data analytics in healthcare

Raymond Gensinger, Jr., MD

CMIO

Fairview Health Services

Data Analytics In Healthcare –

Lessons From Outside The Industry

Smarter Analytics

2RAY GENSINGER 2012

http://www.youtube.com/watch?feature=player_popout&v=_ZyU6po_E74

Data Analytics in Healthcare

RAY GENSINGER 2012 3

• Background and Key References

• Analytics Examples from Other Industries

• Healthcare and Analytics

• Analytics Evolution

• Organizational Analytics Readiness

• D*E*L*T*A

• Closing, Resources, & References

Agenda

Analytics At Work

4RAY GENSINGER 2012

• Big Data: The Management

Revolution

• Data Scientist: The Sexiest Job

of the 21st Century

• Making Advanced Analytics Work

for You

Hot Off the Press

Table of Contents: October 2012

RAY GENSINGER 2012 5Harvard Business Review, October 2012

The New Economy?


“Data is the new oil; it is both

valuable and plentiful but useless

if unrefined” – Clive Humbly, visiting professor of

integrated marketing, Northwestern University

Industry Analytics: Baseball


• Commentator:

“ It’s a cold one tonight…Joe

Mauer is up and is facing Phil

Hughes. Joe has yet to get on

base against Hughes so he’s

about due…That could be unlikely

though given the temperature.

Joe’s OBP is about 0.125 lower

when the temperature is less than

50 degrees out.”

Sunday Night Baseball

• Infrastructure:

• Every play of every game captured

• Sabremetrics

• Detailed meteorological data

• Situational metadata

• Runs in the season

• Men on base

• Current count



Historically

• Gut reactions of scouts

• Performance to date

• Batting average

• Hits only

• Compensation based on

individual performance at the

plate

Oakland A’s: Moneyball

Innovation

• Winners score more runs

• Runs score after you have a base runner

• Who gets on base the most

• On base percentage

• Hits

• Walks

• Analysis of the interactions and performance of players in relation to each other



• Season ticket prices set for entire season

• Individual game tickets priced prior to season starting based on last seasons data

• Popularity of opponent

• Attendance of last seasons games

• Once season starts the game prices vary daily

• Popularity of home and away teams

• Streaking teams or players

• Weather trends

Minnesota Twins: Variable Ticket Pricing

• Data rich

• Information intensive

• Asset intensive

• Time dependence

• Quality control essential

• Dependence on distributed

decision making

Healthcare and Analytics

RAY GENSINGER 2012 11http://www.madisonshope.com/images/Latest/Madison%20in%20ICU%20with%20all%20her%20support%20equipment.jpg

• Descriptive analytics provides simple summaries about the sample and about the observations that have been made. Such summaries may be either quantitative, i.e. summary statistics, or visual, i.e. simple-to-understand graphs. These summaries may either form the basis of the initial description of the data as part of a more extensive statistical analysis, or they may be sufficient in and of themselves for a particular investigation.

Descriptive Analytics

12http://quiqle.info/8731-how-to-read-a-bell-curve.html

http://en.wikipedia.org/wiki/Quantitative_research

http://en.wikipedia.org/wiki/Summary_statistics

• O/E Readmissions .99 (0.9 threshold)

• Optimal asthma care 56.7% (42.3% threshold)

• Optimal diabetes care 37.4% (32.9% threshold)

• Breast cancer screening 74.5% (75% threshold)

• Patient rating of care 72.8% (71.3% threshold)

• Kevin Love scores 26 points per game

• Kevin Love gets 13 rebounds per game

• Kevin Love plays 39/48 min per game

• Kevin Love has a .448 shooting percentage

• Ricky Rubio hands out 8 assists per game

• Ricky Rubio plays 34/48 min per game

• Timberwolves final record was 26-39

(.400)

Descriptive Analytics: Example

Sports Analogy (published) Healthcare Analogy

13

• Predictive analytics is an area of statistical analysis that deals with extracting information from data and using it to predict future trendsand behavior patterns. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting it to predict future outcomes. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions.

