The document discusses the transformative role of AI and advanced analytics in oncology care, emphasizing their potential to enhance clinical decision-making and patient outcomes while addressing financial and ethical implications. Key topics include the integration of big data, personalized treatment approaches, and the shift toward value-based care. It also highlights challenges such as technology resistance, ethical considerations, and the importance of data management in optimizing healthcare delivery.

1. Disrupting the Oncology Care
Continuum Through AI and
Advanced Analytics
US Cancer Specialist
Michael Peters, MBA, CMC®, CSSBB, R.T.(R)(T)

2. US Cancer Specialist2
Disclosure: No Conflict of Interest
With respect to the following presentation, there has been
no relevant (direct or indirect) financial relationship
between the party listed above (and/or spouse/partner)
and any for-profit company in the past 24 months which
could be considered a conflict of interest.
Disclosure

3. US Cancer Specialist3
Agenda
• Defining AI, Advanced Analytics, Clinical Decision
Support
• The Analytic Paradigm shift in Oncology
• Technology Benefits & Limitations
• Financial Considerations and Implications
• Social Resistance to Technology Change
• Ethical and Regulatory Obstacles to Care
• Conclusion

4. AI and
Advanced Analytics
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• Help
physicians to
make more
informed and
accurate
decisions
faster.
• Cull New
Insights into
existing EMR
data
Use of algorithms and software to
approximate human cognition in the
analysis of complex medical data.*
Artificial Intelligence (AI) in Healthcare

6. • Predictors for readmission/recurrence/survival
• Predictors for mortality/morbidity
• Predictor of Clinical Choice Pathway (Financial)
• Health Choice Incentives
Advanced analytics involve more predictive queries
that inform prescriptive actions.
Big Data and Advanced Analytics
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7. Clinical Decision Support
Support or provide recommendations to a health care
professional about prevention, diagnosis, or treatment of a
disease or condition.
Display, analyze, or print medical information about a
patient or other medical information (Peer-reviewed clinical
studies and clinical practice guidelines).
Independent review of recommendation to make a clinical
diagnosis or treatment decision.
Reduce errors and improve
efficiency, standardization,
and cost savings.
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8. Information
Optimization
Foresight
Insight
Hindsight
Difficulty
Value Flow of Analytics
What
Happened ?
Why did it
Happen ?
What will
Happen?
How to Make it
Happen !
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Automatic
Decisions !

9. The Analytic Paradigm
shift in Oncology
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• Personalized vs cohort approaches, N=1
• Genomic sequencing
• Immunotherapy
• Targeted Treatment
• Combination Treatment
• Neo-adjuvant Treatment
• Scaling, access, quality of care issues
• Move from pay-for-service model to value-based care
model.
• Delivery of Evidence Based Medicine.
Analytics Driving Personalized Care

11. Oncology Care: N of 1
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Radiation Oncology “AI” Today
Pre-Loaded Treatment Planning Models
• Utilization of other Leading Institution Protocols
Ability to build/adapt to one’s own Standard of Care Models
• Facility preferences based on outcomes
Automated Contouring/Segmentation
• Improving Efficiency especially in OARs
Surface Guided Laser Alignment Systems
• Improves treatment accuracy

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RO Clinical Decision Support
• Preplanning prediction of dosimetric tradeoffs, assisting
MDs, patients and payers, to make informed decisions
about treatment and dose prescription.
• Integration of dosimetric information with genomics,
proteomics, diagnostic imaging, and EMR, to build
accurate Tumor Control Probability (TCP) and Normal
Tissue Complication Probability (NTCP) models.
• Radiomics, primary extraction of
quantitative imaging to predict various
clinical changes. (Predictive Analytics)

14. Improving Healthcare
Improve Quality and Safety
Optimize Resource Utilization
Evidence-based Outcomes
Right Value for Care
Increase Patient Satisfaction
Cost
Effective,
High Quality
Patient-
Centric Care
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15. Technology Benefits and
Limitations
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Structured Data:
• EHR
• Spreadsheets
Un-Structured Data:
• SDoH*
• Mobile Health
• Text Messaging
• Email
• Social Media
• Live Chat
• Patient Portal
• Tele-health
--------------------------------------
Tip of Data Discovery
*Social Determinants of Health

