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Predicting Patient Outcomes in Real-Time at HCA

Predicting Patient Outcomes in Real-Time at HCA

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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

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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  1. 1. 1 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
  2. 2. 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
  3. 3. 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
  4. 4. 4CONFIDENTIAL - Contains proprietary information. Not intended for external distribution. Where We Are
  5. 5. 5CONFIDENTIAL - Contains proprietary information. Not intended for external distribution. 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
  6. 6. 6CONFIDENTIAL - Contains proprietary information. Not intended for external distribution. CRISP-DM and Data Science
  7. 7. 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
  8. 8. 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
  9. 9. 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
  10. 10. 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
  11. 11. 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?
  12. 12. 12CONFIDENTIAL - Contains proprietary information. Not intended for external distribution. Delivering Value to the Business
  13. 13. 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
  14. 14. 14CONFIDENTIAL - Contains proprietary information. Not intended for external distribution. Data Sources are Constantly Changing
  15. 15. 15CONFIDENTIAL - Contains proprietary information. Not intended for external distribution. 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
  16. 16. 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
  17. 17. 17CONFIDENTIAL - Contains proprietary information. Not intended for external distribution.
  18. 18. 18CONFIDENTIAL - Contains proprietary information. Not intended for external distribution. 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
  19. 19. 19CONFIDENTIAL - Contains proprietary information. Not intended for external distribution. Summary
  20. 20. 20CONFIDENTIAL - Contains proprietary information. Not intended for external distribution. Questions

Editor's Notes

  • Really focusing on the use of Tools
    Architecture
    Deployment
  • Add number of inpatients (~1.8 million)

    real-time – prediction is used to lengthen the intervention window for therapy.

    Batch – for operational stuff.
  • Ask the right question
    Gather data to support your hypotheses
    Test your assumptions
    - Get through this loop as quickly as possible -> h2o makes modeling component straightforward and pain-free.


    Don’t get caught up on this slide
    Cross Industry Standard Process for Data Mining, commonly known by its acronym CRISP-DM, was a data mining process model that describes the overall approach to solving business (or clinical) problems with predictive analytics. Working through this process requires both a Business understanding and Data understanding at the forefront of everything.
    Data preparation
    Modeling
    Evaluation
    Deployment

    The overall arching goal is to extract knowledge from data, using predictive modeling to visualize and present data with an intelligent awareness of the clinical and/or business consequences

  • Data science projects begin by asking a clearly defined business question
    What business decisions will be made using the results of the analysis?
    What does “done” look like?
    Establish that the project falls within one of five defined analysis types:
    Type 1. Classification: Is this A or B?
    Type 2. Anomaly Detection: Is this unusual?
    Type 3. Regression: How much/how many?
    Type 4. Unsupervised Learning: How is it organized?
    Type 5. Prescriptive: What should I do next?




    GitHub: web-based tool allowing for version control and SCM
    Teradata SQL Assistant: Windows-based tool for building and running sql queries against our EDW
    DRAKE: workflow tool
  • SQL, R, Clojure

    Balancing
    Center and scale
    Sampling

    Why do we use R vs. h2o?

    Engineering Features -> we do FE outside of h2o so pre-processing
  • Historically we were restricted by the computational availability of our laptops.

    Nice visualizations for eval results!!!
  • Weak signal?
  • Apply the model to real live data and gain clinical feedback on patients we are seeing in our hospitals now
    Build out infrastructure and architecture to score patients in real-time

    Preventing negative patient outcomes and saving lives

    H2o is the harness that runs on the jvm, brining predictive models to the patients’ bedsides
  • Tableau helps you work with business to solve problems, quickly.
  • Want to use the model in real life and gain clinical feedback
    Create a way for model to capture feedback through an application
    See if the model fits into clinical workflow.

    Near real-time does not scale

  • real-time in healthcare means HL7 based messaging.
    Clojure encapsulates the pojo
  • Cloudera resilient distributed dataset
  • Doing all of this on every single commit

    4 times an hour (05, 20, 35, 50) the job is started A Docker container is spun up, and a jar is executed Data is retrieved from OpenGate, aggregated and transformed Predictive model is applied Predictions are written to PostgreSQL Logs are stored and execution results are reported
  • GOAL: The model accurately predicts patient outcomes earlier than those
    identified through current clinical processes

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