Predictive Analytics

14http://www.simafore.com/blog/bid/78815/Does-LinkedIn-group-growth-mirror-

Predictive-Analytics-hype-cycle

http://en.wikipedia.org/wiki/Information_extraction

http://en.wikipedia.org/wiki/Trend_analysis

http://en.wikipedia.org/wiki/Explanatory_variable

• 50% of all readmission that are

diagnosed with heart failure and

go home on more than 10

medications, but lacking an ACE

or ARB

• Pre-diabetic patients from NE

Minneapolis age 25-35 are 77%

more likely to go on to diabetes

than typical pre-diabetics

• Kevin Love increases his shooting percentage by 0.15 when Rubio is on the floor

• Winning percentage increase from 0.4 to 0.45 when both Love and Rubio play a minimum of 35 minutes

• Other players shooting percentage drops 0.08 when Love and Rubio are on the floor together

Predictive Analytics: Example

Sports Analogy Healthcare Analogy

15

Prescriptive Analytics

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• The final analytic phase is Prescriptive Analytics.[3] Prescriptive Analytics

goes beyond predicting future outcomes by also suggesting actions to

benefit from the predictions and showing the decision maker the implications

of each decision option.[4] Prescriptive Analytics not only anticipates what will

happen and when it will happen, but also why it will happen. Further,

Prescriptive Analytics can suggest decision options on how to take

advantage of a future opportunity or mitigate a future risk and illustrate the

implication of each decision option. In practice, Prescriptive Analytics can

continually and automatically process new data to improve prediction

accuracy and provide better decision options.

http://en.wikipedia.org/wiki/Prescriptive_Analytics#cite_note-2

http://en.wikipedia.org/wiki/Prescriptive_Analytics#cite_note-3

• Pre-diabetic patient from NE Minneapolis

• Age 25-35

• Engage in a wt. loss program and dietary modification

• Initiate ACEi therapy if hypertensive

• Arrange group counseling and therapy

• Age >35

• Consultation endocrinology

• Assign care coordinator

• Purchasing Pattern: March

• Cocoa Butter

• Larger purse

• Vitamin supplements including zinc and magnesium

• Blue rug

• Marketing plan

• Customized direct mail adds for baby products

• 87% likelihood of delivering a baby boy in August!

Prescriptive Analytics: Example

Marketing Analogy Healthcare Analogy

17

Analytics Evolution

RAY GENSINGER 2012 18http://media-cache-

ec6.pinterest.com/upload/272327108688186258_0HugpL3c.jpg

Questions To Be Addressed

RAY GENSINGER 2012 20Adapted from Analytics at Work. Davenport, Harris, and Morison

What happened?

(reporting)

What IS happening

now?

(alerts)

What WILL happen?

(extrapolation)

How and why DID it

happen?

(modeling, experiment)

What’s the NEXT best

action?

(recommendation)

What’s the best/worst

that CAN happen?

(predict, simulate)

When Are Analytics NOT Helpful?


• When there is no time………………………………..Answers needed now

• When there is no precedent…………………………Falling housing prices

• When history is misleading

• Highly variable history…………………………………Major economic turmoil

• Highly experience decision maker (wisdom)………The proven expert

• Immeasurable variables………………………………Emotion, family situation

• Carelessness (Mars Orbiter)

• Failing to consider analysis and

insights

• Failing to consider alternatives

• Waiting too long to gather data

• Postponing decisions

• Asking the wrong questions

• Starting with incorrect

assumptions (housing prices will

continue to rise)

