Nathan is a Data Scientist with years of experience in healthcare analytics. With an in-depth knowledge of healthcare informatics and health insurance, he also understands how to optimize legacy processes that link the old world to the new - Nathan is extremely savvy when it comes to applying machine learning to healthcare.
Like any good Data Scientist, Nathan uses data to complete previously impossible tasks. For example, he builds models that predict which patients are likely to fall after their care ends, or might develop infections, allowing Symphony to head off complications before they happen. Nathan’s deep insights help Symphony provide the best level of care to their patients while reducing overall healthcare costs.
Prior to DataRobot, the machines were holding Nathan back.
Manually building predictive models was time-consuming, resulting in persistent issues, such as:
This manual process seemed flawed to Nathan. Surely these problems could be solved with technology?
|Traits & Capabilities|
|Traits & Capabilities||Data Scientist Mortal||Data Scientist Superhero|
|Bottom Line Business Impact|
|Bottom Line Business Impact||$100K+ per year||$M's+ / Year|
|Model Engine Tested|
|Model Engine Tested||No||200,000,000+ times|
|Standardization||When there's time||Every time|
|Multitasking||Single model||Multiple models|
|Package installation and dependency management|
|Package installation and dependency management||Manual||Not needed|
|Risk of coding errors|
|Risk of coding errors||Substantial||None|
|Popularity with business users|
|Popularity with business users||Somewhat||Very|
|Business user engagement|
|Business user engagement||Too busy||Always available|
|Helped with guardrails|
|Helped with guardrails||None||Yes|
|Skills Needed||Significant - PhD-level||PhD not required|
|Model deployment effort|
|Model deployment effort||Significant||Effortless|
|Model iteration||Manual & slow||Automated|
|Helped by other Data Scientists|
|Helped by other Data Scientists||No||85+|
|Algorithms and libraries|
|Algorithms and libraries||A comfortable few||Many|
|Number of models / month|
|Number of models / month||Very Few||Thousands|
|Model Transparency||None||X-Ray Vision|
|Explainable Models||'Black Box'||Automatic|
After seeing a DataRobot demo, Nathan realized he found the answer to his technology challenges. Seamless integration with Alteryx, and the ability to connect to historical data in lots of different ways, removed any doubt and sealed the deal. He became an official DataRobot user in December 2016 — and by pushing productivity through the roof, he became a Data Science Superhero!
The DataRobot machine learning automation platform captures the knowledge, experience and best practices of the world’s leading data scientists to deliver unmatched levels of automation and ease-of-use for Symphony’s machine learning initiatives. DataRobot enables Nathan to build and deploy highly accurate machine learning models in a fraction of the time he spent with traditional, manual modeling methods.
like lowering malpractice insurance rates to help lower healthcare costs and helping to prevent opioid addictions for patients who require painkillers. By delivering better predictions for Symphony, Nathan is impacting the company’s bottom line performance — and boosting his reputation in the process. Best of all, with DataRobot, Nathan has achieved his personal goal of leveraging machine learning to solve problems that affect real people and improve their lives!
“DataRobot’s platform makes my work exciting, my job fun, and the results more accurate and timely — it’s almost like magic.”