(C) 2010 Copyright - Loyola University Chicago Stritch School of Medicine, All Rights Reserved© Copyright 2013 – Loyola University Chicago Stritch School.

Slides:



Advertisements
Similar presentations
Wincite Knowledge Warehousing and Networking Sophisticated Simplicity.
Advertisements

MongoDB PostgreSQL SaaS Quality Measure Storage
Florida Keys National Marine Sanctuary Water Quality Protection Program Data Integration System Daniel Kiermaier Fish and Wildlife Research Institute.
INTEGRATING BIG DATA TECHNOLOGY INTO LEGACY SYSTEMS Robert Cooley, Ph.D.CodeFreeze 1/16/2014.
Big Data and Predictive Analytics in Health Care Presented by: Mehadi Sayed President and CEO, Clinisys EMR Inc.
Faculty of Computer Science © 2006 CMPUT 605February 11, 2008 A Data Warehouse Architecture for Clinical Data Warehousing Tony R. Sahama and Peter R. Croll.
 Need for a new processing platform (BigData)  Origin of Hadoop  What is Hadoop & what it is not ?  Hadoop architecture  Hadoop components (Common/HDFS/MapReduce)
Databases Chapter Distinguish between the physical and logical view of data Describe how data is organized: characters, fields, records, tables,
The Role of Information Technology For A Private Medical Practice Noel Chua Rosalinda Raymundo.
Copyright © 2006 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill Technology Education Copyright © 2006 by The McGraw-Hill Companies,
(C) 2010 Copyright - Loyola University Chicago Stritch School of Medicine, All Rights Reserved© Copyright 2015– Loyola University Chicago Stritch School.
Feeds Computer Applications to Medicine NSF REU at University of Virginia July 27, 2006 Paul Lee.
(C) 2010 Copyright - Loyola University Chicago Stritch School of Medicine, All Rights Reserved© Copyright 2014 – Loyola University Chicago Stritch School.
Ch 4. The Evolution of Analytic Scalability
Data Mining. 2 Models Created by Data Mining Linear Equations Rules Clusters Graphs Tree Structures Recurrent Patterns.
SQL Server to MySQL Database Migration SQLWays - Migration Software Presentation March 2009 Copyright (c) Ispirer Systems Ltd.
Database Systems – Data Warehousing
Facebook (stylized facebook) is a Social Networking System and website launched in February 2004, operated and privately owned by Facebook, Inc. As.
Database Design - Lecture 1
Big Data. What is Big Data? Big Data Analytics: 11 Case Histories and Success Stories
Evolution of Program Review at UCF Using SAS Business Intelligence Tools to move Towards Optimization Patricia Ramsey & Carlos Piemonti 2013 FAIR Conference.
Team Science – key capabilities “Basic” discovery Clinical applications and samples Population impact “omics” – genomics, proteomics Novel imaging technology.
#HASummit14 Session #30 Breaking Down Silos: Resolving Academic, Medical, and Research Interests Once and for All Presenter Pre-Session Poll Question What.
1 INTRODUCTION TO DATABASE MANAGEMENT SYSTEM L E C T U R E
Florida Keys National Marine Sanctuary Water Quality Protection Program Data Integration System Daniel Kiermaier Fish and Wildlife Research Institute.
Ch 5. The Evolution of Analytic Processes
Presented by CH.Anusha.  Apache Hadoop framework  HDFS and MapReduce  Hadoop distributed file system  JobTracker and TaskTracker  Apache Hadoop NextGen.
Hadoop/MapReduce Computing Paradigm 1 Shirish Agale.
1 Adapted from Pearson Prentice Hall Adapted form James A. Senn’s Information Technology, 3 rd Edition Chapter 7 Enterprise Databases and Data Warehouses.
Anticipated FY2016 Appropriations Agency$ Million NIH200 Cancer70 Cohort130 FDA10 Office of the Natl Coord. for Health IT (ONC) 5 TOTAL215 Mission: To.
Chapter 2 Standards for Electronic Health Records McGraw-Hill/Irwin Copyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved.
“Reaching across Arizona to provide comprehensive quality health care for those in need” ICD10  AHCCCS ICD10 Project Milestones Completed Requirements.
MICROSOFT AZURE ISV PROFILE: D-SCOPE SYSTEMS D-Scope Systems is an enterprise-level medical media product and integration specialist company. It provides.
IS 325 Notes for Wednesday August 28, Data is the Core of the Enterprise.
Big Data Analytics Large-Scale Data Management Big Data Analytics Data Science and Analytics How to manage very large amounts of data and extract value.
1 Categories of data Operational and very short-term decision making data Current, short-term decision making, related to financial transactions, detailed.
Component 6 - Health Management Information Systems
1 Technology in Action Chapter 11 Behind the Scenes: Databases and Information Systems Copyright © 2010 Pearson Education, Inc. Publishing as Prentice.
Chapter 3 Databases and Data Warehouses: Building Business Intelligence Copyright © 2010 by the McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin.
Clinical Research Informatics at the University of Michigan Daniel Clauw M.D. Professor of Medicine, Division of Rheumatology Assistant Dean for Clinical.
Foundations of Business Intelligence: Databases and Information Management.
CS525: Big Data Analytics MapReduce Computing Paradigm & Apache Hadoop Open Source Fall 2013 Elke A. Rundensteiner 1.
National Archives and Records Administration Status of the ERA Project RACO Chicago Meg Phillips August 24, 2010.
Office of Core and Shared Resources Faculty Council Meeting October 9, 2012.
Memphis, TN Thomas Duarte, Executive Director, MSeHA.
Data Mining Basics. “Copyright and Terms of Service Copyright © Texas Education Agency. The materials found on this website are copyrighted © and trademarked.
SAGE Nick Beard Vice President, IDX Systems Corp..
WebScan: Implementing QueryServer 2.0 Karl Geiger, Amgen Inc. BRS NA UG August 1999.
The Need for Data Analysis 2 Managers track daily transactions to evaluate how the business is performing Strategies should be developed to meet organizational.
Research Tools Brought to you by the Clinical and Translational Science Institute Presented by: Terri Shkuda Systems Analyst Research Informatics The Penn.
Informatics Tools and Services Biomedical Informatics Core Tim Aro.
Copyright © 2016 Pearson Education, Inc. Modern Database Management 12 th Edition Jeff Hoffer, Ramesh Venkataraman, Heikki Topi CHAPTER 11: BIG DATA AND.
INTRODUCTION TO HADOOP. OUTLINE  What is Hadoop  The core of Hadoop  Structure of Hadoop Distributed File System  Structure of MapReduce Framework.
REDCap - Research Electronic Data Capture Mike Tran Patrick Shi Tim Aro.
VIEWS b.ppt-1 Managing Intelligent Decision Support Networks in Biosurveillance PHIN 2008, Session G1, August 27, 2008 Mohammad Hashemian, MS, Zaruhi.
Big Data and Patient- Reported Outcomes: Making Sense of the Noise Mike Van Snellenberg, CTO and co-founder, Wellpepper Kristin Helps, RN, Director of.
Group members: Phạm Hoàng Long Nguyễn Huy Hùng Lê Minh Hiếu Phan Thị Thanh Thảo Nguyễn Đức Trí 1 BIG DATA & NoSQL Topic 1:
CPSC8985 FA 2015 Team C3 DATA MIGRATION FROM RDBMS TO HADOOP By Naga Sruthi Tiyyagura Monika RallabandiRadhakrishna Nalluri.
OnCore Current Status and Implementation Project Plan
Public Health February 2017
MUSC i2b2 Jean Craig, Biomedical Informatics
Image taken from: slideshare
Research and Reporting
Biomedical Informatics Core technology, data, services
مدیریت داده ها و اطلاعات آزمایشگاه پزشکی
McGraw-Hill Technology Education
Big DATA.
A Self-Service Patient Cohort Discovery Tool for Research
Copyright © JanBask Training. All rights reserved Get Started with Hadoop Hive HiveQL Languages.
Presentation transcript:

