Accretive Health - Quality Management in Health Care

Better Living

Through Data

Science Scott

Nicholson

@scootrous

snicholson@

accretivehealth.com

lnkd.in/scott

Helping people and businesses

make better decisions

Does big data help people make better decisions? No, insights do. BD is a realization that we can do more with data than we previously thought, just as much as it is about more data being available Companies in 2000 who didn’t know what to do with their “small” data won’t be any better off with big/huge/fat data today. It’s about insights, and data scientists are well-suited to create them. I’d prefer an brilliant Excel/SQL guru who asks the right questions than a deeply technical ‘big data’ engineer who focuses on elegance and algorithms.

Today

What is data

science?

Project phases

Where do you find

people who can do

it?

/Hila

“Data Scientist” means different

things to different people

/Hila



Credit: Drew Conway

/Hila



Credit: Hilary Mason

My definition of a data scientist:

Someone who uses data to solve

problems end-to-end, from asking

the right questions to making

insights actionable.

End-to-end data science: five stages

Ask the right

questions

Choose your

approach

Extract & clean

your data

Build a model

Deploy, learn, iterate

One of the hardest

things to find in a

data scientist

Phase 1

Ask the Right

Questions

Do we always

need to build a

model?

Phase 2

Choose an

Approach

Leverage other

disciplines and

intuition

Is building a

model the first

thing you should

do?

Credit: Sam Shah

The g(l)ory of data

science: most of

the work is here

Phase 3

Extract and

Clean Data

ddd

ddd

In the trenches, dirty jobs, porta-potty Vs Luxury, rocket science, fast cars

Health Care

EHR is not

designed for

data extraction

LinkedIn

On the frontier,

but still difficult

to do agile data

Grab better/new logos

For most

problems, a wheel

has already been

invented…

…just recognize

the wheel!

Example: missing

charges on bill

Phase 4

Model

Building

Always use

workhorse

models first

Online advertising: logistic regression in production at Yahoo for a long time

LinkedIn

Skills universe

Health Care

Networked data

also common

Agile Data

dd

Focus on quick solutions to identify bogeys and get feedback Think like Eric Ries Photo of sand trap?

Deployment and

execution of

predictive models

is crucial

Iteration is key,

especially in an

agile analytics

framework

Phase 5

Deploy,

Learn, Iterate

LinkedIn

Subscriber churn

prevention emails

Health Care

Population health

management &

quality of care

LinkedIn

Build a viewer

app

Picture of viewmaster

Who is good at this stuff?

Well that’s great but who is going to do all of that work?

Just as physicists moved to

Wall Street to be quants and

then on to online advertising

and consumer web, there will

be a significant talent

migration into health care in

the next few years.

But huge opportunities

But huge opportunities

One of the fundamental

problems of our time

18% of GDP! 0.01% is giant

revenue potential

Data availability and

richness only increasing

The right people are

realizing data and data

science are core to the

solution.

Take-aways

Data science is industry-agnostic

There are many challenges, but this is just the

beginning.

There are many challenges, but this is just the

beginning.

EHR data extraction and

updates difficult

Implementation barriers

Nothing scales

Privacy issues

Data aggregation difficult

Not all hospitals are

Stanford, Vanderbilt, etc.

What can we do

about these

challenges?

What can we do

about these

challenges?

Daily/hourly decision

support?

Communicate value

of data mining to

patients

SMART, roll-your-own

EHRs

Thank you! (we’re hiring)

Scott

Nicholson

bit.ly/accretive-data-science-job

@scootrous

snicholson@

accretivehealth.com

lnkd.in/scott

Accretive Health - Quality Management in Health Care

Documents

Transcript of Accretive Health - Quality Management in Health Care