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Harnessing the Power of Data Analytics to Transform Care for Vulnerable Populations
Fred Cerise, MD, MPH
President and CEO, Parkland Health and Hospital System
Ruben Amarasingham, MD, MBA, President and CEO, PCCI
December 2014
Parkland Health & Hospital System:
– 1.3 million patient visits a year.
– 770 staffed adult inpatient beds and 65 staffed neonatal beds.
– First Level I Trauma Center in North Texas.
– A regional burn unit, second largest civilian burn unit in the nation.
– A network of community-oriented primary care health centers.
– The Dallas County Jail health system.
– Primary teaching hospital for UT Southwestern Medical School.
The work comes in many forms. Sometimes it is life changing, life
sustaining and lifesaving. And sometimes it is little understood, little remembered and little noticed ...
unless it goes undone.
2
Support for Parkland
Parkland has served the residents of Dallas County well and has relied on the
taxpayers to continue to invest in its growth and development.
The last major expansion in 1979 was funded by an $80 million bond issue.
In November 2008, Dallas County voters 82 percent in favor of a $747 million bond
issue for construction of a new
Parkland.
The New Parkland
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PCCI Organizational Background
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A 501c(3) non-profit research and development corporation specializing in the development of clinical prediction and surveillance
software for U.S. hospitals and health systems
www.pccipieces.org
Mission
To Help Save A Life.
Why was PCCI created?
• Safety Net Systems have a unique view of the world.
• A vision of the power and promise of a large EMR data repository for use in safety net settings.
• Early indications in 2009 that the software and analytics developed and deployed at PCCI and Parkland could be shared with other hospitals.
• Revenue could fund further research and development for issues that matter to safety net hospitals.
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PCCI Impact at Parkland: Some Highlights
• Pieces™ prediction software has been involved >200,000 patient and resource allocation decisions since 2009
• Sustained reduction in heart failure readmissions since Pieces live in 2009
• All Cause readmission reduction since go-live this year
• $3.2M penalty and 1,421 readmissions avoided
• 100% increase in sepsis bundle compliance
• Early results in sepsis mortality – relative reduction of 17%
• Received $19 M in 1115 revenue capture from PCCI services
• 1 FTE saved due to PCCI infection prevention mobile apps
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PCCI and Parkland Scientific Funding
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> $32M in Funding for Predictive Analytics
PCCI’s Technologies Are Moving from Parkland into Hospital and Community Settings Across the Country
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Dallas-Fort Worth, TX
San Francisco, CA
San Antonio, TX
Parkland and PCCI: Shared Goals
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1. Greater exploration on how we can impact population health with more robust real-time predictive systems
2. Developing novel shared savings programs between Parkland and Community Based Organizations in Dallas
3. Deploying what we have learned to safety net systems nationally
10/24/2014 Proprietary and Confidential, © 2014 PCCI 10
Dr. Amarasingham
www.pccipieces.org
(Video)
PCCI Vision
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To Deploy Predictive and Surveillance Solutions Around the World that Make Healthcare Safer, Simpler, and
Less Stressful.
What We Do in Medicine: Prediction
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1. What does this patient have?
2. What will this patient develop?
3. What will be the effect of a given therapy?
Doubling Time of Medical Knowledge
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30
90
45
1900 1920 1940 1960 1980 2000 2020 2040 2060 2080 2100
1
15
60
75
105
120
135
150
Year
Do
ub
lin
g T
ime
of
Me
dic
al K
no
wle
dg
e
1900: 150 years
Doubling Time of Medical Knowledge
14
30
90
45
1900 1920 1940 1960 1980 2000 2020 2040 2060 2080 2100
1
15
60
75
105
120
135
150
Year
Do
ub
lin
g T
ime
of
Me
dic
al K
no
wle
dg
e
We are here: 1 year
Doubling Time of Medical Knowledge
15
30
90
45
1900 1920 1940 1960 1980 2000 2020 2040 2060 2080 2100
1
15
60
75
105
120
135
150
Year
Do
ub
lin
g T
ime
of
Me
dic
al K
no
wle
dg
e
2020: 2.2 months
• Staggering increase in total medical knowledge • Increasing volume and rapidity of decision-making • Fragmentation and specialization of care • Increasing capacity for error • Scarce resources
What is Electronic Clinical Predictive Modeling and its Purpose?
