Python What? The Strategist as Data Geek

The opinions expressed are those of the presenter and do not necessarily state or reflect the views of SHSMD or the AHA. © 2015 Society for Healthcare Strategy & Market Development
Python What? The Strategist
as Data Geek
October 14, 2015

Speakers
• Patrick Saale– Manager Strategic Resource
Group, LifePoint Health
• Lee Ann Lambdin – Vice President Strategic
Resources, Stratasan
2 Source: Stratasan & LifePoint Health, 2015

Outline
• Role of the Analyst
• Skills of the Analyst
• Tools of the Analyst
– Python What?
– Resources
• Data Information Better Decisions
• Case Study – LifePoint Hospitals, Physician
Referral

Survey Results
• Surveyed Stratasan customers for their
perspective on the role of the analyst
• 21 responses are summarized in the following
slides
• 14 responders were analysts and 7 were users
of analysts
• Employed primarily by health systems and
hospitals

Analyst Role
Turn data into
information so
leadership can make
better decisions
Use data and analytical
tools at the team’s disposal
to support a data‐driven
approach to organic growth
Acquire data and turn it
into useful information
for customers
Summarize and provide
meaningful insight into
what data is dictating
Assist in analyzing data for
purposes of strategic
planning and business
development support
Provide market share,
competitor, and referral
info

Skills of the Analyst
Ability to be creative
and tell a story with
data
Analytical Thinking
Data Interpretation
Summarizing vast amount
of information
Computer/Arithmetic
Skills
Excel #1
PowerPoint
Mapping
Access
SPSS/Statistics
Critical
Thinking/Strategic
Thinking
Problem Solving
Inquisitive
Attention to Detail
Accuracy
Ability to
Communicate/Present
Time Management

Best Verbatim Comments on Skills
7
“Ability to determine what your customer
really needs instead of always just doing
exactly what they ask you to do”
“Support and influence others in
appropriate use of data”
“Master necessary programs to analyze
data to tell the story”
“Analytics tool knowledge (Excel, Access,
SPSS, etc.) doesn’t really matter which one
as long as you know it”
“Ability to understand data trends and use
it to tell a story”
“Knowledge of the field’s terminology and
data available including sources of data”
Source: Stratasan & LifePoint Health, 2015

Tools of the Analyst:
Most Important Data Sources
Internal hospital
or system data
(financial &
volume)/company
results, E.H.R.
State IP, OP, ED,
Observation
databases (where
available)
Demographics
Mapping software
Federal Data
(Medicare)
Industry and
competitor
research
Google searches
Psychographics
(Tapestry
Segmentation)

Top Questions & Project Requests
• What’s my market share?
– Reasons for growth/decline?
• What’s our outmigration?
• What are my competitors doing?
• What are my opportunities for growth?
– Are there needs in the area not currently being met?
• What is the profitability of service lines?
• How many cases are coming from ____?
• How many doctors do I need?
• Where do the doctors need to be located?
• Operational performance?

Best Verbatim Comments: Questions
10
“Can we change this?
Can we have an update?
Can we get it before the deadline?”
“Market share reports to determine current volume,
potential added volume, capacity and service needs”
“Market and finance data reports
for specific service lines”“Do we need more or less
physicians and where do they
need to be located?”
“‐ Create a map with data
‐ Summarize the data
‐ Trend the data”

Anything else we need to know
about Analysts?
11
“They need to always be focused on helping
planning and marketing generate ROI.
Because they are most often the most
analytical thinkers of the group, they need to
lead the charge in measuring and planning
how we can prove the value of what
marketing and planning brings to the table.”
“Need to be creative and think outside the
box. Good communication skills and
ability to ask questions about what is
trying to be accomplished that will
influence data support and analysis.”
“They are really smart!”
“We're awesome ;)”
“good analysts want to spend more
time thinking about how to help
solve problems by drawing
conclusions from data, and less
time on mundane task work.”

Hiring: What to Look for
in an Analyst
• Critical thinking skills
• Holistic decision‐making
• Use of data to inform decision‐making
• Knowledge of how to leverage people who
know Python and big data
• Understanding that no one person can do it all
• Specific skills for specific roles

Best Use of Analysts’ Skills
13

14
DIKW Pyramid: The ConceptDIKW Pyramid: The Concept
WisdomWisdom
KnowledgeKnowledge
InformationInformation
DataData

Executives
Drivers
Implementers

Executives
Drivers
Implementers
What to do?
How to do it?
Delivery

ExecutivesWhat to do?
DriversHow to do it?
ImplementersDelivery

Translation
Translation

Bridging Worlds:
Generate Data-Driven Insight
Attributes, Skills and Tools
? ?
?
!
!
?
?
!
20 Source: Bridging Worlds, SHSMD, 2014 p.57; used with permission
!
!
!
!
!
!

