1 / 16

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

MongoDB Quality Measure Storage PostgreSQL User entered data Screen Shots SaaS Amazon EC2 http:// www.checkqm.com. January, 2012. EMR Generated Data RN Documentation Provider Documentation. External Data Home Monitoring Personal Health Record Social Media *Twitter

usoa
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. MongoDB • Quality Measure Storage • PostgreSQL • User entered data • Screen Shots • SaaS • Amazon EC2 • http://www.checkqm.com

  2. January, 2012 • EMR Generated Data • RN Documentation • Provider Documentation • External Data • Home Monitoring • Personal Health Record • Social Media • *Twitter • *Foursquare • *Yelp • *RSS & Blog • 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

  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

  10. Algorithm Management Output / Results (Actual) Input Data Attributes, Rules, Parameters Input Data Attributes, Rules, Parameters Output / Results (Actual) Available Data Set Training Data Set Test Data Set Hypothesis / Algorithm Model (Core Engine with the Equations / Analysis) Hypothesis / Algorithm Model (Core Engine with the Equations/ Analysis) Statistical Techniques Statistical Techniques Diagnosis Patterns Repository Analyze Output for Model Behavior (Actual versus Desired) Analyze Output for Model Behavior (Actual versus Desired) Not Satisfactory Not Satisfactory Satisfactory Result Satisfactory Result Identify Improvements Matches Expectation Identify Improvements Matches Expectation Feedback and Refine the Model Feedback and Refine the Model Baseline the Pattern Release for Testing the Model Publish new version to Repository

  11. Quantified Self Personal Informatics mHealth

  12. PHR Centric Health http://healthdesignchallenge.com/ HIE Saritor EMR

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

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

  15. Charles Boicey cboicey@uci.edu @N2InformaticsRN

More Related