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Utilizing Data Analytics to Improve Patient Care and System Risk Management
© 2016. All contents herein. Reproduction in whole or in part prohibited without the written consent of Forecast Health.
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Focus on answering the right questions
Keeping the End in Mind
And do so using:
1) All available data
2) Modern analytics
3) “Impactability”
4) Care plan guidance in existing workflow
1) Predictive analytics can help improve outcomes
2) Most predictive analytics focused on overall cost/risk not “impactablity”
3) Most predictive analytics still rely on traditional data
4) Big opportunity to:
• Integrate non-traditional social determinants of health data
• Move analytics from predicting risk to help manage risk
• Focus services on impactable; focus innovation on not impactable
• Provide info about person to help guide the care team
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Context
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Predictive Analytics
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What would you do differently if you knew 30 days before admission which members were going to be readmitted?
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What if you knew which high-risk members were impactable, and would benefit from early intervention?
…And which weren’t?
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If you could pinpoint risk & impactability 30 days in advance...
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• Proactive case management? • Provide financial support? • Extend physical therapy?
1) Predict the right thing accurately: “impactable” risk
2) Provide care team with patient/family-specific guidance
3) Integrate into existing workflow
Managing Risk
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1) Predict the right thing accurately: “impactable” risk
2) Provide care team with patient/family-specific guidance
3) Integrate into existing workflow
Managing Risk
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100%
90%
80%
70%
60%
50% Chance (50/50)
Clinicians (doctors, nurses)
Advanced analytics
How Accurate?
Perfection
Typical analytics
Lots of ways to measure, one is called the “c-statistic”
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Targeting Method C-Statistic BOOST Model 57.2%
Clinical Judgment 59.0%
LACE 61.6%
PARR 64.4%
Forecast Health 89.0%
Highest Risk Tier Sensitivity Top 20% 69.5%
Top 40% 86.7%
Top 60% 94.7%
Top 80% 99.9%
+38%
Statistical: How much more accurate? 30-day Readmission
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Statistical: How much more accurate? Predict 30-Day Readmission
Situation: • Baseline readmissions of 15% • Intervene with top 20% highest risk patients
Find 69 people out of 100 people, or 69%, who will readmit
Find 32 people out of 100 people, or 32%, who will readmit
+2.1x
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Statistical: Trade-off? Predict optimal discharge location (SNF or HH)
Increase discharges from SNF to HH 16% (from 61% to 71%) where penalty is that “only” 5% of the 16% will readmit (0.8%)
Situation: • Baseline readmissions of 15% • Direct 10% lowest risk patients from SNF to HH
Situation: • Baseline readmissions of 15% • Intervene with top 20% highest risk patients
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Statistical: How accurate? Predict 30-Day Readmission 30 Days Prior to Surgery
Prior to surgery, find 42 people out of 100, or 42%, who will readmit after surgery
Social Determinants of Health Definition
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World Health Organization, Commission on Social Determinants of Health, 2008. Closing the Gap in a Generation: Health equity through action on the social
determinants of health. Available from: http://www.who.int/social_determinants/en
Conditions in which people are born, grow, work, live, and age, and the wider set of forces and systems shaping the conditions of daily life.
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Social Determinants of Health
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2
4
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0
40
80
120
160
200
Low Middle High
Admits/1000 Avg Clinic Visits
Andersen and Newman. Societal and Individual Determinants of Medical Care Utilization in the United States. Milbank Quarterly. 2005 Dec; 83(4):10.
