As CTO, Miguel will be responsible for “all things technology” at Lumiata –– Engineering, Data Science and Infrastructure. With adoption of Lumiata Health Analytics accelerating, our technology stack and infrastructure must scale to ensure our customers continue to rely on Lumiata products for their critical operations.
As I got to know Miguel these past few weeks, his two leadership principles stood out for me. One, he is passionate about building high-performance and motivated teams. Two, he believes we must build technology that is inspired by what our customers want (not the other way around). These core principles will guide us as we build and scale enterprise-grade healthcare products and infrastructure to serve our customers.
Leaders have a foundational role to play in both creating products, as well as nurturing a company’s values and culture. I look forward to partnering with Miguel in building a company we’re proud of, and one that our customers love to work with.
A number of industries are effectively leveraging data science to realize greater efficiency and gain a number of benefits. When it comes to the healthcare industry, here are three advantages of using data science approaches:
1. Reliability, Verifiability, and Openness to Experiments
Much of the healthcare industry relies on the knowledge and experience of clinicians, administrators, and underwriters. Along with the insights and creativity of people, there is also the possibility of human error and inconsistency. Data-based approaches can be built to produce similar results under similar conditions, reliably, and importantly can be tested on millions of patients to verify performance—this also allows a fast cycle of experimenting with new methods.
2. Models Reveal Biases
Data science models are designed to identify complex patterns that can predict health outcomes or cost with minimal human intervention. In addition to improving predictions, such models can illuminate how traditional approaches might be biased. Are care programs selectively benefitting certain population groups better than others? Are certain patients not being reached? These questions can be as important as overall program effectiveness.
3. Predictions Often Improve with More Data
Perhaps the most exciting benefit of using machine learning-based models is that they can improve with more data, assuming the model has enough free parameters to discover new patterns and that the new data is sufficiently different from previous datasets. Much of the work in data science is in tuning models such that the number of features is high enough to capture important effects in the data, without introducing so many that the model overfits to the training set. In our experience, after model tuning, adding millions of new medical health records has significantly improved our predictive performance.
Stay tuned, as we take a closer look at each of these advantages and explore how healthcare can benefit from data science.
By: Charles Greenberg, Senior Data Scientist, Lumiata
Lumiata's core is constructed with data science, as we build models for cost and disease prediction. This year, we've welcomed new members to our data science team with a shared dedication to use deep learning and machine learning to innovate healthcare. We're honored to introduce them.
I was born in Arizona, and moved to Israel when I was still a baby. At 19 I graduated with a BSc from Tel Aviv University and moved back to the US to do a PhD in Mathematics at the University of Pennsylvania. I’ve spent the years before coming to Lumiata as a mathematician, having done research as faculty at three different universities: Penn State, Stanford University, and the University of Maryland. I’m excited about applying my knowledge and skills to improving the US healthcare system.
I have a background in Physics and Astronomy, with my PhD research on the evolution of distant galaxies. I decided to switch to data science in the tech industry, hoping to use machine learning and my quantitative skills to make direct impact to the society. I feel privileged to work in Lumiata, where I can not only work on advanced technology, but also solve important problems to improve healthcare and the quality of lives.
I recently graduated from the University of San Francisco with a Masters degree in Data Science. In addition to studying contemporary computation and analytical techniques, I worked with the LA County Registrar, analyzing county wide voting behaviour. It's been exciting to apply my experience to the health domain with Lumiata.
I joined Lumiata as a Data Scientist in May 2018. I have my Master’s degree in Biotechnology and four years of experience working in biopharmaceutical industry. I completed Graduate certificate program in Data science from Harvard University and I joined Galvanize Data Science immersive program to strengthen my skills to drive business decisions by leveraging data. I am passionate about using machine learning techniques to get actionable insights from big data to improve healthcare system. I strongly believe in Lumiata’s mission as data science has the potential to revolutionize healthcare.
I graduated from UC Berkeley in May, 2018 with a degree in Statistics and Computer Science and began working here at Lumiata a month later. During my time at Berkeley I participated in several Data Science internships and research projects that helped bolster my skills as a Data Scientist. I am excited to be working here with the mission to help people across the country by making healthcare more affordable.