MANAGING CLINICAL BIG DATA

Transcription

1 doing the impossible with Diver MANAGING CLINICAL BIG DATA 4 TIPS TO DEPLOY ACTIONABLE CONTENT TO YOUR USERS Aaron McMaster Sr. Data Science Professional Munson Healthcare

2 ABOUT MUNSON HEALTHCARE 8 Hospitals located in Northern Michigan 871 Licensed beds 7,000 employees 160 Employed Physicians Cerner EMR McKesson Star billing/rev cycle

3 ABOUT ME Been with Munson Healthcare for 12 years Functions as a conduit between IT and clinicians Drives the adaption of analytics and support of process improvement efforts Responsible for guiding multidisciplinary teams on the development of clinical intelligence applications to support evidence based decisions As a non-clinician having the ability to increase the quality of care to our patients incredibly engaging and rewarding

4 AGENDA Our goals & challenges Tip 1. Structure for Success Tip 2. Efficient Extraction Tip 3. Big Data Processing Tip 4. Speed to Users What we learned

5 OUR CBI GOAL Unlock data within the EMR to facilitate getting the right information for decision making, to the right people, at the right time. Workflow decision: EMR At the bedside Not at the bedside DI

6 THE CHALLENGE Lots of data (TB s) Highly normalized Complex schema Multiple & inconsistent levels of parent/child relationships Inconsistent usage, integration to workflows Data integrity challenges Many ambiguous customer requests Challenges in request prioritization

7 Foundation behind our warehousing strategy TIP 1: STRUCTURE FOR SUCCESS

8 FOUNDATION FOR RAPID DEVELOPMENT Focus on the customers request Get the right people involved, cannot be driven by IT priorities Balance between getting value now, and making data available for the future Drive towards rapid development lifecycles of CBI projects projects

9 WAREHOUSING THEORY Traditional Approach Speed-to-Value Approach Hybrid Model: Focus on specific customer requirement Acquire most accessible data when accessing a source system Cleanse/validate on a project-by-project basis Focus on reusable content

10 Get the data without impacting the source system TIP 2: EFFICIENT EXTRACTION

11 GET THE DATA! No. 1 priority: do not disrupt the production system Stability is the key to success Create monitoring tools Leverage current technology/systems

12 ORACLE: MATERIALIZED VIEWS An object that contains the result of a query Looks and functions like a table Updated at some regular frequency Low/negligible impact on the source system Optimize by balancing the update frequency (inverse relationship between network traffic generated & quantity of data transferred)

13 ORACLE: MATERIALIZED VIEWS Oracle Database: Materialized Views EMR SQL/ODBC DI Servers (stores historic data)

14 Processing as little data as fast as possible TIP 3: BIG DATA PROCESSING

15 BIG DATA RESOURCE MANAGEMENT Process only the data that is required to meet the customers needs Leverage DI tools/objects to facilitate this As a developer, you need access to tools to monitor system performance during testing: CPU utilization Memory utilization Data storage utilization

16 EXAMPLE: BASIC SCRIPT Build a Repository Build an Adhoc Model

17 SELECTIVE PROCESSING OF DATA Transform Splitting the repository by date 2. Transform directory file list to include only specific files, create File List Input 3. Can be used to process different amounts of data depending on day-of-the-week

18 CREATE SMALL, EFFICIENT MODELS Data File List Transform Parameter File 1. Split adhoc into multiple files 2. Transform file list to create parameters for model building (i.e. filename, model number, etc ) 3. Pass parameters to loop & build models

19 Both content speed, and project speed TIP 4: SPEED TO USERS

20 TWO TYPES OF SPEED 1. Speed of Use 2. Speed to Users

21 DESIGN FOR SPEED OF USE 1. Speed of Use 2. Speed to Users Think process improvement what is the best use of data/technology to meet the customers requirements How does the solution fit into the customers most efficient workflow? The solutions architecture depends on customer needs Near real-time (NRT) Hourly Daily Manage increase in granularity when creating repositories and models

22 INCREASE PROJECT ITERATION SPEED 1. Speed of Use 2. Speed to Users Think process improvement not report remediation when defining customer requirements Invest in developing regular customers By making data accessible from the source system even though its not validated Perform data validation on a project by project basis

23 GOAL: BALANCE OBJECTIVES Maximize Speed Minimize Complexity Maximize Automation

24 Real results at Munson Healthcare RESULTS

25 MHC CLINICAL DATA WAREHOUSE Orders Production Pharm Rad Tasks MBO Doc Order Catalog Encounter Events Person

26 OUR RESULTS Data and reusable content is available for use in many upcoming projects Ease in ongoing prioritization Faster project iterations Clinician workflow changes from static reports to live, living, breathing actionable content

27 LESSONS LEARNED Communicate to customers that CBI projects are not just report remediation The timing of initiating customer engagement is extremely important Make developing regular customers must be a priority Focus on delivering the customers content, try not to get too distracted However; if a task will most likely add value at scale for future content, do it!

28 WHERE WE GO FROM HERE Develop and publish more content faster leveraging existing data & programs More clinical content encompassing the continuity of care Regular linking of orders to results More near-real-time content to clinicians More predictive content to clinicians Preparation for integrating semi-structured data into DI applications

29 THANK YOU & CONTACT INFO Aaron McMaster Sr. Data Science Professional Munson Healthcare Phone: LinkedIn: