Population Health Management - one person at a time

Angus McCann

IBM Global Healthcare Team

angus.mccann@uk.ibm.com @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

angus.mccann@uk.ibm.com @eHealthAngus