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© 2014 IBM Corporation
Xavier Constant Núñez – Business Analytics Architect
October - 2014
Advanced Analytics for Healthcare
Xavier [email protected]
WHO-FIC Network Annual MeetingOctober 2014
© 2014 IBM Corporation
For Healthcare Institutions, harvesting the data profusion with analytics opens up new sources of value
What patient-centric factors are most predictive of outcomes from a treatment regimen ?
What patient-level interventions could help improve treatment adherence ?
How can you provide clinicians with targeted assistance ?
How can you best meet the information and support needs of patients and caregivers?
What channels of communication work best ?
… and many more
Physician notes and discharge summaries
Patient history and symptoms Pathology reports Tweets, text messages and online
forums Satisfaction surveys Claims and case management data Forms-based data and comments Emails and correspondence Trusted reference journals and
portals Paper based records and documents
* AIIM website, accepted industry percentage
Over 80% of stored health information is unstructured*
Data from a variety of sources is now available for harvesting.
These sources generate huge volumes of data …
… and can help identify significant points of leverage
15 petabytes
Amount of new information created each day - eight times more than the information in all US libraries
ClinicalOutcomes
OperationalOutcomes
Health data growing 35%
per year*
© 2014 IBM Corporation
time spent manually interpreting data would become time spent healing patients.•Aggregate, activate and enrich relevant patient information beyond what is known
•Surface earlier, more accurate insights to drive incipient intervention opportunities
•Adaptive and proactive delivery drives individualized, patient centered care
Confirm what I think or suspect?
Show me something new or unexpected?
How many are being missed?
How do we move faster and anticipate change?
If we could only activate the relevant information to bring insights to the point of care when needed most
Knowledge, guidelines and best practice measures
Adapt care to changing conditions and new information
Indentify intervention opportunities
Longitudinal “data driven” insights
Information should aid us, not lie hidden and dormant
© 2014 IBM Corporation
Carilion Clinics flags patients at risk for developing Congestive Heart Failure (CHF)
© 2014 IBM Corporation
Use of Content Analytics to enrich the Predictive Models used to identify patients at risk for developing CHF
66 © 2014 IBM Corporation#ibmiod
Patient Age: 65Gender: MaleRace: White
Diagnosis
MelanomaStage: 2
Social Marital status: single
Labs AJCC: T2
Risk of metastasis
47%
Content AnalyticsPredictive Analytics
RecommendedAdd’l Treatment
DTIC
Similarity Analytics
Care Manager
100’s or 1000’s of patients
100’s or 1000’s of patients
One Patient
Goals Avoid remission
Activities Avoid UV radiationRegular screeningTransportation assistance
A 65-year old white male has been diagnosed with stage 2 melanoma. He is widowed and lives alone.
AJCC: T2
Raw Information(e.g. EMR and Claims)
10’s of thousands of patients
6
Technical Solution OverviewCapture, Analyze, Activate
77 © 2014 IBM Corporation#ibmiod
Examples of NLP Challenges in Healthcare
• Accurately identify and extract facts from text including negation“55%” = LVEF“Patient does not show signs” = Negative Symptom
• Accurately interpret and assign values to ambiguous statements“around 55%” = LVEF“Shows slightly elevated levels” = if condition A = 10%, if condition B = 20%
• Infer meaning from non-contextual content“Cut back from two packs to one per day” = Smoker
• Cleanse, enhance and normalize raw data“Myocardia infarction” and “heart attack” = equal same thingCorrect misspellings and abbreviations through NLPEnhance or augment by assigning correct RxNorm, SNOMED, ICD-10 or other codes / terminology
• Preserve and structure facts and concepts from contextual content:
7
A 42-year old white male presents for a physical. He recently had a right hemicolectomy invasive grade 2 (of 4) adenocarcinoma
in the ilocecal valve was found and excised. At the same time he had an appendectomy. The appendix showed no diagnostic abnormality.
