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Paul Bleicher, MD, Chief Executive Officer, OptumLabs™
19 January 2015
Linked EHR/Claims Data in the High Cost/High Needs Population
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OptumLabs™: Investigate. Collaborate. Innovate.
We accelerate research, innovation and translation by giving our partners access to the largest U.S. linked EHR/claims patient database, thought leadership and the power of multi-partner collaboration
Data
EHR/claims linked data asset and more
Expertise
Data analytics, health economics
Convening
Bringing together partners to work together and share results and insights
Thought leadership
Through OptumLabs, its partners, and collaborators
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Our data today: Overview
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20+ years of data captured in over 1500 fields
• Claims: Facility, Physician, Lab and Pharmacy
Claims, Lab results, Mortality information
• Enrollment: Member coverage periods,
Continuous enrollment, Benefit type
• Sociodemographics: Race, income,
education, net worth
• Health Risk Assessment
Claims (linked):
>130 MM people
EHR (linked): >48 MM patients
Expanded insights with deeper clinical context
250+ additional data fields
Consumer
profile
Expanded insights with consumer profile data for over 37 million consumers
Consumer & Lifestyle data fields – Household composition,
Occupation, Lifestyle interests, Property Equity, Net Worth
Claims: >33 MM people
• Clinical Orders
• Clinical Results
• Diagnoses
• Encounters
• Medications
• Procedures
• Vital Signs
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What unique leverage does linked EHR data provide beyond claims data in understanding high need / high cost
patients?
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Comorbidity Profile of Hospitalized CHF Patients (N = 107,762)
Annual Hospitalization
Rate
Nearly as many patients
have 5 comorbid
conditions as have 1
Claims: Population Level Information on Complex Comorbities
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14,000 incident CHF patients
Hospitalized vs. non-hospitalized patients
3-fold difference in costs during months 4 – 10 post diagnosis
Claims: Rapid Population-Based Comparisons Using an OptumLabs Data Tool
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Claims: Identification of Similar Patient Clusters Supports Care Personalization
Obesity and most MCCs
CAD
Dementia
COPD
Diabetes
Renal
Disease
Dementia, Stroke,
Paralysis
Arrhythmia w/out DM
COPD, Pneumonia Diabetes
w/out compl
Fewest MCCs
CKD with and w/out
DM
Obesity
Clustering of
patients at
highest risk for
hospitalization
(top 10%)
Clustering
strategy shifts
focus to patient
profile
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EHR Supplies Enhanced and Unique Elements; - Optum EHR Includes NLP Sourced Variables
Care Area Labs Visits Provider Diagnosis Procedure
NLP
Drug
Rationale
NLP
Signs,
Disease, &
Symptoms
NLP
Family
History
NLP
Measures
Enrollment Health
Service
Costs
Prescriptions
Filled
Medication
Admins
Microbiology Immunizations Observations
Prescriptions
Written
Patient
Reported
Meds
Patient
Reported
Measures
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Hypotheses Related to Care Management and Readmission Drivers Emerge
Day 1 Day 7 Day 14 Day 21
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Mining Data Through Natural Language Processing Offers Detail Not Present in Structured Fields
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Predictive Modeling Reduced Hospital Admissions by 60%
Heart Failure Hospitalization
Predictive Model:
Used patient prior health care
utilization and clinical findings
(pO2, lab results, vitals) to
predict risk
Contacting and assessing high
risk patients reduced HF
admissions by 60% from prior
year
Effects reverberated through
care system
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• Clinical findings not available in claims
– Laboratory values
– Radiology Findings
– Clinical measurements (e.g. spirometry, LVEF)
– Vitals, smoking status, race/ethnicity
– Patient reported outcomes (e.g. Pain, MMSE)
– OTC medications (e.g. aspirin use)
• Pre-adjudicated diagnosis and procedure information
• Detail information during inpatient confinements
– Temporal details
– Medication delivery in hospital
• Clinical notes can be mined for detail not available in structure data
– E.g. Fall Risk
Integrated EHR and Claims Data: Create Unique Value In Managing Complex Populations