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MongoDB – Quality Measure Storage PostgreSQL – User entered data – Screen Shots SaaS – Amazon EC2

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Presentation on theme: "MongoDB – Quality Measure Storage PostgreSQL – User entered data – Screen Shots SaaS – Amazon EC2"— Presentation transcript:

1 MongoDB – Quality Measure Storage PostgreSQL – User entered data – Screen Shots SaaS – Amazon EC2

2 January, 2012 TDS (Legacy System) 22 Years Patient Data 1.2M Patients 9M Records Orders Labs Transcribed Results Patient Record HL7 Feed Lab Results Physiological Monitors Ventilators Transcribed Reports Radiology Results Endoscopy Results Orders EMR Generated Data RN Documentatio n Provider Documentatio n External Data Home Monitoring Personal Health Record Social Media *Twitter *Foursquare *Yelp *RSS & Blog

3 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.

4 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

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

6 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.

7 Saritor Data 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.

8 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

9 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

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11 Training Data Set Test Data Set Diagnosis Patterns Repository Input Data Attributes, Rules, Parameters Hypothesis / Algorithm Model (Core Engine with the Equations / Analysis) Hypothesis / Algorithm Model (Core Engine with the Equations / Analysis) Analyze Output for Model Behavior (Actual versus Desired) 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) Hypothesis / Algorithm Model (Core Engine with the Equations/ Analysis) Analyze Output for Model Behavior (Actual versus Desired) 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 Statistical Techniques Statistical Techniques Statistical Techniques Statistical Techniques Algorithm Management

12 Quantified Self Personal Informatics mHealth

13 Saritor PHR Centric Health EMR HIE

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15 The difference between a vision and a hallucination is that other people can see the vision. Marc Andreessen

16 Charles


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