This document summarizes a statistician's experience working at a healthcare technology startup that uses electronic health record data. It describes how the company initially had just one quantitative scientist but grew its team to include 70 software engineers and 10 quantitative scientists. It discusses how the company cultivated an R culture through internal packages, training, and hiring. It provides examples of when the company uses R for prototyping but implements in other languages for production, when R is used as a long-term solution, and when R and other languages are used in parallel for analysis.

1. A Statistician Walks into a Tech Company
R at a rapidly scaling healthcare technology startup
Sandy Griffith
Twitter: @sgrifter
sgriffith@flatiron.com
www.flatiron.com

2. My story
Academic biostatistics
© 2016 Flatiron Health, Inc. Proprietary and confidential.

3. My story
3
Academic biostatistics Healthcare tech

4. © 2016 Flatiron Health, Inc. Proprietary and confidential. 4
Flatiron’s mission is to serve cancer patients and our
partners by dramatically improving treatment and
accelerating research.
Our Mission

5. Flatiron Processes EHR Data At Scale
© 2016 Flatiron Health, Inc. Proprietary and confidential. 5
Research-
Grade Data
Demographics
Diagnosis
Visits
Labs
e-Prescribing
Pathology
Report
Discharge
Notes
Radiology
Report
Physician
Notes
Electronic Health
Record
Structured Data Unstructured Data Outside
Practice
Hospital
Lab
Structured Data
Processing
Unstructured
Data
Processing
Standard EHR Data

6. Rapidly Scaling
January 2015
Flatiron: ~140
Software Engineers: ~50
Quantitative Sciences team: 1
6© 2016 Flatiron Health, Inc. Proprietary and confidential.

7. Now: We are a team of 262
7
We include…
All Flatiron data and tools are collaboratively built, implemented and maintained by a
cross-disciplinary team that includes oncology, engineering, and quantitative sciences
We come from…
9 Medical oncologists and nurses
70 Software engineers
10 Quantitative scientists
5 Medical informaticists
+ more!
© 2016 Flatiron Health, Inc. Proprietary and confidential.

8. Primary Language: time of hire
© 2015 Flatiron Health, Inc. Proprietary and confidential. 8© 2016 Flatiron Health, Inc. Proprietary and confidential.

9. Proficiency with R: time of hire
9© 2016 Flatiron Health, Inc. Proprietary and confidential.

10. A decision point early on
10© 2016 Flatiron Health, Inc. Proprietary and confidential.

11. A decision point early on
11© 2016 Flatiron Health, Inc. Proprietary and confidential.

12. Cultivate R culture
1. Internal R Package
2. User group
3. Slack channel
4. Trainings
5. Hiring
12© 2016 Flatiron Health, Inc. Proprietary and confidential.

13. Cultivate R culture
1. Internal R Package
2. User group
3. Slack channel
4. Trainings
5. Hiring
13© 2016 Flatiron Health, Inc. Proprietary and confidential.

14. Proficiency with R
14© 2016 Flatiron Health, Inc. Proprietary and confidential.
Time of hire Now

15. Now we have R users, but when should we use R?
Three scenarios:
1. R for prototyping → !R in production
2. R as a long-term solution
3. R and !R in parallel
15© 2016 Flatiron Health, Inc. Proprietary and confidential.

16. R for prototyping → !R in production
16© 2016 Flatiron Health, Inc. Proprietary and confidential.
Prototype
● One-time linkage
● Small cohort (10s of thousands)
● RecordLinkage R package
● Probabilistic linkage method using
EM algorithm
Production
● Repeated daily at scale
● Large cohort (~5 million patients)
● Code maintained by different team
● Deterministic logic in SQL
Example: Linking external mortality data

17. R for prototyping → !R in production
Why this made sense:
● Stable method -- No longer needed rapid iteration
● Tuning parameters
● Similar performance, more transparency
● No R users on team that would be maintaining code
17© 2016 Flatiron Health, Inc. Proprietary and confidential.
Example: Linking external mortality data

18. R as a long-term solution
Early version (Jan 2015)
18© 2016 Flatiron Health, Inc. Proprietary and confidential.
● bash commands for extracting data
run from R script using ETL tool
● R script run via command line
● parameters in metafiles manually
updated
● Runs a series of Rmd files and
renders HTML output
Current Version (April 2016)
Example: Rmarkdown QA report
● linked to data pipeline maintained
by software engineering
● metafile generated dynamically
● Plotly survival curves
● Flatly bootstrap theme
● Plan to continue using R
indefinitely

19. R as a long-term solution
19© 2016 Flatiron Health, Inc. Proprietary and confidential.
Example: Rmarkdown QA report
Why this made sense:
● Mature product and team
● Quantitative science members remain embedded in team
● Strong support and collaboration with software engineering
● Requirements are dynamic -- continued need for rapid
prototyping

20. R and !R in parallel
● Specific research questions
● 2 people code independently in Python/SQL and R
● Compare results
● Language sometimes incidental, more about 2 different perspectives
Why this made sense:
● High stakes or low error tolerance
● Complicated concepts
● Custom projects often involve novel problems
20© 2016 Flatiron Health, Inc. Proprietary and confidential.
Example: Some external collaborations

21. Thank you
● Melissa Curtis
● Josh Kraut
● Kathi Seidl-Rathkopf
● Cindy Revol
● Rachael Sorg
● Jay Rughani
21© 2016 Flatiron Health, Inc. Proprietary and confidential.
● Paul You
● Aracelis Torres
● Alphan Kirayoglu
● Ben Birnbaum
● Ann Jaskiw
● James Gippetti
Join our Team!
Drop me a note at sgriffith@flatiron.com, @sgrifter,
or visit flatiron.com/careers