What’s important for you?
21 Source: Bridging Worlds, SHSMD, 2014 p.57; used with permission

22
The Analyst

How Big is Big?
23
Big Data
Medium Data
Small Data
A lot more
problems with
medium and small
data, and
opportunities in
the data you deal
with every day

Look at the Data
• What is in this data?
• What question am I trying to (can I) answer
with this data?
• How do I leverage the data to answer the
question?

Glossary: Analyst as Data Geek
• Handout
–Definitions
–Uses

Look at the Data
• R
– R Studio is a free software environment for statistical computing and graphics. It
compiles and runs on a wide variety of UNIX platforms (foundation operating
systems are built on), Windows and MacOS.
• SPSS
– IBM SPSS Statistics is an integrated family of products that addresses the entire
analytical process, from planning to data collection to analysis, reporting and
deployment. Used for describing large data sets, for example 3 years of patient
data.
• SAS
– Another brand of statistical software
• Python
– is a programming language that has powerful libraries for data analysis.
It also allows you to automate steps of processing or analyzing data.

Actual Python code:
script that is loading
ICD10 codes into a
database from CSV
files so we can run
queries and joins

Process the Data
• How do I make the data useful?
• What are we going to do to it?
– Rollups, aggregation, curation, cross‐walking
– Machine learning (fancy statistics)
• Where are we going to do it?
– Your laptop
– Cloud computing
– Hadoop

Nobody Understands the Cloud

• Cloud computing
– Cloud computing allows you to use computers you don’t own to operate
programs you use. Gmail, anything from Google. You can purchase access to
warehouses of computers via cloud providers like Amazon, Google, and
Microsoft. This allows you to run tools like Hadoop to process large amounts
of data.
Process the Data: Cloud
Intel has launched Collaborative Cancer Cloud, a new service to
enable providers and researchers to securely share genomic,
imaging and clinical data among participating organizations
across the globe.
By 2020, the goal is to have physicians be able to give a patient
a diagnosis and generate a specific treatment plan within 24
hours. Over time, the platform will be modified to support
other types of research and treatment.

Process the Data: Machine Learning
& Predictive Analytics
“For the past 10 years, we have been working
on that area,” Ebadollahi said. “We have very
advanced machine learning, pattern
recognition, on imaging and video in general,
most especially in medical imaging. Now, this
intent to acquire Merge will bring a conduit to
attach those technologies coming out of our
research.”

Analyzing & Presenting the Data
• How to make the data tell a story?
–Excel
–PowerPoint
–GIS
–Tableau
–JavaScript
–D3

Analyzing & Presenting: Excel
• Pivot Tables
• Macros
• Cell Links
• V‐Lookup or Index Match
• Format Painting
• Custom Sorts

Analyzing & Presenting:
PowerPoint
• Custom color palate and template with logo
• Graphs, graphs, graphs
• Add maps and photos
• Tell a story

Analyzing & Presenting: Tableau
• Business analytics software
• Business dashboards
• Big data analysis
• Data discovery
• Social media analytics
“We are looking to move our market share reporting to Tableau within
the year, as the level of detail we’re being asked to report on has
grown beyond Excel’s capacities.… It’ll increase automation and
decrease errors on our part.”
‐Stratasan customer

Analyzing & Presenting: JavaScript
• JavaScript ‐ This programming language is all about
presentation layer (charts, graphics, and user interaction). It is
the glue that holds Internet together. Every modern browser
runs Javascript.
• D3 ‐ D3.js is a powerful JavaScript library for producing
dynamic, interactive data visualizations in web browsers.

Analyzing & Presenting: GIS
• GIS – A Geographic Information System enables you to
envision the geographic aspects of a body of data. This lets us
visualize, question, analyze, and interpret data to understand
relationships, patterns, and trends. (Esri) Used primarily in
government, conservation, zoning and construction.
– Esri ArcGIS
• Very granular demographic data – example patient origin by
block group, demographics by block group

38
C A R D I O L O G Y P RO J E C T E D
B L O C K G RO U P VO L U M E
C A R D I O L O G Y
B L O C K G RO U P S C O R E C A R D
Block Groups outlined in green are considered the best targets
Block Groups grayed out do not have the desired tapestries

Brentwood Emergency Patient Origin by Block Group
39
B R E N T WO O D M E D I C A L C E N T E R
E M E R G E N C Y PAT I E N T O R I G I N B Y B L O C K G RO U P

Analyzing & Presenting:
Tapestry Segmentation
• ESRI Tapestry data – Tapestry segmentation provides an
accurate, detailed description of America's neighborhoods—
U.S. residential areas are divided into 67 distinctive segments
based on their socioeconomic and demographic
composition—then further classifies the segments into
LifeMode and Urbanization Groups. Tapestry Segmentation is
used to target your population with specific messages that are
meaningful to the specific population.

