Data Scientist Allison Baker and Development Manager of Data Products Cody Hall work with a talented team of data scientists, software engineers, and web developers, and are building the framework and infrastructure to support a real-time prediction application, with the ability to scale across the entire company. Paramount to these efforts has been the capability of integrating the architecture for software production with the predictive models generated by H2O. This talk will review the processes by which HCA is building a pipeline to predict patient outcomes in real-time, heavily relying on H2O’s POJO scoring API and implemented in Clojure data processing. #h2ony - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

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Predicting Patient Outcomes in Real-Time at HCA
Presentation by Allison Baker and Cody Hall
Hospital Corporation of America
Department of Data and Analytics, Clinical Services Group
July 20, 2016

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• Introduction to HCA
• Introduction to our team
• Data science pipeline
• Near real-time architecture
• Real-time architecture
• Current POC goals
Overview

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“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

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Where We Are

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Data Science and Data Products Teams
Dr. Martin Tobias
Data Scientist
Sandeepkumar Kothiwale
Data Scientist
Allison Baker
Data Scientist
Dr. Nan Chen
Data Scientist
Kunal Marwah
Data Scientist
Gerardo Castro
Data Scientist
Chris Cate
Data Scientist
Igor Ges
Data Product Engineer
Josh Wolter
BI Developer
Dr. Jesse Spencer-Smith
Director of Data Science
Dr. Edmund Jackson
Chief Data Scientist
VP of Data and Analytics
Warren Sadler
Data Product Engineer
Cody Hall
Development Manager of Data Products
Nick Selleh
Application Engineer

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CRISP-DM and Data Science

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• 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

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• 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

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• Analytic server
– 64 cores
– 4 Terabytes of hard disk
– 1.5 Terabytes of RAM
• Iterate models
• Evaluate statistics
Modeling

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• Consider
– Re-defining the problem
– Additional modeling
– Additional data sourcing
• Discuss results with clinical owners and
business stakeholders
– Consider additional features
Interpretation and Reporting

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• 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?

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Delivering Value to the Business

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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

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Data Sources are Constantly Changing

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Prediction Product
Facility + Team
Patient
Kafka
Topic
OpenGate
MS
SQL PostgreSQL
Analytic
Store
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:
Vitals
Lab results
Orders
Demographics
Surgery times
Nursing documentations
Prediction
Measures
+ EventsHL-7
Measures
+ Events
& PredictionHL-7
Measures + Events
HL7QL
(Spark)
Kafka
Topic
EDN Predictive Model + ETL
• Clojure (FE library)/Spark job
• PowderKeg
Measures
+ Events
Data Persistence
Near Real-Time System
Real-Time System

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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

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A Proof of Concept Use Case and Goals
Primary:
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 outcome
3. Determine the usefulness of an early prediction model within a clinical workflow
Secondary:
1. Improve the prediction model through incorporation of feedback provided by the
clinical team
2. Maximize the utility of the prediction tool to improve a clinical workflow for the
facility staff

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Summary

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Questions