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Because everyone matters. IBM Health and Social Programs Summit, October 2014 Stephen Morgan Senior Vice President and Chief Medical Officer Carilion Clinic Jianying Hu Research Staff Member and Manager of Healthcare Analytics Research IBM Paul Grundy Global Director of Healthcare Transformation IBM
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Innovative Insights for Smarter Care: Care Management and Analytics
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Stephen MorganSenior Vice President and Chief Medical Officer
Carilion Clinic
Innovative Insights for Smarter Care: Care Management and Analytics
Jianying HuResearch Staff Member and Manager of Healthcare Analytics Research
IBM
Paul GrundyGlobal Director of Healthcare Transformation
IBM
How Analytics Can Impact Patient Care
Stephen A. Morgan, MD
SVP and CMIO
Carilion Clinic
The practice of medicine has changed…
“The complexity of modern medicine exceeds the inherent limitations of the unaided human mind.”
David M. Eddy (1990)Kaiser Permanente
CHF In Virginia
The Current Approach
• Identify patients who already have CHF• Risk Stratify • Address gaps in Care • Registry based
• Prevention• Treating the underlying disease(s)
• High blood pressure • Coronary disease • Diabetes• COPD
• Not everyone with these illnesses will develop CHF
The Future- Proactive Care
• Identify patients at risk before they develop symptoms of heart failure • Maximize treatment of underlying conditions• Closer follow up• Delay or prevent the onset of severe heart
failure • Bend the disease curve
CHF Onset Project
• Collaboration ( Carilion, IBM, Epic) • 3 years data / 500,000 records reviewed• NLP used to obtain unstructured data (20M)• 8500 patients at risk
• 3500 identified with NLP• Risk score generated based on clinical ,
social and demographic data • Score available in EMR • Develop treatment protocols to address at
risk patients.
Understanding the Population
• 3 focus areas• At risk for developing CHF
• Proactive
• Those who were identified by the predictive model as being at risk but did not develop CHF
• What did we do different
• Those who developed CHF • What treatments worked best
The Future
• Integrating claims data
• Integrating social data• Social media • Patient entered
• Expand to other disease models
• Improved documentation using NLP
• Prescriptive modeling for treatments
© 2014 IBM Corporation14
Contact:
Jianying Hu, PhD [[email protected]]Principal Research Staff Member and Manager, Healthcare Analytics Research
IBM T.J. Watson Research Center, Yorktown Heights, NYURL: http://www.research.ibm.com/healthcare
Data-Driven Healthcare Analytics for Personalized Healthcare
© 2014 IBM Corporation
China
Almaden
Austin
Tokyo
Zurich
India
Dublin
Melbourne
Brazil
Africa
WW IBM Health Informatics Research
Medical Imaging, Multi-modal
Medical Document Retrieval
Medical Imaging, Multi-modal
Medical Document Retrieval
Analytics ,Visualization,
Wellness
Analytics ,Visualization,
Wellness
Analytics , Medical Imaging,
Translational Medicine
Analytics , Medical Imaging,
Translational Medicine
Mobile Health, Health Systems
Analysis
Mobile Health, Health Systems
AnalysisPopulation HealthPopulation Health
Clinical decision support system,Vulnerable population
Clinical decision support system,Vulnerable population
AnalyticsAnalytics
Analytics, Medical Imaging, Health Systems Research
Analytics, Medical Imaging, Health Systems Research
Haifa Watson
© 2014 IBM Corporation
Data Driven Healthcare Analytics
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Care Delivery Network
Personalized Evidence &InsightsPatient
Translational Medicine
Translational Medicine
Predictive ModelingPredictive Modeling Risk StratificationRisk Stratification
ANALYTICS
Patient SimilarityPatient Similarity
VISUAL ANALYTICS
Practice Management
Practice Management
Real World EvidenceReal World Evidence
Point of Care Decision Support
Point of Care Decision Support
Wellness Management
Care Coordination
Decision Points
ICD
A
Visualization for PatientClusters
Visualization for PatientClusters
Visualization for PatientEvolutions
Visualization for PatientEvolutions
Clinical PathwayMining
Clinical PathwayMining
Claims
EMRs
Demographics
……
Utilization Pattern Analysis
Utilization Pattern Analysis
© 2014 IBM Corporation
Patient Similarity Analytics + CareFlow Visualization:
Visualize Relevant Care Pathways and Associated Outcomes
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Key Features– Uses Patient Similarity Analytics
to find clinically similar patients.– Provides a visualization
overview of the evolution of clinical pathways of the similar patients.
