Big Data and Analytics

Seven Safety Checks before you Dive

into the Big Data Ocean

April 15, 2015

Frank Poggio, President

The Kelzon Group DISCLAIMER: The views and opinions expressed in this presentation are those of

the author and do not necessarily represent official policy or position of HIMSS.

Conflict of Interest

Frank Poggio has no real or apparent conflicts of interest to report …other than being an avid scuba diver.

© HIMSS 2015

I . Explain how to scrub your data.

II. Describe techniques for proper model

validation.

III. Discuss why you should keep your focus as

narrow as possible

IV. Explain how to factor in change, particularly

changes in medical practices and protocols.

V. Identify areas where you can see fast ROI

without investing millions.

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Learning Objectives

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The ‘Quants’ are Coming…

to Health Care

Hospitals with Big Data

and Analytic Projects:

2011 - 10%

2016 –50%

Source: Shelly Price, HIMSS Director, 11/25/2014

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A Small History of Big Data: 1. 1940’s, Initiated during WW2 to determine optimum

bombing runs, anti-aircraft gun placements, depth

charge placements, etc.

2. 1950-60s, Applied to

business operations.

Optimization

techniques to improve

production using linear

programming, mostly in

process industries. 3. 1970s, Monte Carlo simulations for strategic planning

and management decision making. 4. 1990 – present, Probabilistic Analysis, Random Walks,

Chaos and Game Theory. Primarily for stock market trading.

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BD&A in Healthcare

Why Now?

…Real value or just more marketing

hype and another revenue boondoggle

for vendors?

Predictive

& Strategic

Tools

Why Now?

Anatomy of Info Systems

Medical

Decision

Making

Management

Decision

Making

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Typical Operational Analytics Facility Planning : Basic assignment of resources, quadratic assignment problems, traffic flow, target assignment problems, Bayesian theory applications. Optimal Search and Routing , such as determining the routes of phlebotomists to minimize med tech labor. Supply Chain Management - managing the flow of raw materials and components based on viable or uncertain demand Automation - automating or integrating robotic systems in human-driven operations processes. Real time patient monitoring. Floor Planning: designing the layout of they key equipment and resource components to reduce process time and cost Transportation - efficient management of transport and delivery systems using linear programming Labor resource allocation of inter/intra department tasks, work flow and hand-offs

Traffic Analysis - Queuing systems to reduce wait times and support predictive patient

scheduling.

Clinical Decision Support – using predictive tools such as Fuzzy Logic

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Typical Strategic Analytics Long Term Horizons…

• Population Management

• Market Strategies

• Demand Forecasting

• Response to Regulations

• Facility Location

• Long term Contract Simulation

and Negotiation

• Program /Service Initiation/

Expansion

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Before you Dive in

The Seven Safety Checks

To avoid….

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Safety Check #1

1. Big Data and Bad Data Don’t Mix !

Before you dive in you should get ask and answer:

a) What is in your data?

b) What ‘vintage’ /context?

c) Originally captured where and how?

d) What coding structures were used?

e) Impact of system conversions /mutations?

f) What is the overall consistency and integrity of your data?

Scrub your data…

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“It’s easier said than done. In the absence of interoperability, the biggest

challenge is pulling data from disparate databases. “Two-thirds of the

problem is how to get the information out, how to match it, and how to tie

it together and make it useful,” says Girish Navani, eClinicalWorks’ founder

and CEO.

Safety Check 1

1. Big Data and Bad Data Don’t Mix !

“On average, healthcare's current patient registries

are very imprecise, relying mostly on billing

diagnosis codes or manual chart abstraction for

patient identification. However, we know from

experience that these diagnosis codes are a

shadow of clinical accuracy. Patient registries that

rely on billing and claims data are, on average, 30-

40% inaccurate. In an accountable care

organization… a 30-40% error rate is financially

unacceptable, not to mention the effects on

population health” Dale Saunders, Health Catalyst, November 12, 2014

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Safety Check #2 2. Focus Keep your focus as narrow as possible. Most

big data projects fail because you tried to do

too much, or you were too broad in our goals

which led to loss of control, missed target dates

and blown project budgets.

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“The dirty secret is that a significant majority of big-

data projects aren’t producing any valuable,

actionable results,” said Michael Walker, a partner

at Rose Business Technologies, which helps

enterprises build big-data systems. According to a

recent report from the research firm Gartner Inc.,

“through 2017, 60% of big-data projects will fail to go

beyond piloting and experimentation and will be

abandoned.” Wall Street Journal, 12/16/14

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Safety Check 3 3.Validate your model.

