Date post: | 16-Apr-2017 |
Category: |
Data & Analytics |
Upload: | jo-fai-chow |
View: | 731 times |
Download: | 1 times |
1
Predicting Patient Outcomes in Real-Time at HCA
Presentation by Allison Baker and Cody HallHospital Corporation of America
Department of Data and Analytics, Clinical Services GroupJuly 20, 2016
2CONFIDENTIAL - Contains proprietary information. Not intended for external distribution.
• Introduction to HCA• Introduction to our team• Data science pipeline• Near real-time architecture• Real-time architecture• Current POC goals
Overview
3CONFIDENTIAL - Contains proprietary information. Not intended for external distribution.
“Above all else, we are committed to the care and improvement of human life. In recognition of this commitment, we strive to deliver high-quality, cost-effective healthcare in the communities we serve.” – HCA Mission Statement
• Hospital Corporation of America (HCA) is the leading healthcare provider in the country– 169 hospitals– 116 freestanding surgery centers in 20 states and the U.K.
• Approximately 233,000 employees across the company • Over 26 million patient encounters each year• More than 8 million emergency room visits each year• About 2 million inpatients treated annually
Hospital Corporation of America
4CONFIDENTIAL - Contains proprietary information. Not intended for external distribution.
Where We Are
5CONFIDENTIAL - Contains proprietary information. Not intended for external distribution.
Data Science and Data Products Teams
Dr. Martin TobiasData Scientist
Sandeepkumar Kothiwale Data Scientist
Allison Baker Data Scientist
Dr. Nan ChenData Scientist
Kunal MarwahData Scientist
Gerardo CastroData Scientist
Chris CateData Scientist
Igor GesData Product Engineer
Josh WolterBI Developer
Dr. Jesse Spencer-SmithDirector of Data Science
Dr. Edmund JacksonChief Data Scientist
VP of Data and AnalyticsWarren Sadler
Data Product Engineer
Cody HallDevelopment Manager of Data Products
Nick SellehApplication Engineer
6CONFIDENTIAL - Contains proprietary information. Not intended for external distribution.
CRISP-DM and Data Science
7CONFIDENTIAL - Contains proprietary information. Not intended for external distribution.
• Begin by asking stakeholders and business owners “What business decisions will be made with the analysis results?”
• Document all project and product features, timelines and code using GitHub
• Source historical data using Teradata SQL• Log all data sourcing and data extract steps using DRAKE• Options
– Continuous integration– Jenkins to monitor DRAKE builds
Problem Definition and Data Sourcing
8CONFIDENTIAL - Contains proprietary information. Not intended for external distribution.
• Run preliminary visualization • QA data testing for coverage, outliers, abnormalities, format and structural issues,
frequency, duplication and accuracy• Pre-process data
– Balance outcomes– Filter patients– Remove non-data
• Engineer features
Data Manipulation
9CONFIDENTIAL - Contains proprietary information. Not intended for external distribution.
• Analytic server– 64 cores– 4 Terabytes of hard disk– 1.5 Terabytes of RAM
• Iterate models• Evaluate statistics
Modeling
10CONFIDENTIAL - Contains proprietary information. Not intended for external distribution.
• Consider– Re-defining the problem– Additional modeling– Additional data sourcing
• Discuss results with clinical owners and business stakeholders– Consider additional features
Interpretation and Reporting
11CONFIDENTIAL - Contains proprietary information. Not intended for external distribution.
• We can effectively engineer thousands of clinically and statistically relevant features.
• We can successfully build accurate, complex and sophisticated predictive models.
• How do we take these models to the patient bedside?
What Now?
12CONFIDENTIAL - Contains proprietary information. Not intended for external distribution.
Delivering Value to the Business
13CONFIDENTIAL - Contains proprietary information. Not intended for external distribution.
Near Real-Time Tool
• Consists of 3 main components– Data source (different than historical training source)– Scoring engine– User interface
• Shows early value using a minimally viable product-based approach• Phases POC to include development time for real-time architecture• Updates in 15 minute batches• Provides near real-time predictions • Solicits feedback from facilities, focusing on accuracy and usefulness
14CONFIDENTIAL - Contains proprietary information. Not intended for external distribution.
Data Sources are Constantly Changing
15CONFIDENTIAL - Contains proprietary information. Not intended for external distribution.
Prediction Product
Facility + Team
Patient
KafkaTopic
Ope
nGat
e
MSSQL PostgreSQL
AnalyticStore
HDFS Cluster
Predictive Model• Single POJO .jar• Clojure (FE library)
ETL• Independent SQL process
HDFS Cluster
Data Source• 15 minute batches• SQL defined
Data Source• Streaming• HL7QL defined
• GitHub & Nexus• Jenkins• Tableau
Supporting Infrastructure• PostgreSQL administration
& monitoring• Docker with Node JS (UI)
User Interface (UI)• Displays measures + events• Notifications of predictions
• Prompt for acknowledgement or dismissal• On acknowledgement, disable
notifications for 12 hours
Measures + Events:VitalsLab resultsOrdersDemographicsSurgery timesNursing documentations
PredictionMeasures+ EventsHL-7
Measures+ Events
& PredictionHL-7
Measures + Events
HL7QL(Spark)
KafkaTopic
EDN Predictive Model + ETL• Clojure (FE library)/Spark job• PowderKegMeasures
+ Events
Data PersistenceNear Real-Time System
Real-Time System
16CONFIDENTIAL - Contains proprietary information. Not intended for external distribution.
Real-Time Infrastructure
• Continuously consumes HL7 messages from a Kafka topic and parses via Spark and HL7QL
• Processes (producers) publish messages to Kafka topics (categories) and subscriptions are made to the topics to process the message feeds (consumers)
• Apache Spark is the application interface to allow for cloud computing • HL7 Query Language (HL7QL) parses the messages
• Scores (predicts) on new streaming information– Runs a .jar file via a Spark process compiled from Clojure code and H2O POJO
• Deploys with Docker– Container-based application architecture
• Continuously monitors with Jenkins
17CONFIDENTIAL - Contains proprietary information. Not intended for external distribution.
18CONFIDENTIAL - Contains proprietary information. Not intended for external distribution.
A Proof of Concept Use Case and GoalsPrimary:1. Assess clinical workflow to identify how the model can support the current clinical
processes for treating negative patient outcomes 2. Determine the model’s capability to extract meaningful information from existing
and available patient data and identify patterns that predict the outcome3. Determine the usefulness of an early prediction model within a clinical workflow Secondary:4. Improve the prediction model through incorporation of feedback provided by the
clinical team 5. Maximize the utility of the prediction tool to improve a clinical workflow for the
facility staff
19CONFIDENTIAL - Contains proprietary information. Not intended for external distribution.
Summary
20CONFIDENTIAL - Contains proprietary information. Not intended for external distribution.
Questions