Getting Started With a Healthcare Predictive Analytics Program

GETTING STARTED WITH
PREDICTIVE ANALYTICS
Professor Bryan Bennett
Northwestern University
July 14, 2014

www.dataenabledhealth.com
@enabledhealth
Agenda
• Predictive Analytics Primer
• Predictive Analytics Program
• The Analytics Program Lifecycle
• Areas Where Predictive Analytics Can be
Utilized Right Away
• Major Challenges to Implementing a
Healthcare Predictive Analytics Program

@enabledhealth
What is Predictive Analytics?
• Predictive analytics is the practice of extracting
information from existing data sets in order to
determine patterns and predict future outcomes
and trends.
• It does not tell you what will happen in the
future.
– It forecasts what might happen in the future with an
acceptable level of reliability, and includes what-if
scenarios and risk assessment.
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@enabledhealth
Gartner Even Goes Further
• In addition to predicting what might happen, they
add:
– Analysis measured in hours or days (real-time or near
real-time).
– The emphasis on the business relevance of the
resulting insights, like understanding the relationship
between x and y.
– An emphasis on ease of use, thus making the tools
accessible to business users.
Source: www.gartner.com
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@enabledhealth
Gartner Analytic Ascendancy Model
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@enabledhealth
Gartner Analytic Model Examples
Type of
Analytics
Question
Answered
General Business
Example
Healthcare Example
Descriptive
Analytics
What
Happened?
How many cars did we
sell last year?
How many patients were
diagnosed with HBP last
year?
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@enabledhealth
Type of
Analytics
Question
Answered
General Business
Example
Healthcare Example
Descriptive
Analytics
What
Happened?
sell last year?
year?
Diagnostic
Analytics
Why Did It
Happen?
Why did we only sell x
cars last year?
Why did these patients
develop HBP?
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@enabledhealth
Type of
Analytics
Question
Answered
General Business
Example
Healthcare Example
Descriptive
Analytics
What
Happened?
sell last year?
year?
Diagnostic
Analytics
Why Did It
Happen?
cars last year?
develop HBP?
Predictive
Analytics
What Will
Happen?
If I run x advertising
programs, how many
cars can we sell?
What are the chances Mr.
Jones’ HBP will result in a
stroke?
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@enabledhealth
Type of
Analytics
Question
Answered
General Business
Example
Healthcare Example
Descriptive
Analytics
What
Happened?
sell last year?
year?
Diagnostic
Analytics
Why Did It
Happen?
cars last year?
develop HBP?
Predictive
Analytics
What Will
Happen?
If I run x advertising
programs, how many
cars can we sell?
What are the chances Mr.
Jones’ HBP will result in a
stroke?
Prescriptive
Analytics
How Can We
Make it
Happen?
What do we need to do
to sell x number of
cars?
Mr. Jones should be put
on x medication to
prevent his HBP from
resulting in a stroke.
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Getting Started With a Healthcare
Predictive Analytics Program

@enabledhealth
Predictive Analytics Implementation
• Needs executive support
• Needs a well-defined business challenge or query
– Is there a relationship between certain variables and a health
outcome?
• Needs lots of data
– Past and current
• Need the right team
– Quantitative (numbers) and qualitative (strategic)
– Transform data from information to intelligence and insight for
organization
• Needs to be an integral part of the organization’s
operations
• Need to track results and update models
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@enabledhealth
Importance of Executive Support
• Success is not all about the tools utilized or best
analyst
– Management support for analytics throughout the
organization has proven to be a critical success factor,
including:
• Top down mandates for analytics, sponsors and champions
• Being open to change and new ideas
• Having unified analytics-driven focus on the patient’s health
• Identifying and addressing operational threats to patient
care

@enabledhealth
• Enterprise-wide solutions needs enterprise-wide
support
– Cross departmental silos
• Need sufficient resources
– Right people on the team
– Commitment from other departments
• Most important of all attributes
– Makes the others happen

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Executive Support Leadership
Management Leadership

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Executive Leadership Involves
• Using a good solution selection process
• Making sure you have the needed resources
to complete the project
– Financial and personnel
• Having a vision for where you’re going
• Building credibility with your team members
• Raising your team members to their potential

@enabledhealth
Wall of Shame
• Examples are Everywhere
• CEOs and CIOs are resigning or
terminating due to problemed EHR
implementations
• Blame game
– I.T. can’t force EHR or analytics program on staff
• Like telling them how to practice medicine

@enabledhealth
Implementation Complexity
• Many EHR implementation mistakes
• An analytics program implementation is much
more complex than an EHR
– On a scale of 1 to 10
• EHR = 5 Analytics = 12

@enabledhealth
• Healthcare Challenge
– Executives have to manage organization’s staff to get
their cooperation and buy-in
• Particular challenge working with providers who might
believe someone is trying to tell them how to practice
medicine
• Possible Solution
– Be involved!
– Get staff involved as early as possible
– Focus on the benefits the analytics will offer

