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A Statistician Walks into a Tech Company: R at a Rapidly Scaling Healthcare Startup
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.
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A Statistician Walks into a Tech Company: R at a Rapidly Scaling Healthcare Startup
- 1. A Statistician Walksinto 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 biostatisticsHealthcare tech
- 4. © 2016 FlatironHealth, 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 EHRData 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 area 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: timeof 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 pointearly on 10© 2016 Flatiron Health, Inc. Proprietary and confidential.
- 11. A decision pointearly 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 haveR 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 along-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 along-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 !Rin 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 ● MelissaCurtis ● 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