getting started with a healthcare predictive analytics program

GETTING STARTED WITH PREDICTIVE ANALYTICS

Professor Bryan BennettNorthwestern 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

Predictive Analytics Primer

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.

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

Gartner Analytic Ascendancy Model

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?

Question Answered

What Happened?

Diagnostic Analytics

Why Did It Happen?

Why did we only sell x cars last year?

Why did these patients develop HBP?

Question Answered

What Happened?

Why Did It Happen?

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?

Question Answered

What Happened?

Why Did It Happen?

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.

Getting Started With a Healthcare Predictive Analytics Program

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

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

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

Executive Support Leadership

Management Leadership

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

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

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

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

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

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

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

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

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

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

Organization

Sales &Marketing

Executive

Accounting& Finance

Purchasing/Production

CustomerService

Corporate Data SilosTypical Data Model

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

www.dataenabledhealth.com@enabledhealthSales &

Marketing

Executive

Accounting& Finance

Purchasing/Production

CustomerService

Corporate StrategicData Warehouse

CorporateStrategic Data

Warehouse

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

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

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

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

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

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

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

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

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

EHR Staffing Mistake• Many tried to implement EHRs with current

staff– Have other responsibilities– Lacked skills set

• Need specialized personnel to be successful

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

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

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

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

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

Pre Analysis

Data Gathering

Execution

Post Analysis

Adjustment

AnalyticsProject

Lifecycle

Areas Where Predictive Analytics Are Being Utilized Right Now

Areas Showing Benefits Now• Improved Patient Flow• Disease Outbreak Prediction• Emergency Room Risks• Reduced Readmissions

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

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

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

– Resources can be allocated to prepare for influx of patients with the flu

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

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

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

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.

Reducing Readmissions• Structuring a Project– Problem Definition– Data Needs– Modeling– Results Analysis

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

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

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

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

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

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

getting started with a healthcare predictive analytics program

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hbp lastyear

jones hbp

hbp fromresulting

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x advertisingprograms

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