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Using electronic data to predict future clinical events so that one can:
1. Discriminate between high and low risk patients 2. Prevent adverse events 3. Allocate scarce clinical resources under real-time
demands 4. Suggest actions
Every Adverse Event has a Timeline
30 days 90 days Years Hours
Cardio-Pulmonary Arrest
Sepsis
Asthma Complications
Short-Term Diabetic Complications
Preventable
Admissions Triad: diabetes,
hypertension, CKD
Readmissions
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Admission Discharge 30 Days 90 Days 24 hours
Every Adverse Event has a Timeline
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Every Adverse Event has a Timeline
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2 1 3
5 5
4
ID Risk List Orders
Inpatient Intervention Outpatient Intervention
Admission Discharge 30 Days 90 Days 24 hours
7 days
Pieces
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Evaluation &
Improvement
EMR
Identification of HF patients in Real-Time Using Natural Language Processing and Data Mining
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Natural Language Processing
“68 yo WF presents with acute on chronic non ischemic
systolic and diastolic chf, severely depressed ef and grade ii diastolic dysfunction.”
Disease/ Symptom Time Attribute
Acute Heart Failure current and primary
• Systolic, significant
depression in ejection
fraction;
• Diastolic dysfunction,
grade 2
• Non-ischemic
Chronic Heart Failure historic
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System calculates risk for readmission
22
8.77
14.27
17.94
26.93
51.65
45.68
26.0
19.98
16.08
12.22
0
10
20
30
40
50
60
70
30
-Da
y R
ea
dm
issio
n (
%)
Very Low Low Intermediate High Very High
Predicted Readmission Risk Category
Derivation Samples
Validation Samples
Identifying High-Risk Patients in Real-Time
*
Amarasingham et al, Medical Care, 2010
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Pieces provides list of targeted high risk patients
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Activation of Clinical Pathways in the EMR
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Pieces tracks interventions in the EMR
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Pieces monitors outcomes
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Real-Time Failure Analysis Examples
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Hospital Factors PIECES™
Performance Patient Selection Follow-Through
Intervention
Effectiveness
CHF Volumes Up PIECES™ Down High Risk Patient
Missed
Non-CHF Patient
Enrolled
Inpatient Intervention
Not Ordered /
Completed
Early Discharge Pattern
Noticed CHF Patient Missed
Patient Incorrectly
Scored CHF Patient Excluded
Phone Call Not Placed /
Completed within TF
Clarity Down High Risk CHF Patient
Missed
Missing Data Skewing
Risk Calculation
Low Risk Patient
Enrolled
Outpatient Visit Not
Scheduled / Completed
within TF
Clarity Run-Time Slow Incorrect CHF
Evaluation
Daily Census of High
Risk Patients
Inconsistent
Excluded Patient
Enrolled
Appointments Not
Prioritized by Risk
Improper Disease
Threshold
Modeling of Risk
Distribution Incorrect Effect of the Weekend
Quality of Outpatient
Visit Diminished
Model Feeds Broken Model Feeds Broken Screening Protocol
Adherence CHF Clinic Overrun
Applies not only for readmissions, but for all of Pieces e-models.
Real-Time Failure Analysis Examples
29
Hospital Factors PIECES™
Performance Patient Selection Follow-Through
Intervention
Effectiveness
CHF Volumes Up PIECES™ Down High Risk Patient
Missed
Non-CHF Patient
Enrolled
Inpatient Intervention
Not Ordered /
Completed
Early Discharge Pattern
Noticed CHF Patient Missed
Patient Incorrectly
Scored CHF Patient Excluded
Phone Call Not Placed /
Completed within TF
Clarity Down High Risk CHF Patient
Missed
Missing Data Skewing
Risk Calculation
Low Risk Patient
Enrolled
Outpatient Visit Not
Scheduled / Completed
within TF
Clarity Run-Time Slow Incorrect CHF
Evaluation
Daily Census of High
Risk Patients
Inconsistent
Excluded Patient
Enrolled
Appointments Not
Prioritized by Risk
Improper Disease
Threshold
Modeling of Risk
Distribution Incorrect Effect of the Weekend
Quality of Outpatient
Visit Diminished
Model Feeds Broken Model Feeds Broken Screening Protocol
Adherence CHF Clinic Overrun
Applies not only for readmissions, but for all of Pieces e-models.
