The document discusses upcoming changes to health system funding in Ontario through initiatives like the Health Based Allocation Model (HBAM) and Quality-Based Procedures (QBP) that will affect funding for cancer surgery programs. It emphasizes that accurate data reporting will be critical for preparing for these reforms and receiving appropriate levels of funding. The Decision Support Department can help cancer programs ensure data accuracy and provide recommendations based on evidence to facilitate successful implementation of health system reforms.

1. Health System Funding
Reform and You
Data accuracy and its importance for the Cancer
Surgery Program
Decision Support

2. Agenda
• Key messages
• Health System Funding Reform (HSFR) overview
• Heath Based Allocation Model (HBAM) and its impact on the
Cancer Surgery Program
• Quality-Based Procedures (QBP) and its impact on the Cancer
Surgery Program
• Importance of data accuracy for the Cancer Surgery Program
• Introducing the Decision Support Department
• Q & A
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3. Key Messages
• Health System Funding Reform (HSFR) will affect
the funding and clinical operation of the Cancer
Surgery Program.
• Data accuracy will help us prepare and anticipate
full implementation and revisions of HSFR.
• The Decision Support Department at Trillium Health
Partners will provide you with evidence-based,
actionable, and clinically-relevant recommendations
based on the accurate data your collect.
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4. Health System Financial
Reform (HSFR)
• Moving away from historical-cost based funding system (i.e.
global system)
• Heavy reliance on data reported to CIHI
• Two components:
1. Health Based Allocation Model (affects departments with
Inpatient/Ambulatory patients categorized under Neoplasm)
2. Quality-Based Procedures (affects POCUs operating on
Cancer Surgeries)
• Will represent 70% of total funding when fully implemented (30%
remaining still under global system)
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5. Health System Financial
Reform (HSFR)
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Before HSFR After Complete Implementation
30%
40%
30%
100%
Global System HBAM QBP

