Talk given at BIO-IT 2018

1. Population Health Informatics
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Frank Wang
Clinical Assistant Professor
Sacred Heart University
wangf3@sacredheart.edu
frankfangwang@gmail.com

2. Population Health Defined
Population health is defined by Population Health Alliance as the:
• Distribution of health outcomes within a population
• Health determinants that influence this distribution
• Policies and inventions that affect the determinants
Copyright © 2018 FFW LLC.BIO-IT Presentation Population Health
Informatics
Genes and Biology
Centers for Disease Control and Prevention. Social determinants of health. http://www.cdc.gov/socialdeterminants/FAQ.html.

3. Scope of Population Health Management
• Clinical case reports and studies on managing major population health
conditions
• Compliance programs
• Health economics
• Outcome assessment
• Provider incentives
• Health care reform
• Resource management
• Return of investment
• Health care quality
• Care coordination
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4. Population Health Informatics Data Sources
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Outcome
• Mortality
• Quality of life
• Disparity
• Race
• Ethnicity
• Gender
• Geography
Determinants
• Health care
• Individual
behavior
• Social
environment
• Physical
environment
• Genetics
Population Health
Data Types
• Clinical (Electronic
Health Records)
• Insurance (Claims)
• Surveys (e.g. ADL)
• Social data (e.g. SES)
• GIS-bound
• Genotyping

5. Population Health Informatics Data Types
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6. 23
Population Healthcare Informatics
Accountable Care and Population Management Platform
Patient Panel
Definition
Targeted
Populations &
Outcomes
Baseline
Expenditures
& Costs
Accountability
Models
Financial
Reconciliation
Population
Health
Management
• Predicting Risks
and Stratify
• Changing Processes and
Operations
• Changing Delivery
Model
• Managing
Population
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7. Predictive Analytics in Population Health Management
Calculate Risk Scores What-if Intervene
Geo-spatial analysis of
high-risk population
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Stratifying Population and Predicting Risks Based on Comorbidity
• Classifying Diseases • Identifying Comorbidity • Counting Patients by Comorbidity

9. Reports
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• Data and visualization enabled care coordination
• Workflow improvement

10. Reports
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• Report shows care
coordination plan
• Report shows a patient’s
point of care plan

11. Reports
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• Report identifies care gap of each individual patient
• Report shows top area for improvement

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• Potentially avoidable readmission cases
• Calculating readmission risks
Operational Dashboards: 30-Day Readmission Risk
• Under ACA, CMS launched Readmissions Reduction Program (HRRP) in
2012
• CMS is to reduce payments to hospitals with excess readmissions.

13. Dashboards
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• Data replaces anecdotal evidence
• Visualizations highlight opportunities and drive performance improvement

14. Physician Scorecard
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• Scorecard identifies physician performance measures

15. Analytics Dashboards
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• Providing analytic dashboards of performance measures
• Utilization at the department level • Utilization at each hospital

16. Analytics Dashboards
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• Analytic dashboard provides quality measures

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• Identify
patient
zip code
• Identifying
comorbidity
Population Health Management Data Visualization
• Identifying
high-risk
population
• Care
Coordination

18. Key Take-aways
• Effective data analytics is required.
• Data visualization is an iterative process – its purpose is to answer questions
about the data
• Real-time reports can prioritize care coordination plan, identify gap of care and
prompt care coordinators to reach out to at-risk populations
• Both operational and analytic dashboards in healthcare provide quality and
financial measures and performance results of an individual and an
organization
• Data visualization can not influence the culture of an organization without
process improvement and change management.
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