Patient Age: 42 Gender: Male Race: White
Procedure hemicolectomy diagnosis: invasive adenocarcinoma anatomical site: ileocecal valve grade: 2 (of 4)
Procedure appendectomy diagnosis: normal anatomical site: appendix
Content Analytics
88 © 2014 IBM Corporation#ibmiod
Content Analytics Applied to Improve Patient Outcomes
8
Content Analytics
Language IdentificationLanguage Identification
Spell checkingSpell checking
Lexical analysisLexical analysis
Part of Speech, DisambiguationPart of Speech, Disambiguation
Named Entity RecognitionNamed Entity Recognition
Part of a SentencePart of a Sentence
Semantics (Relationships), DisambiguationSemantics (Relationships), Disambiguation
Synonims, ConceptsSynonims, Concepts
Objectiveness: opinion, doubt, fact, questionObjectiveness: opinion, doubt, fact, question
IdeasIdeas
Indexing
Evolving topics
Sentiment Analysis
Interest analysis
life-events
QA
Deep QA (Watson)
Brand Analytics
Classification
Custom applications
text-analytics
NLPDocument Summarization
© 2014 IBM Corporation
Enrich & Improve Predictive Models with information trapped in unstructured data
• Content Analytics capabilities :• Trend, Pattern, Anomaly, Deviation and
Context Analysis
• Medical Fact, Relationship and Outcome Annotation
• Healthcare Accelerators speed time to value:
• Annotators focused on extracting medical terms
• Approximately 800 pre-built rules developed in IBM Content Analytics Studio
• Included diagnoses, procedures, labs, many drugs
• Transforming unstructured data to CPT, ICD9, and SNOMED concept ID outputs
• Detecting negations
• Detecting family histories
Content Analytics
© 2014 IBM Corporation
Enrich & Improve Predictive Models with information trapped in unstructured data
Content Analytics
© 2014 IBM Corporation
Real case: Readmissions at SetonThe Data We Thought Would Be Useful … Wasn’t
• 113 candidate predictors from structured and unstructured data sources
• Structured data not available, not accurate enough without the unstructured content
• Unexpected Indicators Emerged … Readmission is a Highly Predictive Problem! • 18 population specific predictors surface previously unknown intervention opportunities
11
Unstructured data unlocks hidden insights
Predictor Analysisc % EncountersStructured 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%
Content Analytics
1212 © 2014 IBM Corporation
Output: alerts predictions recommendations categorization visualization …
Training
Analysis
Medical health records / provided services / lab tests
Statistical Prediction Models
2. Analysis – runtime process
1. Learning
A new Patient / change in
disease state / new condition
Periodic Training:Learn from the latest data
Collect real data
Predictive AnalyticsPredictive Analytics
1313 © 2014 IBM Corporation
Representing Patients using Information Obtained from Multiple Sources of Data
Feature Extraction
Feature Extraction
Patient Feature Vector
x1
xN
x2
Patient
Predictive Analytics
1414 © 2014 IBM Corporation14 IBM Confidential 18 de desembre de 2014
Similarity Visualization
Patient Representation
Similarity Identification
Patient Similarity Analytics - Given an index patient, find clinically similar patients
Similarity Analytics
© 2014 IBM Corporation
Congestive Heart Failure (CHF) Onset Prediction (Results achieved in 6 weeks)
© 2014 IBM Corporation
IBM Advanced Care Insights provide analytical capabilities enabling holistic, individualized approaches to Smarter Care
ObjectiveFind clinically similar patients for decision support and Comparative Effectiveness
Patient Similarity
ObjectiveIdentify most effective treatment option for a given patient
Personalized Comparative Effectiveness
Predict Patient Clinical Pathway Patient / Provider Matching
Visualize Population Cohorts
Visualize Disease Pathways
Predictive
Analytics
Multimodal Longitudinal
Patient Data (e.g. Structured +
Unstructured [text, image, genetics, …],
potentially social media)
Chance of
Adverse
Event = 80%
X months
ObjectiveAnalyze patient’s longitudinal records to model the future risk of developing adverse conditions
ObjectivePredict and visualize patient disease progression
ObjectiveVisualize populations through interactive multi-dimensional exploration of inter-cluster and intra-cluster relationships
ObjectiveMatch patients with providers based on similarity analytics and optimal performance characteristics`
Differentiating
Identifying
Challenging Patients
© 2014 IBM Corporation
Thank You