getting started with a healthcare predictive analytics program
DESCRIPTION
These are the slides from the workshop I delivered at the Healthcare Analytics Symposium in July 2014. This 3-hour workshop walked the attendees step-by-step through the requirements to start a healthcare predictive analytics program and some of the areas already showing progress.TRANSCRIPT
GETTING STARTED WITH PREDICTIVE ANALYTICS
Professor Bryan BennettNorthwestern University
July 14, 2014
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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
Predictive Analytics Primer
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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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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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Gartner Analytic Ascendancy Model
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Gartner Analytic Model ExamplesType 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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Gartner Analytic Model ExamplesType 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?
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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Gartner Analytic Model ExamplesType 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?
Diagnostic Analytics
Why Did It Happen?
Why did we only sell x cars last year?
Why did these patients 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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Gartner Analytic Model ExamplesType 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?
Diagnostic Analytics
Why Did It Happen?
Why did we only sell x cars last year?
Why did these patients 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
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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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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
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Importance of Executive Support• 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
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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
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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
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Importance of Executive Support• 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
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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
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Well-Defined Business Problem• 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
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Well-Defined Business Problem• 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
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Well-Defined Business Problem• 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
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Well-Defined Business Problem• Healthcare Challenge– Business challenges are everywhere. The real
problem is prioritizing which one to address first• Possible Solution– 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
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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
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Organization
Sales &Marketing
Executive
Accounting& Finance
Purchasing/Production
CustomerService
Corporate Data SilosTypical Data Model
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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
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Marketing
Executive
Accounting& Finance
Purchasing/Production
CustomerService
Corporate StrategicData Warehouse
CorporateStrategic Data
Warehouse
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Types of Data Needed• Some of the types of data needed:– Clinical Data– Demographic Data– Insurance / Reimbursement Data– Operational Data– Cost Data
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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
• Possible Solution– Determine what data is valuable and have
executive open regular access to it
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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
• Possible Solution– Need a good team of modelers, analysts and
clinicians to make sense of the model results
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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
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Data Warehouse Challenges• Healthcare Challenge– Healthcare data includes structured as well as
unstructured (text) data• Possible Solution– Tools are currently available that take the
unstructured data and convert it into some form of useful structured data
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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
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The Analytics Project Team• 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
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The Analytics Project Team• Healthcare Challenge– The challenge will be finding qualified people from
an already scarce resource pool and getting them to accept the lower wage healthcare may pay
• Possible Solution– The entire team must be paid market wages– Outsourcing might need to be an option• Bottom Line: GET HELP!
– Especially when first starting
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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
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EHR Staffing Mistake• Many tried to implement EHRs with current
staff– Have other responsibilities– Lacked skills set
• Need specialized personnel to be successful
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Integral Part of the Organization• Healthcare Challenge– Everyone must buy-in to the results of the
analytics program including clinical, finance and operational staff.
• Possible Solution– 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
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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
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Results Tracking and Model Updates• Healthcare Challenge– With the right team in place this should not be an
issue• Possible Solution– Have the right team but also the right process to
manage and update the models– See Analytics Program Lifecycle
The Analytics Program Lifecycle
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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
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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
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Pre Analysis
Data Gathering
Execution
Post Analysis
Adjustment
AnalyticsProject
Lifecycle
Areas Where Predictive Analytics Are Being Utilized Right Now
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Areas Showing Benefits Now• Improved Patient Flow• Disease Outbreak Prediction• Emergency Room Risks• Reduced Readmissions
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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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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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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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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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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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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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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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Reducing Readmissions• Structuring a Project– Problem Definition– Data Needs– Modeling– Results Analysis
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Reducing Readmissions• Structuring a Project – Problem Definition– What patients are most likely to be readmitted– What are the causal drivers of readmission
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Reducing Readmissions• Structuring a Project – Data Needs– Patient vitals– Patient conditions– Departments involved in patient care– Specialties involved in patient care
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Reducing Readmissions• 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
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Reducing Readmissions• 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
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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
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Questions and Answers• Contact Information:– J. Bryan Bennett, “The Professor”
• Healthcare Transformation Specialist, Data Scientist and Predictive Analytics Professor
– E-mail• [email protected]
– Website / Blogs• www.dataenabledhealth.com• www.himssfuturecare/blog/1266
– Twitter• @enabledhealth
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