@enabledhealth
Well-Defined Business Problem
• How do we reduce readmissions?
• How can we predict when someone might
develop a more serious condition while in the
hospital, i.e., stroke, heart failure, etc.?
• The business problem must be measureable
and the operation repeatable over a specific
time period

@enabledhealth
• Measureable
– The results of the analysis must be able to be
measured or counted to determine if the
prediction was accurate
– For example:
• Number of patients developing a certain condition

@enabledhealth
• Repeatable operation:
– The attribute you choose to measure must occur
regularly and have a repeatable pattern
– For Example:
• Patients unfortunately get readmitted regularly or
develop other measurable conditions

@enabledhealth
• Specific time period
– The variable being measured must have a specific
beginning and ending
– For Example:
• Readmissions within 30, 60 or 90 days
• Condition developed during hospital stay

@enabledhealth
– Business challenges are everywhere. The real
problem is prioritizing which one to address first
– Find the challenge(s) that have the most potential
of showing quick results and improving care
• Picking ‘low hanging fruit’ is always good
• People lose interest in longer term projects if results
aren’t delivered soon

@enabledhealth
Data Needs
• A variety of data is needed for an effective
predictive analytics program
– Transactional and descriptive
• In many cases, the data is usually kept in
multiple silos across the organization
• A data warehouse is typically needed to
efficiently access all this data

@enabledhealth
Organization
Sales &
Marketing
Executive
Accounting
& Finance
Purchasing/
Production
Customer
Service
Corporate Data Silos
Typical Data Model

@enabledhealth
The Problem With Data Silos
• Data silos are a repository of data stored and
used by a single or few departments in an
organization
• Usually does not exchange data with other
groups or departments
– Data may not be updated
• Impacts data integrity
• Executive sponsor is needed to “open” these silos

Sales & @enabledhealth
Marketing
Executive
Accounting
& Finance
Purchasing/
Production
Customer
Service
Corporate Strategic
Data Warehouse
Corporate
Strategic Data
Warehouse

@enabledhealth
Types of Data Needed
• Some of the types of data needed:
– Clinical Data
– Demographic Data
– Insurance / Reimbursement Data
– Operational Data
– Cost Data

@enabledhealth
Data Needs
• Healthcare Challenge (1)
– There’s lots of data but a lot of it is locked in
departmental silos which ultimately makes all the
data useless
– Determine what data is valuable and have
executive open regular access to it

@enabledhealth
Data Needs
• Healthcare Challenge (2)
– There are no predefined predictive variables, like a
FICO score
• Some basic variables like high blood pressure may
predict other potential problems
• Don’t know which variable(s) will be most predictive
– Need a good team of modelers, analysts and
clinicians to make sense of the model results

@enabledhealth
Data Warehouse Challenges
• A well designed data warehouse will provide
the data services infrastructure to for an
effective predictive analytics programs
– One of the most overlooked aspects
• Data warehouses also decrease the risk that
current (or more recent) data gets accidentally
overwritten with outdated (or less recent)
data

@enabledhealth
Data Warehouse Challenges
– Healthcare data includes structured as well as
unstructured (text) data
– Tools are currently available that take the
unstructured data and convert it into some form
of useful structured data

@enabledhealth
The Analytics Project Team
• Executive – to insure access to the needed data
and the right people
• Champion – ultimate owner of the analytics
project whose problem or query we seek to
answer. Could be department head or CMO
• Project Manager – needed for larger projects to
manage the day-to-day needs of the project and
to make sure the analysts have the data and
support they need

@enabledhealth
• Data Analyst – usually builds or gathers the
data into a format or file the modeler will use
• Modeler – usually a statistician who will
actually build the models using various
modeling tools
• Clinician – needed to help the team make
sense of the results / numbers from a
healthcare point of view

@enabledhealth
– The challenge will be finding qualified people from
an already scarce resource pool and getting them
to accept the lower wage healthcare may pay
– The entire team must be paid market wages
– Outsourcing might need to be an option
• Bottom Line: GET HELP!
– Especially when first starting

@enabledhealth
Integral Part of the Organization
• Organization must constantly be asking why,
what, who, when, where and how
• Not solved solely with technology
– Must include processes to capture the right
information
– Personnel must be trained to capture and properly
record information for analysis

@enabledhealth
EHR Staffing Mistake
• Many tried to implement EHRs with current
staff
– Have other responsibilities
– Lacked skills set
• Need specialized personnel to be successful

@enabledhealth
Integral Part of the Organization
– Everyone must buy-in to the results of the
analytics program including clinical, finance and
operational staff.
– Focus on the benefits of your analytics program
– Show real results to providers
• Avoid the impression that the analytics will tell them
how to practice medicine

@enabledhealth
Results Tracking and Model Updates
• Need to determine if results were predicted
by the model
• Utilize prior results to improve on model
– Update and test frequently
• Otherwise, model may loose its effectiveness

@enabledhealth
Results Tracking and Model Updates
– With the right team in place this should not be an
issue
– Have the right team but also the right process to
manage and update the models
– See Analytics Program Lifecycle