– Identifies most desirable and most problematic pathways to inform decisions
Use Cases– Physician designing a
personalized pathway for a patient
– Care manager validating a prescribed pathway
– Medical director doing quality control
CareFlow Visualization
Historical patient event trails and patient characteristics
Characteristics of current patient
© 2014 IBM Corporation
IBM Research Healthcare Predictive Modeling Pipeline
Key Innovations to Address Challenge in Building Models from EMR Data:
Feature preprocessing to handle incompleteness and sparsityPattern mining to derive higher level phenotype representationsFeature selection algorithms to identify salient signals from high dimensional data Big data platform to support rapid exploration of large model space
Key Innovations to Address Challenge in Building Models from EMR Data:
Feature preprocessing to handle incompleteness and sparsityPattern mining to derive higher level phenotype representationsFeature selection algorithms to identify salient signals from high dimensional data Big data platform to support rapid exploration of large model space
Structured Data
Structured Data
Feature extractionFeature
extractionLongitudinal
Patient Representation
LongitudinalPatient
RepresentationFeature selectionFeature selection
Unstructured Data
Unstructured Data
Prediction Model
Prediction Model
Scoring
Model Development
Feature engineeringFeature engineering
Model ExplorationModel Exploration
PreprocessingPreprocessing
• Diagnoses
• Procedures
• Medication
• Lab results
• ……
• Encounter Notes
© 2014 IBM Corporation
Use Case: CHF Onset Prediction
AUC significantly improves as complementary data driven risk factors are added into existing knowledge based risk factors.
A significant AUC increase occurs when we add first 50 data driven features
AMIA2012
• 4644 case patients, 45,981 control patients
• Over 20k features of different types (diagnoses, demographics, Framingham symptoms, lab results, medication, vital)
• Novel feature selection algorithm enabling integration of knowledge driven and data driven risk factors
• Investigation of different observation windows (30 – 900 days) and prediction windows (1 – 720 days)
• Investigation of multiple classification models (logistic regression, random forest, kNN, cox regression …)
NIH grant on early CHF detection
2013
NIH grant on early CHF detection
2013
What is the likelihood of an individual patient experiencing a HF onset 6 months (or any other period) down the road?
© 2014 IBM Corporation
Use Case: Predictive Model for ADHD Medication Adherence
Client Need Benefits
Proposed Solution POC Study Results
• Identify risk factors for non-adherence to therapy
o Improve adherence to therapyo Improve patient outcomes
Predictive models for non-adherence• Patient stratification for risk of non-adherence
• Identify key risk factors for non-adherence
• Identify interventions to improve adherence
• Claims (IMS) + EMR data
• >20,000 Patients, over 5 years
© 2014 IBM Corporation
Actionable Risk Stratification: Insights Generation for Care Planning
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Universal Feature Model
Universal Feature Model
DiagnosisProceduresPharmacyLabDemographics……
Global Risk Factor
Identification
Global Risk Factor
Identification
Risk Factor Guided
Clustering
Risk Factor Guided
Clustering
Group Risk Factor
Analysis and Care Pathway Mining
Group Risk Factor
Analysis and Care Pathway Mining
Care Template Creation
Care Template Creation
Risk Group Assignment
Model
Risk Group Assignment
Model
Care Plan Personalization
Care Plan Personalization
Patient
Dante Chalker
Dante’s Care Plan
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Stratify patients based on risk factors, identify key risk factors and actions for each risk group, to support care plan template design and matching of patient based on risk profiles
Clusters of patients with different key risk factors
ICDM 2013
© 2014 IBM Corporation
Data Driven Analytics for Smarter Care – The Journey Continues
Predict Risk: Cross-domain comprehensive risk and risk factor analysis
– Risk types: physical health, mental health, quality of life ……
– Data Sources: phonotype, genotype, public health, social intervention, behavioral ……
Understand Drivers: Disease Modeling
– Phenotype representation from complex data
– Disease progression models
Improve Practice: Care Pathway Analytics
– Platforms for identifying practice patterns from observed pathways
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Joint disease risk prediction (AMIA 2014)
Multi-linear regression (AMIA 2014)
Learning probabilistic disease progression model (KDD 2014)
Care Pathway Explorer (IUI 2014)
For more info and links to our publications visit: http://www.research.ibm.com/healthcare/
For more info and links to our publications visit: http://www.research.ibm.com/healthcare/
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Moderated discussion