Hospital Factors PIECES™
Performance Patient Selection Follow-Through
Intervention
Effectiveness
CHF Volumes Up PIECES™ Down High Risk Patient
Missed
Non-CHF Patient
Enrolled
Inpatient Intervention
Not Ordered /
Completed
Early Discharge
Pattern Noticed CHF Patient Missed
Patient Incorrectly
Scored CHF Patient Excluded
Phone Call Not Placed /
Completed within TF
Clarity Down High Risk CHF Patient
Missed
Missing Data Skewing
Risk Calculation
Low Risk Patient
Enrolled
Outpatient Visit Not
Scheduled /
Completed within TF
Clarity Run-Time Slow Incorrect CHF
Evaluation
Daily Census of High
Risk Patients
Inconsistent
Excluded Patient
Enrolled
Appointments Not
Prioritized by Risk
Improper Disease
Threshold
Modeling of Risk
Distribution Incorrect Effect of the Weekend
Quality of Outpatient
Visit Diminished
Model Feeds Broken Model Feeds Broken Screening Protocol
Adherence CHF Clinic Overrun
• Concentrated care
management efforts
on ¼ of the patients
• 26% relative reduction
in odds of readmission
• Absolute reduction of 5
readmissions per 100
index admissions
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Amarasingham et al, BMJ, 2013
A Different Hospital: Readmission Performance
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Every Adverse Event has a Timeline
30 days 90 days Years Hours
Cardio-Pulmonary Arrest
Sepsis
Asthma Complications
Short-Term Diabetic Complications
Preventable
Admissions Triad: diabetes,
hypertension, CKD
Readmissions
32
Sepsis: Bundle Compliance and Mortality Results
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FY 13 (Pre)
Length of Stay:
Patients 1,445 263 - -
Mean LOS 10.4 days 8.1 days -2.2 days -21.5%
Median LOS 6.6 days 5.8 days -0.8 days -12.6%
FY 13 (Pre)
POA Performance:
Patients 1,445 120
Lactate within 3 Hr. 54.6% 64.2%
IV Abx within 3 Hr. 27.1% 50.0%
Bundle Compliance 14.0% 29.2%
FY 13 (Pre)
Mortality:
All 6.9% 5.7%
Post Pieces
Live Relative Improvement
17.4%
2x relative improvement
18.0%
85.0%
Post Pieces
Live
Absolute
Reduction
Relative
Reduction
Post Pieces
Live Relative Improvement
-
Every Adverse Event has a Timeline
30 days 90 days Years Hours
Cardio-Pulmonary Arrest
Sepsis
Asthma Complications
Short-Term Diabetic Complications
Preventable
Admissions Triad: diabetes,
hypertension, CKD
Readmissions
34
The Complexities of Predictive Modeling
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Amarasingham et al, Health Affairs, 2014
Cohen G, Amarasingham R et al, Health Affairs, 2014
The Complexities of Predictive Modeling
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Amarasingham et al, Health Affairs, 2014
1. Interventions for highest risk patients * 2. Considering clinical vs. social risk 3. Explanation vs. Prediction 4. Non-health care data sources * 5. Changing EMR data models 6. Changing clinical interventions 7. Changing populations
Connecting the Community
37
Pieces™: Analytics and Intelligence Layer
• Leverages predictive and
prescriptive analytics on medical and social data to identify at risk individuals
• Enhances population health, preventive care, and disaster response initiatives
• Informs allocation of
healthcare and community resources
38
Partnerships
39
Community driven connections
Novel shared savings: Pilot starting at Parkland and DFW
41
Community-based
organizations
Hospitals
Services
Shared Savings ($)
42
Pieces Iris™
• 70+ scheduled implementations in 2015
• 110,000 expected lives to be touched • Diverse social service organizations
(e.g. homeless shelters, food distribution centers, transportation, counseling, job skills training, financial assistance, clothing, and many more)
Pieces Plexus™
• Pieces Plexus Go Live in Q4 2015
Implementation Status
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Conclusions
• Predictive analytics are a promising way to help improve timeliness, safety and quality in health care.
• Predictive analytics may be particularly useful in resource constrained environments.
• There are many ways to approach predictive analytics at any given institution.
Thank You!