Northern Block Groups are zoomed in the next map
41
D O M I N A N T TA P E S T R Y S E G M E N TAT I O N
B L O C K G R O U P
• The Tapestry
Segmentation and
LifeMode (Psychographic
Profile) for each Block
Group is represented by
a Number & Letter
combination
• This ID helps guide your
marketing execution plan
D O M I N A N T TA P E S T RY S E G M E N TAT I O N
B L O C K G RO U P

42 Source: Stratasan & LifePoint Health, 2015; esri.com

Resources to Learn More
• Coursera
– https://www.coursera.org/
• Python website
– https://www.python.org/about/
• YouTube
– https://www.youtube.com/watch?v=puS8Tu3JPnU
– https://www.youtube.com/watch?v=GZpwGt0hzKs
•

Case Study:
Physician Referrals
47

Case Study: Physician Referrals
• Situation: A data set that had not been
previously utilized by the organization
was introduced
• Outcome: The organization reacted to
optimize the use of the information
through an entirely new set of
processes and a change in
organizational structure
• Next comes the “How”…

Data Science
• Step 1 ‐ Look at the data:
– Source NPI Numbers
– Destination NPI Numbers
– Shared Patients
• Step 2 – Process the Data (Connecting the Dots):
– Data will give information about the relationships
between physicians.
– Enough organizational savvy to know who could use the
information (Physician Sales Team) – engage them on
the discovery phase.
– Identify other sources of information that will help to
add context

Data Science
• Step 3 ‐ Present the Data:
– Nicely formatted Excel table
– Map
– Infographic

Medicare Physician Referral
Databases
• https://questions.cms.gov/faq.php?faqId=7977
• Files are 1‐7 gigabytes
• 30 day referrals 2.4 gigabytes
• Smallish data; too big for Excel

Physician Network Intelligence
Provider 1
The patient volume when two providers
bill Medicare for the same patient within
30 days of each other.
Provider 2
52 Source: Stratasan & LifePoint Health, 2015; Medicare Referral Database (2014)

TOTA L V I S I T S B Y Z I P C O D E
G R E AT E R H E A LT H S Y S T E M M A R K E T S H A R E
Source(s): Stratasan (2014); Esri (2014); Medicare Referral Database (2014)53

2014 Medicare Physician to Physician Network Summary -
Primary Care Doctors to Any Orthopods
James Kessler William Miller
Lawrence
Supik
Martin
Senicki
Ryan
Slechta
William
Handley
James Kessler
(Angel)
William Miller
(Haywood)
Employed Total 27% 12% 15% 34% 88% 7% 5%
Anthony Esterwood 0% 0% 23% 78% 100% 0% 0%
Beth Bailey 46% 25% 0% 30% 100% 0% 0%
Elizabeth Dixon 44% 36% 20% 0% 100% 0% 0%
Ewa Susfal 0% 0% 17% 60% 77% 23% 0%
Lee Ann Manthorne 58% 42% 0% 0% 100% 0% 0%
Randall Provost 60% 25% 15% 0% 100% 0% 0%
Steven Queen 58% 18% 0% 0% 76% 0% 24%
Todd Davis 16% 0% 18% 45% 79% 9% 11%
Private Total 27% 42% 17% 4% 90% 0% 10%
Matthew Mahar 29% 35% 8% 0% 73% 0% 27%
Ofelia Balta 45% 24% 21% 10% 100% 0% 0%
Roy Gallinger 34% 27% 16% 0% 77% 0% 23%
Thomas Wolf 27% 44% 19% 11% 100% 0% 0%
Grand Total 27% 23% 15% 24% 89% 4% 7%
PCP Status/Name
Employed Orthopods
Employed
Total
Sources: 1. CMS Physician Referral Patterns 2013 - 2014 30 day interval: https://questions.cms.gov/faq.php?faqId=7977
2. NPI Monthly File; http://nppes.viva-it.com/NPI_Files.html
3. Physician Compare Downloadable Database; https://data.medicare.gov/data/physician-compare
Targeted SalesVisit
initiated with Dr.
Susfal to understand
reason for leakage

Results
• LifePoint has reduced referrals to Dr. Kessler
from Dr. Susfal 25%.

Conclusions

Conclusions
• Use data to tell a story and make better decisions
• Learn new things all the time
• Search for answers
• Focus on the important; don’t major in the
minors
• Acknowledge you can’t know everything, but
recognize what you don’t know

Python What? The Strategist as Data Geek

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