• MongoDB– Quality Measure Storage

• PostgreSQL– User entered data– Screen Shots

• SaaS– Amazon EC2

• http://www.checkqm.com

January, 2012

Big Data = Complete Data

• The Electronic Medical Record is primarily transactional taking feeds from source systems via an interface engine.

• The Enterprise Data Warehouse is a collection of data from the EMR and various source systems in the enterprise.

• In both cases decisions are made concerning data acquisition.

• Hadoop is capable of ingesting and storing healthcare data in total.

Big Data = Infrastructure

• Low Cost of Entry & Scalable– Open Source– Commodity Hardware• UCI Hadoop Ecosystem

– 8 nodes– 4 terabytes

• Yahoo Hadoop Ecosystem– 60K nodes– 160 petabytes

• Cloud Ready

Big Data = Interoperability

• An Ecosystem that Supports– Hadoop (HDFS)– MongoDB (NoSQL)– Neo 4j (Graph Database)– Relational Data Base– MapReduce– JBoss Drools– Mahout

Limits of Current Ecosystem

• The Electronic Medical Record is not up to the task of handling complex operations such as anomaly detection, machine learning, building complex algorithms or pattern set recognition.

• Enterprise Data Warehouses (EDW) suffer from a latency factor of up to 24 hours. The EDW serves clinicians, operations, quality and research retrospectively as opposed to real time.

SaritorData Information Knowledge Wisdom

• A healthcare information ecosystem built on “Big Data” technologies capable of serving the needs of clinicians, operations, quality and research in real time and in one environment.

• Able to ingest all healthcare generated data both internal and external.

• Platform for advanced analytics such as early detection of sepsis & hospital acquired conditions. Prediction of potential readmissions. Complex algorithm and machine learning platform.

Health Care Data Sources• Legacy Systems• All HL7 Feeds (EMR source systems)• All EMR Initiated Data • Device Data (in one minute intervals)

– Physiological Monitors– Ventilators– Smart Pumps

• Real Time Location System• Hospital Sensors• Genomic Data• Home Monitoring• Social Media

– Healthcare Organization Sentiment Analysis– Patient Engagement

Saritor Initial Functionality

• Integration with EMR to View Legacy Data• 30 Day Readmit Prediction (UCI Centric)• Early Sepsis Detection & Notification• Integration with UCI Clinical Intelligence

Applications• Chronic Disease Scorecards • Home Monitoring Analytics• Social Media Sentiment Analysis

Training Data Set

Test Data Set

DiagnosisPatterns Repository

Input Data Attributes, Rules, Parameters

Hypothesis / Algorithm Model(Core Engine with the Equations / Analysis)

Analyze Output for Model Behavior (Actual versus Desired)

Identify Improvements

Feedback and Refine the Model

Matches Expectation

Release for Testing the Model

Output / Results (Actual)

Input Data Attributes, Rules, Parameters

Hypothesis / Algorithm Model(Core Engine with the Equations/ Analysis)

Analyze Output for Model Behavior (Actual versus Desired)

Identify Improvements

Feedback and Refine the Model

Matches Expectation

Baseline the Pattern

Publish new version to Repository

Output / Results (Actual)

Not Satisfactory Satisfactory Result Not Satisfactory Satisfactory Result

Available Data Set

StatisticalTechniques

StatisticalTechniques

Algorithm Management

Quantified Self

Personal Informatics

mHealth

Saritor

PHR Centric Health

EMR

HIE

http://healthdesignchallenge.com/

http://www.health2con.com/devchallenge/

The difference between a

vision and a hallucination

is that other peoplecan see the vision. Marc Andreessen

Charles Boiceycboicey@uci.edu@N2InformaticsRN