identifying and reducing variation in the supply...
TRANSCRIPT
Identifying and Reducing Variation in the Supply Chain
April 14, 2015
Yohan Vetteth, MBA
VP of Healthcare Data & Analytics
DISCLAIMER: The views and opinions expressed in this presentation are those of the author and do not necessarily represent official policy or position of HIMSS.
Kari Bignell
Manager, Data Architecture
Conflict of Interest
Yohan Vetteth, MBA Kari Bignell
Has no real or apparent conflicts of interest to report
© HIMSS 2015 2
Learning Objectives Discuss the multidisciplinary approach used by collaborative care teams to implement an analytics platform that helps ask better questions to facilitate clinical and financial improvements
Evaluate the advantages of an adaptive data warehousing approach to build applications for clinical and business analytics
Discuss how the deployment of the Supply Chain
Discovery Application has the potential to improve
health outcomes for patients, cost savings, and
operational efficiency by reducing variation in care
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An Introduction to the Benefits Realized for the Value of Health IT
http://www.himss.org/ValueSuite
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Stanford Health Care
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Stanford Health Care
v2014
v10
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Clinical & Business Analytics
Stanford Health Care
Information Technology Services
2012 Established
5 Employees
2015 Current Day
35 Employees
Clinical & Business Analytics
Prioritized data-driven quality improvement initiatives
Apps designed for self-service discovery and metric tracking
Strong clinical and business context
Agile project development for evolving user needs
EDW
Reporting Analytics Advanced Analytics
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Guiding Principles
Make information more accessible
Single source of truth
Secure data
Avoid waste
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Iterative Multidisciplinary Approach
Evaluate outcomes measurements
Evaluate clinical relevance
Test improvement hypothesis
Modify as relevant
Standardize and
improve workflow
Deploy evidence-
based interventions
Define cohort criteria
Define measurement
metrics
Clinicians
Support
Services
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Agile Approach
Time to
Market
Design
Data
Focus
Ad-Hoc
Need driven
No consistency
Fragmented
Not always
accurate
Most data
modeling
upfront
Traditional BI
Technology
driven
Multiphased
4-6yr start-up
period
Original
requirements
Adaptive Data
Warehouse
New
functionality
along the
journey
Flexible
ONLY relevant
data tables
aggregated
Clinical
outcomes
Established 2012
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Source Systems
Epic Lawson
Architecture
Enterprise
Data
Warehouse
Clinical & Functional
Data Marts
Clarity
Reporting
Metric & Discovery
Apps
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=
Analytics Journey
Multiple reporting
sources
Inconsistent data definitions
Fragmented
Lack of governance causes confusion
and loss of credibility
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Analytics Journey
Avoid data staging
through extracts
Standardize technology
architecture
Build strong data
access and guidelines
Strong data governance
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Data Literacy
Metric Apps have
specific business
needs and require
strong business or
clinical context to
enable using the app
Discovery Apps
empower the users
to learn about the
subject area with
little to no business
or clinical context
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Paradigm Shift to Accountable Care
FROM TO
Variation in care Evidence based standards of
care
Fee for service Pay for performance
Segmented care Coordinated care continuum
Sick care Well care
Vague and unmanageable
cost structures
Cost containment and
transparency
How do we make healthcare less expensive? The levers are ugly.
We can pay doctors and hospitals less; or we can get them to reduce waste.
Dr. Atul Gawande
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Hospital Supply Chain
Incentives to build more accountable care-focused
capabilities have given health systems good reason to
evaluate their total cost structure
Supply-side economics
Purchasing practices at hospitals and health systems
continue to evolve, with the supply chain continuing to be a
target for large non-labor cost savings
As hospital consolidation continues…creating consistency within the
supply chain across the continuum of care will become increasingly
influential to the bottom line
Supply expense growth outpacing all others
Supply costs are growing faster than wages or benefits, driven by the rampant
proliferation of expensive devices. Physician preference items (PPIs) now
account for 60% of med/surg spend, compared with 40% a decade ago. More
than half of orthopedic procedures now use implantable devices, as do more
than one-third of cardiac procedures
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Finding Value
Lawson
SUPPLY
Accuracy of par levels
Over processing
Poor pricing
Epic
DEMAND
Accuracy of preference cards
Wasted Items
Expedited demand
How much does our
procedure supply use
vary between surgeons?
How much are we
wasting?
How accurate are our
preference cards?
What is our average
spend for a procedure?
How often are
we running for
supplies during
surgery?
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Finding Value
EDW
SUPPLY + DEMAND
Lawson
SUPPLY
Epic
DEMAND
SUPPLY CHAIN
DISCOVERY APPLICATION
Variation Insight
Waste Reduction
Cost Awareness
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Approach to Improvement
Challenge:
Single and elective surgeries
Identify high cost &
unnecessary variation
Balance those with high cost
& unnecessary variation with
cultural readiness and
engagement to drive
outcomes and improve quality
Cohort:
Focus:
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Understanding Usage Variation C
oeff
icie
nt
of
Vari
ati
on
Mean Supply Cost (or Quantity)
Biggest
Opportunity
Low Cost
Low Variation
Metrics Influenced:
• Supply Expense per
Adjusted Patient Day
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Measuring Variation - Direct Cost
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2 x 2 representation of
the Procedures
performed illustrating
greatest opportunity
Scorecard trending of
Procedures ranked by
opportunity
Understanding Item Variation To
tal
Un
iqu
e I
tem
s U
sed
Biggest
Opportunity
Mean Unique Items Used • Number of Items (SKUs)
• Inventory Turns
• Number of Preference Cards
• Item Pricing
Metrics Influenced:
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Measuring Variation - Distinct Items
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Search for items to see
how many surgeons
are using an item and
when they used it last
Focus in on highest
variation by Item
Group Types
Pilot Project - ENT Department
?
Pilot Focus: Otolaryngology Department
Two concepts, one view
Iterative, Rapid Development Approach:
Goal:
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Surgery Basket
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Compare how many
of each item is used
and the different
types of items used
across surgeons for
a specific procedure
Implement Intervention
Utilize the discovery app to:
Identify improvement areas
Facilitate conversations
Pinpoint items for removal from
preference cards and item master
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Analyze Results
Use Lean processes to help:
Update and streamline the preference
card system
Establish a solid baseline to enable
improvement measurement
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Conclusions
Evaluate the advantages of an adaptive data
warehousing approach to build applications for
clinical and business analytics
• Driven by clinical outcomes and not by the “IT bling”
• Supports a lean culture of continuous improvement
that requires agile and flexible solutions where
requirements are constantly changing
• Highly scalable design provides faster results for
future needs
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Conclusions
Discuss the multidisciplinary approach used by
collaborative care teams to implement an analytics
platform that helps ask better questions to facilitate
clinical and financial improvements
• Embedding IT with the clinicians and support services
provides a holistic approach and prevents rework
• Spend more time understanding the problem – the real
problem – versus just filling the specs!
• CBA members are viewed as content experts in the
subject matter, not just IT
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Conclusions
Discuss how the deployment of the Supply Chain
Discovery Application has the potential to improve
health outcomes for patients, cost savings, and
operational efficiency by reducing variation in care
• Transparency around item usage for similar
procedures and the challenges preference items bring
• Providing a mechanism to facilitate the appropriate
dialogue around variation of items and usage
• Conversations driven by clinicians around what items
to use enables stronger engagement and cost
containment
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A Review of Benefits Realized for the Value of Health IT
http://www.himss.org/ValueSuite
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Questions
Yohan Vetteth, MBA
VP of Healthcare Data & Analytics
Kari Bignell
Manager, Data Architecture
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