• Finding an analytic that identifies

the answer you were seeking

• Failing to fully understand

alternative of data interpretation

Decision Making Errors

Logic Errors Process Errors


Organizational Analytic Readiness


• Analytically Impaired

• Lacking skills, data, or leadership

• Localize Analytics

• Disparate glimmers lacking in coordination

• Analytic Aspirations

• Willingness but lacking in a DELTA element

• Analytic Companies

• Tools and people but hasn’t turned content into a competitive advantage

• Analytic Competitors

• Uses knowledge gained to compete and succeed

Five Stages of Development

Analytical Delta


• Data

• Enterprise

• Leadership

• Targets

• Analysts

Analytics Success


• The Trouble with cubes

• Unstructured data is a lot like panning for gold, first you sift a lot of dirt

• Uniqueness

• What is it that you have that NOBODY else has

• Nike+ running sensors

• Best Buy Reward Zone

• Health Insurance Companies

• Integration through key identifiers

• Quality is less necessary secondary to the volume of data available

Data

TITLE & CONTENT

Understanding the “Mass” of DATA

• Volume

• World generates 2.5 exabytes of internet traffic each day (zetabyte annually)

• One second of traffic today equals the totality of traffic in all of 1992

• Exabyte

• 1000 petabytes

• 1000 terabytes

• 1000 gigabytes

• 1000 megabytes

• 1,000,000,000,000,000,000 bytes = 1 quintillion bytes

26RAY GENSINGER 2012

Gigabyte of Written Material

27

180 ft3

Terabyte of Written Material


180 ft3

Petabyte of Written Material


Analytics Success


• Integration of data from across the organizational silos

• Disparate data isn’t local or independent, it is FRACTURED

• Duplication of resources, services, licenses, subscriptions

• IT Leadership

• Guide the work that matters

• Create an infrastructure that can be widely leveraged

• Share a roadmap with both short and long term success strategies

Enterprise

Analytics Success


• There has to be a recognizable name and title behind the strategy; preferably

a CxO

• Hire smart people and recognize them for what the contribute

• Demand data and analysis for all decisions to be made

• Balance analysis, experience, wisdom

• Invest in the necessary infrastructure as a strategic imperative along with

any other high profile strategy

Leadership

Analytics Success


• What is it PRECISELY that you would like to achieve?

• Retail:

• Inventory management, price optimization

• Hospitality

• Customer loyalty

• Healthcare

• Maximize accuracy of initial diagnoses

• Highest value care path…Expected outcomes or better at the lowest cost

• Discover opportunities for differentiation

• Goals

• Eliminate the exodus of patients

Targets: What to Achieve

Analytics Success


• Complex work streams with many variables or steps

• Simple decisions require absolute consistency

• When an entire service line is in need of attention

• Processes that require complex inputs, connections, and correlations

• Anywhere forecasting is necessary

• Current areas of below average performance

Targets: Where to Achieve

Answer the Questions: Set the Targets


What happened?

(reporting)

What IS happening

now?

(alerts)

What WILL happen?

(extrapolation)

How and why DID it

happen?

(modeling, experiment)

What’s the NEXT best

action?

(recommendation)

What’s the best/worst

that CAN happen?

(predict, simulate)

Dr. Surgeon’s cases

start 30-45 minutes

late

Room C, Dr.

Surgeon’s, 30 minutes

behind scheduleNurses in Dr.

Surgeon’s rooms will

require OT, annual

costs determined

Dr. Surgeon clocks into

the the parking ramp

15 minutes late daily

Schedule a quick case

early morning ahead of

Dr. Surgeon

Start times consistent,

OT drops,

Revenue/case

increases, Dr. Surgeon

quits; either way

finances better

Target = Growth

Strategy = Employee Retention

RAY GENSINGER 2012 35Adapted from the “Putting the Service Profit Chain to Work,” HBR, Mar -Apr,

1994.

Internal Services Enhancing Quality for all Employees

Enhance environment

Flexible staffing

Financial benefits

Growth opportunities

Enhanced Employee Satisfaction

Improved attendance

Employee retention

Employee productivity

Enhanced Patient Experience

Consistent services

Improved interactions

Empathetic environment

Service oriented

Improved Patient Satisfaction

Retention

Word of mouth

Social media accolades

Employer expectations

Patient Loyalty

Better outcomes

Increased market share

Revenue growth

Profit

Analytics Success


• Analytic Champions: <1%

• Executive decision makers hooked on analysis

• Willing to change the business based on the results

• Analytic Professionals: 5-10%

• PhDs in economics, statistics, research methods, mathematics (or evaluation

studies)

• Programmers and statistical model developers

• Analytic Semi-professionals 15-20%

• MBAs or process improvement experts

• Apply and work the models and theories of the champions and pros

Analysts: Skills and Backgrounds

Analytics Success


• Analytic Amateurs: 70-80%

• Knowledgeable consumers of data

• Business managers operating a business unit or help desk staff trying to

anticipate the source of a system error

• The Farm Team

• Curious innovators from around the company

• Ask questions challenging the status quo

• Experiences unrelated to healthcare but knowledgeable about math and

statistics

Analysts: Skills and Backgrounds, cont.

