predicting patient outcomes in real-time at hca

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


“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


Where We Are


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


CRISP-DM and Data Science


• 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


• 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


• Analytic server– 64 cores– 4 Terabytes of hard disk– 1.5 Terabytes of RAM

• Iterate models• Evaluate statistics

Modeling


• Consider– Re-defining the problem– Additional modeling– Additional data sourcing

• Discuss results with clinical owners and business stakeholders– Consider additional features

Interpretation and Reporting


• 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?


Delivering Value to the Business


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


Data Sources are Constantly Changing


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


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


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


Summary


Questions

predicting patient outcomes in real-time at hca

Data & Analytics