365002(structure of breast upper outer quadrant)
morphology
location
valueCodeableConcept
code
SCT#399390009(Tumor-node-metastasis (TNM) stage
grouping)
AJCC#IIA (Stage II A)
http://build.fhir.org/ig/HL7/fhir-shorthand/

16. • HL7 FHIR Breast Cancer IG
http://hl7.org/fhir/us/breastcancer/2018Sep/index.html
• ASCO mCODE initiative
http://hl7.org/fhir/us/mcode/
Oncology Specific FHIR standards
Community initiatives for standardization

17. Dealing with Clinical Terminology
Too many standards, not enough standardization!
• Free text
• Many standards.
• Mapping/transcoding.
• Enabling Named Entity Recognition.
• Semantic querying.

18. Free Text Data Capture Debacle
Diagnosis
Small cell lung cancer
Diagnosis
Lung SCC
Diagnosis
Small cell carcinoma of lung
Diagnosis
Small cell carcinoma,
lower lobe of right lung
All above data entries are for the same general disease!!!
Difficult to Aggregate
query for all patients with lung cancer
Ambiguous
Different levels of granularity
No Validation
Free text ... typos

19. SNOMED-CT (vs ICD-10 vs ICD-O-3) for Cancer Diagnosis Coding
Need for semantic mapping
Malignant neoplasm of breast (disorder)
SCTID: 254837009
Carcinoma of breast (disorder)
SCTID: 254838004
Disease (disorder)
SCTID: 64572001
Clinical finding (finding)
SCTID: 404684003
SNOMED CT Concept (SNOMED RT+CTV3)
SCTID: 138875005
ICD-10
Carcinoma of breast - upper,
outer quadrant (disorder)
SCTID: 286895009
Malignant neoplasm of breast
upper outer quadrant (disorder)
SCTID: 37308300
ICD-10
Breast structure (body structure)
SCTID: 76752008
Anatomical structure
(body structure)
SCTID: 91723000
Malignant epithelial neoplasm -
category (morphologic abnormality)
SCTID: 399879007
Structure of upper outer quadrant
of breast (body structure)
SCTID: 76365002
Body structure (body structure)
SCTID: 123037004
Neoplasm and/or hamartoma
(morphologic abnormality)
SCTID: 400177003
Infiltrating duct carcinoma
(morphologic abnormality)
SCTID: 82711006
ICD-O-3
topography
ICD-O-3
morphology
ICD-10
ICD-O-3
morphology
ICD-O-3
morphology
ICD-O-3
topography
direct/indirect parent
finding site
associated morphology
Infiltrating duct carcinoma
of breast (disorder)
SCTID: 408643008
SNOMED
only
SNOMED
only
SNOMED
only
ICD-O-3
topography

20. General Healtcare
• SNOMED-CT
• LOINC
• RxNorm
• FHIR ValueSets
• RadLex
• ICD-10
• ..
Oncology specific
• ICD-O-3
• NCI Thesaurus
• NAACR
• AJCC / UICC (TNM Staging)
• ...
Relevant terminology systems/standards
Not a comprehensive list

21. Imagine A Future Where…
…a connected healthcare ecosystem enables personalised
patient care
ACADEMIA
MEDICAL ASSOCIATIONS
REGULATORY BODIES
PAYORS
INDUSTRY
HEALTHCARE
PROVIDERS
GOVERMENT
NON-PROFITS
Increasingly personalized
healthcare
Democratization of healthcare
data
PATIENT-CENTRIC
DATA-DRIVEN
VALUE-BASED
21
1950 1960 1970 1980 1990 2000 2010 2020 Future

23. Doing now what patients need next