RAY GENSINGER 2012 38Copyright: Charles Peattie and Russell Taylor

• Good to great salary

• Opportunity to create something

new

• Recognition

• Lots of unstructured data

• Autonomy

• Access to “the Bridge”

• Know where to look

How to Catch a Quant

The Really Big Fish You Need the Right Kind of Lure!

RAY GENSINGER 2012 39http://www.dnr.state.oh.us/Default.aspx?tabid=19220


Let them know you are interested in how they contribute to the field

Ask them how their work can apply to business challenges

Offer them a challenge to evaluate as part of the interview

Assess their coding/programming skills

Host a competition

Check with your local venture capitalist

Scan through LinkedIn (who do you think created this anyway)

Scan through the “R user groups” (http://blog.revolutionanalytics.com/local-r-groups.html)

Large universities as well as the unknowns (UT Austin, UC Santa Cruz)

Hang out at Hadoop World (http://www.hadoopworld.com/)

Top Ten Ways to Find a Quant


http://www.talend.com/blog/2010/10/14/a-great-hadoop-world-congratulations-to-cloudera/

Analytics Success


• Data is a statistician’s crack,

once you have a sample you

can possibly get enough”.

Analysts: Organizing and Developing

Corporate

Division

Analytics Group

Analytics Project

Function

Analytics Group

Analytics Project

Center of Excellence

Smarter Analytics

http://youtu.be/bVY7OmYqBSY 44RAY GENSINGER 2012

http://youtu.be/bVY7OmYqBSY

• Big Data University: IBM

• LINK

• Intel Big Data: Intel

• LINK

• EMC2

• LINK

Available Online Resources


http://bigdatauniversity.com/

http://www.intel.com/content/www/us/en/big-data/big-data-analytics-turning-big-data-into-intelligence.html?cid=sem116p19340

https://education.emc.com/guest/campaign/data_science.aspx

TITLE & CONTENT

Questions?

Raymond A. Gensinger, Jr.

[email protected]

612-672-6670


mailto:[email protected]

TITLE & CONTENT

References• http://youtu.be/bVY7OmYqBSY

• Davenport, T., Harris, J., Morsion, R. Analytics at Work: Smarter Decisions, Better Results. Harvard Business Press. Boston, Massachusetts. 2010.

• http://www.simafore.com/blog/bid/78815/Does-LinkedIn-group-growth-mirror-Predictive-Analytics-hype-cycle

• Adapted from the “Putting the Service Profit Chain to Work,” HBR, Mar -Apr, 1994.

• http://media-cache-ec6.pinterest.com/upload/272327108688186258_0HugpL3c.jpg

• http://quiqle.info/8731-how-to-read-a-bell-curve.html

• http://www.madisonshope.com/images/Latest/Madison%20in%20ICU%20with%20all%20her%20support%20equipment.jpg

• McAfee, A., Brynjolfsson, E. Big Data: The Management of Revolution. Harvard Business Review. 2012; 90(10):60-69.

• Davenport, T, Patil, DJ. Data Scientist: The Sexiest Job of the 21st Century. Harvard Business Review. 2012; 90(10):70-77.

• Barton, D., Court, D. Making Advanced Analytics Work for You. Harvard Business Review. 2012; 90(10):78-83.

• http://www.dnr.state.oh.us/Default.aspx?tabid=19220


http://www.simafore.com/blog/bid/78815/Does-LinkedIn-group-growth-mirror-Predictive-Analytics-hype-cycle