Run simulations against past time periods with known

outcomes. Did you get the answer you expected? If not revise

or replace the algorithms. Smaller models are easier to

validate, apply basic common sense against any prediction.

Get end user, usually an executive or physician group, buy-in

to the model logic so they have full trust in the data before they

can accept any predictions.

“We’ve seen buy in challenges. It comes in two

forms. One is blind trust – it looks complicated

so it must be right, or it looks complicated so it

can’t be right. You want understanding to be

intuitive…and trust between the developing

person and the decision maker”.

Wes Hunt, CDO, Nationwide Insurance. WSJ, 10/20/2014 –

“Tons of Data, Now to put it to Use”

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Safety Check #4

Big change can sink your analytics. One of the primary

reasons to apply models to big data is to predict change, then,

use that new knowledge to deal with the change before it

becomes an issue or problem.

4. Anticipate Big Change

There are changes like medical

advances and protocol revisions

that your historical big data can’t

predict. You need to understand,

anticipate and factor them into

any decisions you make.

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Safety Check #5 5. Pick the low hanging fruit first.

There are two major kinds of analytics; strategic models, and

operational models. Strategic models try to predict enterprise wide

outcomes and volumes five to ten years out. Operational models deal

with more immediate issues, such as; How can we handle higher

patient volumes using less resources? What can we do to reduce re-

admits? What is the expected ROI on a large capital investment?

They are the low hanging fruit.

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Example:

Low Hanging Fruit w/o Big Data

Tuesday, February 11, 2014 , Modern Health Care

Low-cost interventions after discharge reduce

readmissions in randomized study .

“Providing a solid base of primary-care service and

coordinating specialty care

care for high-risk patients

really does reduce repeat

hospitalization and

emergency department

visits, a new study suggests.”

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Safety Check #6 6. Beware of Analysis Paralysis

You could spend a long time drifting in the big data ocean and analysis

paralysis could easily set in. Remember, there will always be flaws in your

historical data, and no model can be perfect so do not let perfection become

the enemy of good. You do not have an unlimited budget. All analytics need

to be improved, so do it incrementally. After many iterations and revisions,

and based on your real life experiences, if the model still does not make

sense to you toss it out and move on.

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Safety Check #7

7. Understand and

Educate on BD Limits

What problems are you really trying to solve? Many organizations waste time

and money building models for problems they really do not have or

understand. Due to ‘hype’, department managers come to believe the model

will ‘fix’ operational problems. Department managers need to be trained in how

to use and interpret these powerful tools. Understand and communicate what

the tool can, and can’t do, and the real limitations of your model.

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Align the right talent…

Such a drastic change, of course, will require that healthcare organizations

develop or hire new skill sets to compile what John Mattison, MD, chief medical

information officer at Kaiser Permanente, called a “cross-breed of expertise

wherein data scientists work in tandem with subject matter experts.” “Big Data Not for the Weak”, Modern Healthcare, 11/25/2014

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Dimensional Insight,

Health Catalyst,

IBM

Information Builders,

McKesson

Microsoft,

MicroStrategy,

Oracle,

Qlik,

SAP,

SAS

Tableau

For more on evaluation criteria and processes email:

FLP@KelzonGroup.com

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1. Bad Data and Big Data – DON’T Mix

2. Stay Focused

3. Validate Your Model

4. Anticipate BIG Changes

5. Pick the Low Hanging Fruit

6. Beware of Analysis Paralysis

7. Understand and Educate on BD&A Limits BD&A – A very Big Opportunity,

A very Big Investment Risk

Thanks…and Q & A…

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Frank L. Poggio has over forty years of experience in health care

systems. He has served as a hospital administrator/CFO,

software entrepreneur, and industry consultant. He is President of

The Kelzon Group a firm that focuses on HIT system issues.

Previously, he was General Manager of Mediware, Inc. Blood

Bank Division. He served as EVP of Pharmacy Data Systems Inc., and before that as

President of Citation Computer Systems, Inc. In 1980, Mr. Poggio founded Health Micro Data Systems

(HMDS), the first firm to implement client server based systems for health care organizations.

He started his career with General Electric where he completed operations research, strategic planning

and financial modeling projects. Over his career he has presented many times at HIMSS and HFMA

and served as a faculty member of the University of Wisconsin Graduate School of Business.

Mr. Poggio can be reached at: FLP @ KelzonGroup.com