Using Data to improve patient care

1. Paddling Upstream:
Data Capture to Improvement
Cynthia Davis, Principal

2. Who We Are
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3. Mission
CIC Advisory is a nationally recognized leader in
helping healthcare organizations use technology to
provide high quality and safer care for patients,
families and communities.
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4. How We Are Different
Improving care and efficiency results requires
better processes rather than new or better tools.
Better processes are a people solution: using data
effectively calls for strategic and thoughtful
governance.
Effective leadership is also necessary to ensure
underlying data integrity.
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5. Healthcare Analytic Adoption Model
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6. Where is the Stream Going?
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7. The Healthcare Challenge
How to best manage the
dynamics of the shift
Results (quality, ROI)
Operational (efficiency, productivity)
Clinical (analytics, workflow)
Technology (infrastructure, data)
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Regulatory (payment reform, meaningful use)
Financial (risk, referrals, revenue)
Network (owned, affiliated, contract)
Legal and Governance (antitrust, liability)

8. Moving Upstream
Define:
Triple
Aim
Care team
engagement
Control
Workflow
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Improve
HIT
Measure
Analyze
Analytics

9. Direction Moving Upstream in Alignment
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Configure and Adopt New HIT Tools
Triage and Select Interventions
Coordinate Care
Assess Patient Compliance
Evaluate Outcomes
Determine Intervention Efficacy
•
•
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•
Care Team
Engagement
Connect Patient Data
Improve Speed and Accessibility
Enable Workflow
Improve Adoption, Usability
Define:
Triple
Aim
Control
Measure
Workflow
HIT
Improve
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•
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Automate Care Guidelines
Enable Care Team
Support Patient Engagement
Capture Compliance Measures
Align Performance with At-Risk Contract
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Analyze
Analytics
•
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Transform and Stratify Data
Identify and Prevent Risk
Alert Physicians, Patients
Measure Quality, Cost Performance
Determine Intervention Efficacy
Measure At-Risk Performance

10. Navigating the Stream with Paddle Alignment
Intervention Efficacy
Track Process,
Outcome:
Care Management
Define:
Triple
Aim
Integrate The Data:
Single Patient Record
Control
Measure
Triage,
Intervention Selection:
Care Management
Transform and Stratify:
Population Analytics
Improve
Analyze
Predict and Alert,
Population Analytics
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11. Enablers of Smooth Paddling:
The Quality and Quantity of Your Data
• Goals and
Preferences
Place
Time
• Interventions
• Drugs
• Therapy
• Education
• Symptom
• Management
• Visits
• Location
• Providers
• Behavior
• Activity
• Nutrition
• Outcomes
Connections
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12. How to Paddle
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13. Transformation Approach
“Maintain and improve”
Sustain
“Take action”
7. Don’t let up
8. Create a new culture
Conversion
“Set the Stage and
Decide What to Do”
Start hardwiring: transformation action plan
Engage
4. Secure buy-in
5. Empower team members to act
6. Create short-term wins
Prepare
1. Create urgency
2. Pull together a guiding council
3. Develop transformation vision and strategy
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Adapted From: Kotter J. Our Iceberg Is Melting. 8-step process for successful change. Pages 130-131

14. The Overall Process:
Methodology + Technology
1. Break Ground: Start building Level 0 and Level 1
infrastructure
2. Analyze Opportunities: Run the Key Process Pareto
Analysis to identify clinical and cost variability
3. Prioritize: Select the care processes or disease states for
targeted improvement
4. Organize: Establish the clinical process improvement teams
5. Set the Goals: Choose the AIM (Analytically Informed
Medicine) statement
6. Measure Precisely: Build the underlying Subject Area Mart
for precise analytics
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15. The Four Key Questions
Who are we monitoring?
Identify patients with condition or
attribute of interest
What are we measuring?
Patterns of best practice, clinical
outcomes, costs
What are our goals?
Targets for adherence,
outcomes and costs
How will we achieve them?
Cultural behaviors
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Protocols, orders,
workflows…modifi
cations to the EMR

16. Data Governance (why)
We did a great Data
Cleanup effort last year but
the data is corrupt again.
Shouldn’t that effort made
sure that errors don’t creep
in again?
I get two different results
from two different systems
and, guess what, they are
both wrong
I need to do a study/research
that looks at our revenue for
the last 5 years. I was told
that the data is all archived but
no one knows where it is or
how to retrieve it.
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I clean up the data
that is usually
unclear and
inconsistent when
I receive it
I am trying to determine why
we have different formulas
for inventory in different
systems.
Doesn’t anyone own or have
responsibility for this data
through the company?
I just reviewed a Data Model
of our business to prepare
for an acquisition. It looked
very nice but it used
indecipherable terminology,
a very old business model
and not current

17. Data Governance Benefits
• Data Governance (DG) are rules, policies, procedures, roles,
and responsibilities that guide overall management of data
• Governance provides the guidance to ensure that data is
accurate and consistent, complete, available, and secure
• Governance body based on people, process, & technology
• Data centric organization
• Master Data Management
• Prepare us towards effective and efficient changes
• Comprehensive and accurate data gives us the competitive
advantage in the market area
Data Governance helps us to manage our date
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18. Use Case Discharge Location
Need:
• At D/C patients need to have correct discharge location
entered in XYZ
• Data will be used for Readmission Analytics and for
follow up survey call
Issue:
• Require entry in 2 systems to D/C a patient
• Current process follows two slightly different workflows in
XYZ for FirstNet and IP
• Data is a radial button selection for disposition but free
text detail for location
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19. Team Process
• Team selection:
– Target– Delivery (cohort)
– Members: Knowledge Manager, Subject Matter Experts
(both Physician & Nurse), Data Analyst, and Data Architects
– Kick-off meeting
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•
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Weekly work meetings
Identified Aim Statement & long term goal
Selected, built, and refined metrics
Created dashboard visualizations to display metrics
Published dashboards for targeted audience
Identified next Aim Statement -> repeat process
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20. Data Quality Issues
• Gestational age charted in four different locations in
EHR[clinical peer groups changed operational use of EHR]
• Chart times for Stage 1 labor duration present only 68%
[clinical peer groups changed operational use of EHR]
• Errors in documentation (nurse name as provider for
C-Section) [correction process with Health Information Mgt]
• Delivery records missing the ‘Marked as Delivered’ indicator
[tracking process on units & correction process with HIM]
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21. Indication For C-Section – The Problem
Not documented 15% of the time.
When documented,
it was not informative.
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22. Next Steps: Requirements
Systematic Approach across all setting with effective Clinical and Data Governance
Communication and Collaboration tools that assist providers and coordinators
Resources to perform coordination tasks
• Proximity and close working relationship between coordinators and clinicians
• Patient contact with a coordinator, 24x7 access
Initial Focus on transitions of care, medication management, notification of and access to info on
hospitalizations, ED visits
Patient Engagement (Activation)
Health Information Exchange
Teleservices, Mobile Health
Analytical Tools to Manage Populations – assess medical/socioeconomic risk, define populations at risk,
measure outcomes
• Integrations of Financial and Clinical data to support analytics
• Ability to operate in Multiplayer Environment, different risk models
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23. Thank You
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