(C) 2010 Copyright - Loyola University Chicago Stritch School of Medicine, All Rights Reserved© Copyright 2013 – Loyola University Chicago Stritch School of Medicine – All Rights Reserved Loyola University Chicago Health Sciences Division Stritch School of Medicine (SSOM) Loyola University Chicago Health Sciences Division Stritch School of Medicine (SSOM) The Clinical Research Database (CRDB) January 8, 2014

(C) 2010 Copyright - Loyola University Chicago Stritch School of Medicine, All Rights Reserved© Copyright 2013 – Loyola University Chicago Stritch School of Medicine – All Rights Reserved Ron Price Associate Dean, Office of Information Systems Loyola University Chicago Stritch School of Medicine Maywood, Illinois & Associate Vice President, Informatics and Systems Development Loyola University Chicago Health Science Division Speaker:

(C) 2010 Copyright - Loyola University Chicago Stritch School of Medicine, All Rights Reserved© Copyright 2013 – Loyola University Chicago Stritch School of Medicine – All Rights Reserved What is the Clinical Research Database (CRDB)? Large-scale, de-identified clinical data warehouse structured to support a wide range of clinical analytics Operates on advanced Hadoop technology CRDB data are accessible via a web-based front-end for casual users (e.g., faculty, housestaff and students) and via a wide range of tools for advanced users (e.g., analysts, bioinformatics staff, etc.) Initial target data loads for the CRDB are from Epic (1/1/2007-9/30/2013)

