the evolution of population health management€¦ · portal/phr care management activities...
TRANSCRIPT
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The Evolution of Population Health Management March 1, 2016 Terri Steinberg, MD, MBA Chief Health Information Officer & VP, Population Health Informatics Christiana Care Health System
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Terri Steinberg, MD, MBA
Have no real or apparent conflicts of interest to report.
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Today’s Agenda
Introduction
Learning Objectives
Value of Health IT STEPS
History
Blueprint for Success
Successes/Outcomes
Lessons Learned & Recommendations
Value of Health IT STEPS
Q&A
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Illustrate best practices for a data-driven
population health management program 1
Learning Objectives
Explain various aspects of a
comprehensive care coordination program 2
Identify technology components
used to manage population groups 3
Illustrate nuances of analytic
assignments of population risks 4
Recognize potential pitfalls for
technology-enabled population health 5
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Savings
Patient Engagement &
Population Management
Treatment/Clinical
Electronic Secure Data
http://www.himss.org/ValueSuite
T
E
P
S
Satisfaction S
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Christiana Care Health System
53,684 Admissions
21st in the nation
40,684 Surgeries
24th in the nation
172,510 ED Visits
25th in the nation
6,594 Births
33rd in the nation
286,770 Home Health
Care Visits
567,988 Outpatient Visits
196,930 Primary Care
Office Visits
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2012 CMS Grant
Awarded
$3M, Three-year
grant focused on
ischemic heart
disease
2013 Care Management
Programming
Basic data
integration
2014 Predictive
Analytics
Full data
integration
16 2015
Expand to Other
DM Programs
Bundled Payments
Risk
Contracts
Population Health Management Timeline
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Components for Effective Population Management
Technology Analytics
Care
Coordination
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Care Coordination - CARELINK
Pharmacists
Medical
Directors
Social
Worker
Support
Staff
Case
Manager
Manager
PATIENT
AND
FAMILY
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Clinical Programming
Pharmacists
Medical
Directors
Social
Worker
Support
Staff
Case
Manager
Manager
PATIENT
AND
FAMILY
Ischemic Heart Disease
VNA
Bariatric Program
Healthy Beginnings
Project Engage
Joint Replacement Program
Independence at Home
Cancer Program
Inpatient Discharge
Planning
Cystic Fibrosis Program
ED Case Management
HIV Program
Surgery
Medical Home Without Walls
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Technology Components
• Data Lake (Homegrown)
• Operational Data Store (Homegrown)
• Care Management Application (Aerial™/Medecision)
• Predictive Analytic Engine (Neuron/Coldlight Systems)
• Member Portal (Aerial/Medecision)
• Later additions:
– Provider Network Management (Aerial/Medecision)
– Utilization Management (Aerial/Medecision)
– Regression Analytics (Aerial/Medecision)
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Core System Data Contributors
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CCHS Systems of Interest
Neuron
Data Consolidation
Data Lake
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Three Functional Components
Aerial™
Population
Health
Management
Neuron
Predictive
Analysis
Portal/PHR
Care
Management
Activities
Clinical
Views
Communication
BP Cuff Scale
Insulin Pump
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Population Health Technology Challenges
• Patients receive care in many ways, from many healthcare
business entities.
– Not all members managed in our population health programs
are our patients.
• Data are not integrated across health care organizations.
• Communication to providers across business entities is
fractured and difficult.
• A single outcome and management view that crosses
business entities has not previously been developed.
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Data Analytic Goals
• Risk-stratify the population.
– Identify patients who need more or less care.
• Learn about data patterns and correlations that are unexpected.
– Notify Care Coordinators about members at risk.
– Identify data patterns associated with low cost and high quality.
– Trigger Care Coordinators when adverse events occur or are likely to
occur.
• Manage the care utilization for member groups.
– Approve services and procedures.
– Identify gaps in care.
• Understand and manage the performance of a clinically integrated
network.
– Provide quality and cost reporting
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Manual Risk Processes are Automated
The system continuously runs
the algorithm and re-scores the
patient; changes in status are
presented to the care team.