The Analytics Program Lifecycle

@enabledhealth
Journey vs. Destination
• A predictive analytics program
implementation is a journey not a destination
• The results and learnings from previous
analysis and modeling should be incorporated
in future analysis
– Builds a stronger model
• Must follow the Analytics Program Lifecycle

@enabledhealth
The Analytics Program Lifecycle
• Initial Research & Pre-Analysis
– Defining the business problem or query
• Data Gathering
– Capturing data and creating file for analysis
• Execution
– Model building and applying to dataset
• Post-Analysis
– Did the model predict what was expected
• Adjust model based on results
– Use the results of the analysis to improve the model

@enabledhealth
Pre Analysis
Data
Gathering
Execution
Adjustment
Post
Analysis
Analytics
Project
Lifecycle

Areas Where Predictive Analytics
Are Being Utilized Right Now

@enabledhealth
Areas Showing Benefits Now
• Improved Patient Flow
• Disease Outbreak Prediction
• Emergency Room Risks
• Reduced Readmissions
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@enabledhealth
Improved Patient Flow
• Can help an organization predict which
resources will be needed at any given time
• Predicting patient flow versus patient tracking
• Reduces bottlenecks and wait times
– Especially in the emergency room
– Increases patient satisfaction
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@enabledhealth
Improving Patient Flow
• Admissions and discharges
– Efficient patient placement at admission
– Find bottlenecks and drive for earlier or later
discharge times
• Capacity management
– Identify underused beds and labs to better target
patient usage
– Improves patient care and increased revenues
• Transport and housekeeping
– Track job times and responsiveness to improve
turnover
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@enabledhealth
Disease Outbreak Prediction
• Google Flu Trends has been shown to foresee
an increase in influenza cases 7 to 10 days
earlier than the CDC
– Based on online search trends
• People with symptoms seek further information
• Can pinpoint disease increase down to the hospital
level
– Resources can be allocated to prepare for influx of
patients with the flu
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@enabledhealth
Predicting Disease Outbreak
• Google Flu Trends found a close relationship between how
many people search for flu-related topics and how many
people actually have flu symptoms
– A pattern emerges when all the flu-related search queries are
added together
• They compared query counts with traditional flu
surveillance systems
– Discovered that many search queries tend to be popular exactly
when flu season is happening
• By counting the frequency of the search queries they can
estimate how much flu is circulating in different countries
and regions around the world
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@enabledhealth
Emergency Room Uses
• Used to predict whether a patient is likely to:
– Go into cardiac arrest
– Suffer a stroke
– Potentially suffer from sepsis shock
• While in the emergency room
• Collecting real time data along with patient’s
clinical history
– Compare to prior patient data
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@enabledhealth
Reduced Readmissions
• Risk of readmission in 30 days can be
predicted in order to assist with the decision
to release a patient
• Reduces cost of readmission and the
opportunity cost of a patient occupying a bed
that could be used by someone else
• Requires a proactive versus reactive approach
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@enabledhealth
Reducing Readmissions
• The hospital must understand the factors effecting
readmissions (discovery)
– Create an algorithm built on data from past patients who
were and were not readmitted, i.e. what was different?
• Create automated processes to identify patients who
are at risk for readmission based on clinical,
demographics, etc.
– Counter with a strategic response
– Gaining information immediately from failures
• Make sure personnel adher to the identified strategy
– Evaluate effectiveness of their approach.
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@enabledhealth
• Structuring a Project
– Problem Definition
– Data Needs
– Modeling
– Results Analysis

@enabledhealth
• Structuring a Project – Problem Definition
– What patients are most likely to be readmitted
– What are the causal drivers of readmission

@enabledhealth
• Structuring a Project – Data Needs
– Patient vitals
– Patient conditions
– Departments involved in patient care
– Specialties involved in patient care

@enabledhealth
• Structuring a Project – Modeling
– Run simulations, optimizations and/or regression
analysis to determine likelihood or readmission
– Use all patients
• Readmitted and Non-readmitted
– Create an individual readmission risk score

@enabledhealth
• Structuring a Project – Results
– Use risk score results to determine which patients
likely to be readmitted in next 30 days
• Real time results or daily
• Hospital can exercise appropriate
interventions
– Communications
– Post-discharge interventions

Major Challenges to Implementing a
Healthcare Predictive Analytics Program

@enabledhealth
Major Challenges Summary
• Have involved executive support
• Building a comprehensive data warehouse
• Identifying predictive variables
• Hiring analysts / modelers
• Incorporating the program into the data
collection and transformation process
• Access to the analysis on devices at the point
of patient contact

@enabledhealth
Questions and Answers
• Contact Information:
– J. Bryan Bennett, “The Professor”
• Healthcare Transformation Specialist, Data Scientist and
Predictive Analytics Professor
– E-mail
• bryan@dataenabledhealth.com
– Website / Blogs
• www.dataenabledhealth.com
• www.himssfuturecare/blog/1266
– Twitter
• @enabledhealth
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Getting Started With a Healthcare Predictive Analytics Program