(C) 2010 Copyright - Loyola University Chicago Stritch School of Medicine, All Rights Reserved© Copyright 2013 – Loyola University Chicago Stritch School of Medicine – All Rights Reserved Why use Hadoop? Developed by Yahoo in mid-2000’s and is extensively utilized by “big-data” internet companies (and the NSA) to process large amounts (petabytes) of structured and unstructured data. Hadoop is a data management/processing framework that distributes data storage and processing over clusters of inexpensive computers Hadoop’s strengths are its ability to scale and to efficiently handle unstructured data (e.g., text reports, images, BLOBs, etc.) SSOM’s Hadoop environment – –Development and Production environments – –Production environment 12-node cluster (2 namenodes, MySQL srv, and 9 datanodes) 178TBs of storage (current core Epic EMR is 4TBs)

(C) 2010 Copyright - Loyola University Chicago Stritch School of Medicine, All Rights Reserved© Copyright 2013 – Loyola University Chicago Stritch School of Medicine – All Rights Reserved Why use Hadoop? Hadoop’s strengths are its ability to scale and to efficiently handle unstructured data (e.g., text reports, images, BLOBs, etc.) “Of the 1.2 billion clinical documents produced in the United States each year, approximately 60 percent contain valuable information trapped in unstructured documents that are unavailable for clinical use, quality measurement and data mining.”* Some estimates put this number closer to 80% * Health Management Technology – June 2012

(C) 2010 Copyright - Loyola University Chicago Stritch School of Medicine, All Rights Reserved© Copyright 2013 – Loyola University Chicago Stritch School of Medicine – All Rights Reserved Why not just use Epic? Epic is LUMC’s EMR however most data originates and are stored their native (e.g., granular or structured) formats in local ancillary systems (e.g., Clinical Labs, RIS/PACS, EPS, etc.) Epic is optimized for healthcare operations and not for research or population studies Activity related to large-scale analytics impacts system performance The “10,000 table” issue (actually 11,964! tables) Systems supporting research and population studies need – –Flexibility to handle “foreign” (e.g., external, multi-center) data – –Flexibility to handle unstructured data – –Need ability to scale to “big data” levels

(C) 2010 Copyright - Loyola University Chicago Stritch School of Medicine, All Rights Reserved© Copyright 2013 – Loyola University Chicago Stritch School of Medicine – All Rights Reserved CRDB Version 1.0 (July 2013) Current data – –De-identified with keys held in Epic Clarity data warehouse – –Data source of Epic Clarity (updated nightly) – –Data period of 1/1/2007 through 09/30/2013 – –Updated quarterly (next update mid-March 2014) – –Data tables Demographics Encounters (Inpatient, Outpatient, ED, Obs and home health) Procedures and clinical lab values Flowsheet measures (vitals, physical findings, etc.) Medications Payor information at encounter level CRDB application is widely available on the portal

(C) 2010 Copyright - Loyola University Chicago Stritch School of Medicine, All Rights Reserved© Copyright 2013 – Loyola University Chicago Stritch School of Medicine – All Rights Reserved Demonstration of the CRDB CRDB

(C) 2010 Copyright - Loyola University Chicago Stritch School of Medicine, All Rights Reserved© Copyright 2013 – Loyola University Chicago Stritch School of Medicine – All Rights Reserved CRDB Version Future Website development activities – –Request for expedited IRB – –Refinement of “groupings” for ICD9s, CPTs and providers Capture of additional data (Current calendar year) – –Microbiology results and other report text blobs End-user Query Tool – Additional query parameters and analysis modules – –Enhanced logic functions (January 2014) – –CPTs (March 2014) – –Labs (June 2014) – –Flowsheet measures (August 2014) – –Units (October 2014)

(C) 2010 Copyright - Loyola University Chicago Stritch School of Medicine, All Rights Reserved© Copyright 2013 – Loyola University Chicago Stritch School of Medicine – All Rights Reserved Current Usage (July 2013 – Dec 2013) Unique CRDB Users – 213 Query Tool CRDB Cohort identifications – 302 CRDB Data Extracts (since August) – –5 large clinical extracts for a recent PCORI grant – –Large data extract for Chicago Health Atlas project – –2 QI projects – –6 Medical Student/Resident clinical research projects

(C) 2010 Copyright - Loyola University Chicago Stritch School of Medicine, All Rights Reserved© Copyright 2013 – Loyola University Chicago Stritch School of Medicine – All Rights Reserved Questions and Answers