6. Health Based Allocation
Model (HBAM)
• Increase resource utilization efficiency
• Expected weighted case X Expected unit cost =
Funding
• Expected weighted case: Uses data from Discharge
Abstract Database (DAD), National Ambulatory Care
Reporting System (NACRS) plus Stats Can population data
• Expected unit cost: data from MIS FC, derived from linear
regression of numerous hospitals (regression model not
published)
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7. Financial Implication of
HBAM
• Neoplasm Acute Inpatient in 2014: 7,000 (70,000
Acute Inpatients X 10% Neoplasms cases)
• Final HBAM Expected Unit Cost in 2014: $5,500
• Approximate funding: $38.5 M
• Given that expected weighted cases (i.e. patient
demographic & grouping) are consistent, 10%
excess in actual unit cost compared to expected
unit cost will equate to $4 M budget deficit.
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8. Reaching HBAM Efficiency
1. Proactive in identifying clinical/population trend
(i.e. anticipate expected weighted case)
2. Benchmark healthcare supply/overhead utilization
(i.e. control actual unit cost)
3. Reduce healthcare supply cost (i.e strategic
sourcing)
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9. Quality-Based Procedures
(QBP)
• Aimed to provide better quality of care, improve clinical practice,
enhance patient experience, and potential cost-savings
• Influence the amount and method of funding of procedures covered by
QBP
• Cluster patients based on related Dx or Tx, and attach an expected cost
per procedure assuming hospitals have adopted clinical best-practices
• Number of Procedures X Expected Cost per Procedure = Funding
• Use data from Discharge Abstract Database (DAD) and National
Ambulatory Care Reporting System (NACRS) (also used for HBAM)
• Wave two of QBP will include Cancer Surgery for Q3 of 2014-2015
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10. Financial Implication of QBP
• Number of Cancer Surgeries: approx. 1,200 (Total
Day Surgeries in Canada 228,000 X 5.3% Day
Surgery marketshare X 10% Neoplasms Surgeries,
for Trillium Health Partners in 2014)
• Expected Cost per Procedure: $4,600
• Budget: $5.5 M under QBP
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11. Financial Implication of QBP
• Cancer Care Ontario (CCO) helps the Ministry of Health to
allocate funds through Cancer Surgery Agreements (CSA).
• Each participating hospital have to meet the targets outlined in
the CSA.
• Funding from the Cancer Surgery Agreement (CSA) will be
gradually transferred to QBP (~20% all cancer surgery funding
in Ontario).
• FY15/16, prostate and colorectal cancer will not be part of CSA,
a financial implication of $420,000 (prostate and colorectal
cancer represent 38% of newly diagnosed cases X $5.5 M X
20% CSA portion).
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12. Specialties that will be
influenced by QBP
• Gastrointestinal: Colon, Rectal, Stomach
• Hepatobiliary: liver, biliary, pancreas
• Thoracic: Lung, esophagus
• Breast Cancer
• Thyroid
• Genitourinary: kidney, bladder, testis, adrenal gland
• Prostate
• Gynecology: Endometrium, Cervical, Ovarian, Vulvar
• Ophthalmic
• Head & Neck
• Sarcoma: Bone, Soft Tissue
• Neurology: brain, spinal
• Skin (including melanoma)
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13. QBP Metric for Cancer
Surgery (Prostate & Colorectal)
Data sourced from Discharge Abstract Database (DAD)
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14. Future QBP Metrics
• Consult / Pre-treatment Assessment (e.g.
number of pre-op consultations)
• Follow up (e.g. post-op infection rate)
• Data will be sourced from National Ambulatory
Care Reporting System (NACRS), Continuing Care
Reporting System (CCRS), or National
Rehabilitation Reporting System (NRS).
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15. Reaching QBP Standards
1. Early assessment of current clinical practice &
implications of QBP
2. Clinical process remapping according to QBP-
identified best-practice guideline
3. Adopt clinical scorecard with the aim of being QBP
compliant
4. Facilitate departmental change management
5. Identify and anticipate future QBP quality metrics
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16. Key to HSFR Implementation
Success
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17. Key to HSFR Implementation
Success
• Data
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18. Key to HSFR Implementation
Success
• Data
• Data
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19. Key to HSFR Implementation
Success
• Data
• Data
• More Data!
Yes
Captain?
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20. Data Accuracy & HBAM
Efficiency
1. Proactive in identifying clinical/population
trend (i.e. anticipate expected weighted case)
• Accurate documentation of NACRS (e.g. patient
demographic components, comorbidity) will allow
better forecasting of case mix.
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21. Data Accuracy & HBAM
Efficiency
2. Benchmark healthcare supply/overhead
utilization (i.e. control actual unit cost)
• Precise and fair (weight-adjusted) benchmarks
require accurate MIS FC (e.g. nursing hours), and
NACRS (e.g. interventions), and cart (SAP) data.
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22. Data Accuracy & HBAM
Efficiency
3. Reduce healthcare supply cost (i.e strategic
sourcing)
• Better contract prices and negotiating position
require accurate MIS FC (e.g. product spend per
cost centre) and SAP data.
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23. Data Accuracy & QBP
Standards
1. Early assessment of current clinical practice &
implications of QBP
2. Clinical process remapping according to QBP-
identified best-practice guideline
• Need accurate data to assess current level of QBP
compliance and predict post-remapping metrics
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24. Data Accuracy & QBP
Standards
3. Adopt clinical scorecard with the aim of being
QBP compliant
4. Facilitate departmental change management
• Accuracy of clinical scorecard depends on the
availability and quality of selected metric (e.g. LOS)
• The tractability and continued commitment of
change management depends on frequent
milestone updates (not necessarily CIHI data)
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25. Data Accuracy & QBP
Standards
5. Identify and anticipate future QBP quality
metrics
• Additional metrics will be introduced gradually (e.g.
post-op hematoma < 4/1,000 cases). Keeping all
QBP related data up-to-date will ensure less time
commitment down the road.
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26. The Bottom-line
• Coding must be appropriately assigned to Case
Mix Group/HBAM Impatient Group (CMG/HIG).
• If data is inconsistent, the Cancer Surgery
Program will not receive consistent and
appropriate level of funding.
• The financial stress ultimately results in patient
care quality and safety risks.
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27. A Little Overwhelming?

28. Decision Support to the
Rescue
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29. Decision Support to the
Rescue
• Work in conjunction with the clinical team to ensure
data accuracy
• Troubleshoot complex cases
• Create easy-to-follow decision support tools based
on accurate data
• Decisions recommendations will be easy to
implement in clinical practices
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30. Contact Information
• Gary Spenser (Mgr. — Decision Support)
• XXX-XXX-XXXX
• Mary Eleid (Consultant — Decision Support)
• XXX-XXX-XXXX
• Peter Zhang (Sr. Consultant — Decision Support)
• XXX-XXX-XXXX
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31. Q & A
Decision Support

32. References
• Ontario Hospital Association (2014). Toolkit to Support the Implementation of
Quality-Based Procedures.
• Canadian Cancer Society (2014). Canadian Cancer Statistics.
• Ministry of Health and Long-Term Care (2012). Quality-Based Procedure.
• Ministry of Health and Long-Term Care (2015). Quality-Based Procedure
Clinical Handbook for Cancer Surgery.
• Ministry of Health and Long-Term Care (2013). Online Self-Study, Module 1-6.
• Ministry of Health and Long-Term Care (2011). HBAM, Phase 2 Education -
Regional Consultation Session Toronto Central LHIN.
• Ministry of Health and Long-Term Care (2013). HBAM 2012-13 Results -
Hospitals.
• Ministry of Health and Long-Term Care (2013). HBAM Service Component Tool
2014,V11.
APA format available upon request
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