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Risk Prediction – Clinical Algorithm
Discharge Date Last Name First Name Neuronge Percentile Ranking
1/5/15 17:32 Smith Joan 0.210863 67 Lower Risk
1/5/2015 15:34 Allen Tim 0.357509 92 Top 10%
1/5/2015 14:46 Underman Sarah 0.441008 97 Top 5%
1/5/2015 14:07 Milan Joseph 0.192883 61 Lower Risk
1/5/2015 12:51 Moore John 0.141418 40 Lower Risk
1/5/2015 12:50 Hall Lisa 0.249838 78 Lower Risk
1/5/2015 12:15 Menden George -0.00035 0 Lower Risk
1/5/2015 11:54 Williams Bob 0.691852 99 Top 5%
1/5/2015 11:31 Morman Vince 0.225231 72 Lower Risk
1/5/2015 11:20 Geiss Lynn 0.221296 71 Lower Risk
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Model Calibration
0%
10%
20%
30%
40%
50%
60%
70%
80%
1 2 3 4 5 6 7 8 9 10
All Patients
PE Model 1 Iteration 1 Model 1 Iteration 2
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Blueprint for Success
Well-Trained Care management team
Strong Technology infrastructure
Buy-in From stakeholders
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Improved Workflow
• 50% reduction in the number of process
steps required for a care manager to
access and process program participant
readmissions
• Increase in productivity
– 43% reduction in overdue tasks
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Clinical Parameters
• Improved depression scores
•High scores for care satisfaction
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Outcomes
•A strong technical infrastructure enables
excellent patient/care manager ratios
1/2000 – 1/2500
• Improved patient well-being, including less
depression
•A correlation with financial benefits was not measured
– CMS data was not available
– Aggressive timelines didn’t permit ongoing study after the grant ended
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Clinical Challenges
• Effectively motivating patients to care for
themselves
• The sometimes overwhelming burden of
socioeconomic factors
•Mental health and addiction issues among
patients
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Lessons Learned
1. Program development needs to be sequential, not
concurrent
– IT platform needs to start first because it has the longest
lead time
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Lessons Learned
1. Program development needs to be sequential, not
concurrent
– IT platform needs start first because it has the longest lead
time
2. Excellent care management is hard to do
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Lessons Learned
1. Program development needs to be sequential, not concurrent
– IT platform needs start first because it has the longest lead time
2. Excellent care management is hard to do
3. Patients don’t yet understand the role of aggressive
care management
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Lessons Learned
1. Program development needs to be sequential, not concurrent
– IT platform needs start first because it has the longest lead time
2. Excellent care management is hard to do
3. Patients don’t yet understand the role of aggressive care
management
4. Must understand the right metrics to measure
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Lessons Learned
1. Program development needs to be sequential, not concurrent
– IT platform needs start first because it has the longest lead time
2. Excellent care management is hard to do
3. Patients don’t yet understand the role of aggressive care
management
4. Must understand the right metrics to measure
5. Address socioeconomic and psychosocial factors
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Lessons Learned
1. Program development needs to be sequential, not concurrent
– IT platform needs start first because it has the longest lead time
2. Excellent care management is hard to do
3. Patients don’t yet understand the role of aggressive care
management
4. Must understand the right metrics to measure
5. Address socioeconomic and psychosocial factors
6. Predictive analytics may be superfluous
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Future Plans
• Expand clinical programming
• Assume various risk-based contracts
– MSSP
– PMPM relationships
– Value-based risk
• Clinical Goals:
– Majority of care will be risk-based
– Develop effective care delivery models
– Provider empowerment
– Patient/member satisfaction
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Savings
Patient Engagement &
Population Management
Treatment/Clinical
Electronic Secure Data
http://www.himss.org/ValueSuite
Satisfaction S
T
E
P
S
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$1.75 Million/year
Prioritization of care based
on levels of need
Incorporating claims data from within
and outside of Christiana Care
Physical limitation, angina frequency
and quality of life gains
Excellent participant satisfaction scores
http://www.himss.org/ValueSuite
S
T
E
P
S
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Terri Steinberg, MD, MBA
Chief Health Information Officer & VP,
Population Health Informatics
Christiana Care Health System