17. Relational Databases
Use “metadata,” information that describes the data,
providing information about the data source, data
collection methods and meaning
Tabular structure keeps data well organized, accessible,
and relatively easy for people to view and understand
In relational databases, the exact kind and quantity of
data is always known
Relational databases are
well-suited to conventional
data analysis such as reporting
and classical statistical analysis.
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18. Don’t use the normalized data model found in
relational databases. They don’t organize data in
tables, rows and columns.
Do not use the SQL query language for data
access. They use alternative languages which are
suited to their data models.
Big Data, that is data which pushes the limits of
conventional data management technology, is
difficult or impossible to manage with relational
databases.
Manage the Big Data that is compelled by
quantity and other characteristics effectively,
compared to conventional relational databases.
NoSQL databases
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19. • Report or Dashboard
Driven Analytics
Answering the known:
• Volume/Activities
• Charges
• Diagnosis
• Missing out on the Key
Contributors
• Risk/Outcomes
• Actual care costs
• Attrition
• Performance
• Population Health
$0
$10,000
$20,000
0 20 40 60
CostperPatient
Patient Volume
Cost vs Volume
Gamma Knife
(Mult)
Gamma Knife
(Single)
IMRT
Head/Neck
IMRT Prostate
Radiation Oncology Analytics Today
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20. • Data Analyzation
(qualified staff)
• Appropriateness of data
(capture the right information)
Today’s Limitations:
People, Process, Technology
• Discreteness of data
(capture in the right format)
• How easy it is to extract the
data (capture in an accessible
way)
• True Analytics
(interoperability)
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21. Financial
Considerations and
Implications
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22. ($3,938) $4,354 ($1,104)$6,702
*Advanced
Analytics
*A I
*Labor
Hrs.
*Costs
Economic and Financial Constraints
Improve Quality
$11,713
$19,317
$8,368
$16,483
$18,415
$15,379
$12,722
$15,379
IMRT 30 Fx H&N Tomo SBRT 5 Fx H&N IMRT 15 Fx Pelvis Tomo SBRT 5Fx Pelvis
Treatment Technique Cost/Reimbursement Analysis
Cost of Care HOPPS CMS Allowable
22
Actual Cost of Care calculated at a
community based hospital on the West
Coast
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Yr. 1 Yr. 2 Yr. 3 Yr. 4 Yr. 5
New/Active 250 500 750 1000 1500
Continuing Care 250 750 1500 2500
Yr. 1 Yr. 2 Yr. 3 Yr. 4 Yr. 5
New/Active $125k $250k $375k $500k $750k
Continuing Care $50k $150k $300k $500k
Typical Cloud Based Software Management and Service Agreement
Total New Patients and Continuing Care
Initial Capital Expenditure
Initial Infrastructure/Configuration
$2.5M-$3.0MInterface Development
Deployment/Training/Clinical Expertise
AI Financial Investment

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Money is in the Data
$$ = Software Sales,
Hardware, Support
$$$$ =
Repurposing/Reselling
Data
$ = Ensure Your %
of Resell

25. Ethical and Regulatory
Obstacles
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26. Ethical and Regulatory Obstacles
• Would it be ethical to have a human make
‘‘corrections” to a tumor volume?
• Would it be medically defensible to select a plan
disfavored by an AI-based plan comparison?
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27. Social Resistance to
Technology Change
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• Privacy
• Transparency & Equality
• Economic impacts
• Governance & accountability
• Managing AI risk
• AI morality & values
• Changing Healthcare Paradigm
Social Resistance to Change

29. Public Good vs Patient Privacy
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Patient Healthcare Records protected by HIPPA
• Requires anonymized Data
Fitness Devices/Search Engines Unregulated
• Can marry anonymized patient dossiers with
named consumer profiles.
Healthcare data has delivered deep insights into care delivery
processes and outcomes.
• AI has the potential to improve outcomes by 30 to 40
percent at the same time the costs of treatment by as much
as 50%.*
*2016 Frost and Sullivan Study

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• Precision Genomic and Proteomic Radiation Therapy
• Integration of clinically relevant data from multiple
sources (e.g. EHR, imaging data) to further tailor online
adaptive planning.
• Standardized processes and data structures amongst
institutions for comparative treatment analysis.
Radiation Oncology “AI” Future

31. Conclusion
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Just the Facts
• Structured Data is the most Common
Big Data in Healthcare
• Unstructured Data holds most
Clinically Valuable Healthcare Data
• Radiation Oncology is in a silo when
contributing to the Big Data and AI
movement.
• AI positioned well in other healthcare
and oncology specialties.
• Healthcare Improvement is geared
toward Cost Effective, High Quality
Patient Centric Care; Advanced
Analytics and AI can meet that
improvement initiative.

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Time for Change

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Thank You!
Email: mpeters@uscancerspecialists.com
Cell: 727-487-2840
Web: uscancerspecialists.com