Low income group has 51% higher admissions (yet relatively same access to clinics)
Examples: 1) Pollen / weather 2) Food deserts 3) Distance to fitness facilities 4) Distance to PCP, ED, Rx
Population Health Drivers 10%
Physical Environment
10% Genes and
Biology
10% Clinical
Care
30% Health
Behaviors
40% Social and Economic Factors
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10% Physical
Environment
10% Genes and
Biology
10% Clinical
Care
30% Health
Behaviors
40% Social and Economic Factors
Examples: 1) Education attainment 2) Caregiver support 3) Cultural beliefs toward health 4) Financial stability
Population Health Drivers
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10% Physical
Environment
10% Genes and
Biology
10% Clinical
Care
30% Health
Behaviors
40% Social and Economic Factors
Examples: 1) Fitness and outdoor activity 2) Vacation type 3) Convenience food 4) Remote device monitors
Population Health Drivers
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1) ED (1st and Revisits) 2) Rx Non-adherence 3) Bundles:
a) 30-Day-Pre-Op Complications b) At Admission c) Post-Op Complications/Readmissions d) Post-Acute Facility Optimization e) LOS
4) Costs (Concurrent/Prospective)
Sample Suite of Models
Expanded Categories of Non-Clinical Risk
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• Affordability • Communications • Transportation • Social Networking • Material Deprivation • Arrest/Incarceration • Pollution/Weather • Caregiver Support • Lifestyle • English Language Proficiency • Cultural Beliefs Toward Illness and Treatment
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1) Predict the right thing accurately: “impactable” risk
2) Provide care team with patient/family-specific guidance
3) Integrate into existing workflow
Managing Risk
Pre
cisi
on
State / Region
County / MSA
ZIP Code
U.S. Census Tract
U.S. Census Block Group
Individual Household
Individual Person
Multiple Levels of SDH Precision
High
Low
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2
3
4
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Pre
cisi
on
State / Region
County / MSA
ZIP Code
U.S. Census Tract
U.S. Census Block Group
Individual Household
Individual Person High
Low
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2
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Multiple Levels of SDH Precision
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Demonstration
Person-Specific Guidance to Reduce Risk
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The patient is seeing many providers and may have issues with care coordination.
The patient has financial difficulty and may not be able to afford prescriptions or follow-up care.
The patient may need home health and assistance to the hospital or pharmacy.
The patient is at high risk for medication non-adherence and may need counseling or follow-up.
The patient needs additional support due to history of non- acute ED, lives near ED and far from PCP.
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10,000+ Person-specific
data points
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1) Predict the right thing accurately: “impactable” risk
2) Provide care team with patient/family-specific guidance
3) Integrate into existing workflow
Managing Risk
Dr. Brian P. Goldstein
EVP and COO of University of North Carolina (UNC) Hospitals
Member of Forecast Health’s Board of Directors
If a patient’s risk arises from cost, then the care team can provide discount meds; if it comes from limited caregiver support, they can order a visiting nurse. Forecast Health provides the person-specific insights that organizations need to improve care and reduce costs.
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EMR
Other
EMR
EMR
Other
Other Other
Other Other
Enterprise Data Warehouse
BI Reports EMR
Existing Data Marts and Universes
Existing People, Software, Processes, and Technology
Data Extracts Dashboards
Source Clinical
Source Claims
Nightly SDH Enrichment (Star / Other)
SDH Data
Other
Chronicles Registry Build (w/ SDH Smart Data Elements)
Epic Scoring Rules (CER)
FH Analytics Engine
Implement Results
HIPAA Compliant
Implementation: Option 1
EMR
Other
EMR
EMR
Other
Other Other
Other Other
Enterprise Data Warehouse
BI Reports EMR
Existing Data Marts and Universes
Existing People, Software, Processes, and Technology
Data Extracts Dashboards
Source Clinical
Source Claims
Real-time FHIR Data Pull
SDH Data
Other
Real-time SDH Enrichment and Scoring
Seamless EMR App Integration
FH Analytics Engine
Implement Results
HIPAA Compliant
Optional
Implementation: Option 2
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Member Roster
Member Utilization
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Characteristics 1) IP, OP, ED ADT 2) Updated 1x to 4x/day 3) From most NC hospitals 4) Platform for other insights 5) Integrate into EMR 6) Supports 2-way communication
Admission, Discharge, Transfer (ADT) SMART App Platform
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Operational Reporting
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What are cost and service distributions?
Where are my patients relative to my facilities and clinics? Where are my competitors? Where are pharmacies?
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Where are my diverse populations? How do my patients compare to my competitors? Am I missing an opportunity?
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Does household alcohol consumption patterns matter to me?
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What do I want to know about my patient cohorts?
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What are the referral patterns? Where is system leakage? What are the largest care coordination/fragmentation opportunities?
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What is member volume going?
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Where are my ACS conditions? How do they look?
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What do my Preference Sensitive Care conditions look like?
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Which of my PCPs already have the preferred referral patterns?
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Gap Group and Person/Roster Reporting
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Where is the largest variation in readmissions?
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Thank you!
© 2016 All contents herein. Reproduction in
whole or in part prohibited without the written consent of Forecast Health
www.ForecastHealth.com
Contact info: Norm Storwick, FSA MAAA Norm.Storwick@ForecastHealth.com (919) 830-9879
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