Big Data :: Big Demand

Transcription

1 Big Data :: Big Demand

2 Introductions John Martin Director Information Management Children s Hospital of Philadelphia martinjohn@ .chop.edu William Nieczpiel Analytics Development Manager Children s Hospital of Philadelphia nieczpielw@ .chop.edu

3 About The Children s Hospital of Philadelphia 500 pediatric inpatient beds ~29,000 inpatient admissions in FY2014 ~ 1,193,000 Outpatient, ED, Day Surgery visits ~ 50 CHOP Care Network Locations Best in the United States US News and World Report prestigious honor roll Currently building over 8 new buildings totaling over 1,200,000 Sq Ft (est) Patient Care, Research and Education *Information from CHOP 2014 Annual Report

4 CHOP s Analytics Vision Empower decision makers with accessible, timely, and actionable information to accelerate innovation, engage our customers, and enable the highest quality health care.

5 Big Data Why does CHOP need it? 10 Load Processing (Hr) Storage (TB) H/W R1 R Oct-14 Nov-14 Dec-14 Jan-15 Feb-15 Mar-15 Time Daily Processing Improvements Storage Enable the storage, discovery, and high speed processing of very large datasets Advanced Analytics Platform Introduction of an Advanced Analytics Platform

6 Vendor/RFP Process Vendor Focus - Gartner Leaders for data warehousing Process - Created and distributed RFP for response - Reviewed responses and invitations sent for vendor demos (limited to top 3) - Vendors rated based on demo - Top rated vendor invited to provide on-site proof of concept Criteria - Performance - Integration - Scalability - Managed Services - Advanced Analytics Capabilities Source: Gartner (March 2014)

7 General Concept CHOP Data Warehouse CDW

8 Logical Data Warehouse Enable Decisions Right time Knowledge Based CDW Goal for Data Store Everything Process by Use Case Separate Noise Structured Un-Structured Data Lake Patient Encounter Billing Orders General Ledger Etc Medical Device Parametric Alarm Waveform Notes/NLP Claims EMR Access Logs Archival

9 Medical Device Analytics Platform Retrospectiv e Analysis: Another piece of the patient information puzzle, providing more insight into a patients medical events Learning: Review of medical device information post-event with clinical team Streaming Analytics (Preventative ): Cross medical device integration for alerting provider to potential event before occurrence What other opportunities do you see?

10 Medical Device Analytics Platform Waveform & Discrete/Parametric Data, EHR Data Processing Output Technology Clinical Data Science Technology Clinical Real Time Data Processing Multiple Medical Devices Capture, Store, Process & Mine Clinical Annotations (Enhance) Smart Alerts Persistent Storage for Research & Algorithm Development Technology

11 Lessons Learned Delivery and configuration of physical hardware Conversion efforts Use Cases Testing and Validation

12 Big Data Value Add Performance: Data loading 2X to 10X faster Data querying 2X to 2000X faster Storage (Data Lakes): Over 900 TB available Advanced Analytics Database 96TB* Hadoop Cluster 898 TB** **compressed

13 Customer Feedback Quality Improvement: "So fast!!!" "No more thumb twiddling, waiting for jobs to finish" "More focused development time!" "Like a rocket ship, compared to before" "With faster refresh times, information is more readily available" Research (CAG): In just a handful of days, access to the CDW has already produced tangible benefits in two ongoing research projects

14 Customer Feedback Finance: After the CDW upgrade, we have noticed improved performance on the refresh of our QlikView applications. Most notably, the Financial Daily Dashboard has been consistently hitting our SLA target since the upgrade. The SLA was missed at a more frequent basis before the upgrade. This improved timing has enabled financial analysis to occur earlier during the day.

15 Takeaways Integrated Data Warehouse Store All Data Enable Users

16 Questions?