population health management - angus mccann
TRANSCRIPT
Population Health Management - one person at a time
Angus McCann
IBM Global Healthcare Team
[email protected] @eHealthAngus
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Spot the ‘patient’…
Source: Bipartisan Policy Center,
“F” as in Fat: How Obesity Threatens
America’s Future (TFAH/RWJF, Aug.
2013)
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Focusing on sickest does not bend the cost trend
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One ‘patient’ at a time?
Volume-Based/Episodic Value-Based/Continuous
Current View
30 Patients Per Day
14 have Chronic Conditions
Unknown Health Risks
Visits Too Short for Coaching
New Population View
2500 Patient Population
900 have Chronic Conditions
1100-1250 have Mod-High Health Risk
Care enhanced through IT & data
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Key facets of population health management
• Comprehensive view of ‘health’ – physical, mental, whole person
• Early Intervention, Health Promotion and Prevention
• Wider determinants of health considered – eg Income maximisation,
legal advice, housing, education
• Addressing lifestyle behaviours
• Use of data
• Population stratification / risk prediction
• Care pathways defined and used
• Self-management
• Integration across agencies
Well At RiskAcute
Self-Limiting
Chronic Illness
Complex Care
Comprehensive view of ‘health’ / wider determinants
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Population definition / stratification
1. Diabetes stands out
with a low overall
compliance rate
of 38%
2. Significant percent
of diabetic patients
with A1c rates >9
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Bring people into the system (appropriately)
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Automate
Team based care – integrated across agencies
Patient engagement / self management
Identify variances by
practice to target
improvement
strategies
Identify variance in care by practice
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Identify variance by clinician
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New delivery models require integrated data…
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Medical text analytics
Medications
SymptomsDiseases
Modifiers
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The Data We Thought Would Be Useful … Wasn’t
• 113 candidate predictors from structured and unstructured data sources
• Structured data was less reliable then unstructured data – increased the reliance on unstructured data
• New Unexpected Indicators Emerged … Highly Predictive Model
Predictor Analysis % Encounters
Structured Data
% Encounters
Unstructured
Data
Ejection Fraction
(LVEF)
2% 74%
Smoking Indicator 35%
(65% Accurate)
81%
(95% Accurate)
Living Arrangements <1% 73%
(100% Accurate)
Drug and Alcohol
Abuse
16% 81%
Assisted Living 0% 13%
What really causes heart failure readmissions at Seton
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Data Driven
Every Personhas a Plan
Team Based
Automation to Manage a Population Down to
the Individual
Helping the population be healthy
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BCS Health Scotland Conference
• Strathclyde University
• 11/12 Oct
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Population Health Management - one person at a time
Angus McCann
IBM Global Healthcare Team
[email protected] @eHealthAngus