Data Science

From NASA to Health IT—Meet a Data Scientist

As a data scientist, I don’t expect to walk on the moon anytime soon. But when the Neil Armstrong biopic hit theaters, I couldn't help but draw some comparisons between the first lunar landing and the progress of health IT in the digital age.

And I actually have a bit of aviation street cred to make a connection like this. I used to be part of an engineering team that programmed the navigational backup system for the U.S. Air Force's F-16 Fighting Falcon aircraft, and I also worked on a NASA project interpreting encrypted satellite data to prevent spacecraft communication failure.

When I transitioned to healthcare and applied my engineering and big data experience to the industry, a new world of opportunity opened for me. And I feel very fortunate to lead a team that has spearheaded some of the most innovative population health management (PHM) and value-based care technology in today’s health IT marketplace. For example, our customers now have the ability to:

  • View longitudinal patient records, care coordination strategies and performance reporting solutions on a dashboard display to improve patient outcomes, lower costs and grow revenue
  • Analyze quality by physician, practice and organization to drive clinical best practices and cost-management strategies
  • Revolutionize how healthcare for Medicare and Medicaid populations is delivered, managed and monitored

The ability for PHM solutions to extract all available healthcare information from any system, in any care setting, and in any format, was simply unheard of just five years ago. For example, pulling and homogenizing data from multiple and redundant EHRs has been a tremendous hurdle for healthcare providers to overcome. But we’re now able to do this thanks to an industry dedicated to applying informatics to spur innovation.


After NASA, I continued working with satellites and helped develop solutions that could store, normalize and graph telemetry data, as well as create alerts from the information. From there, I moved into healthcare as part of a team that identified utilization trends in hospitals and projected supply requirements using algorithms.

HIMSS gave me a broader view of the healthcare market, the different types of players in it, and the sheer number of participants in the space. After attending a few events, I quickly realized two things: HealthEC could help the vast majority of the industry with data aggregation and analytics, and people were truly interested in working together to develop IT solutions.

Healthcare Data Scientists Face New Challenges

Second generation PHM technology—along with other innovations in the health IT space—may not be pushing the limits of space travel, but we are certainly pushing the limits of data aggregation and analytics as more healthcare organizations transition to value-based reimbursement.

And just like the space program faced incredible challenges before the first astronauts landed on the moon, significant obstacles remain for health IT. How do we tap data from insurance companies, pharmacies, hospitals, labs and providers that is not easily accessible (i.e., stored in blocks of text on a physician’s computer, PDFs, etc.) and incorporate this information into data models? How do we overcome inaccurate data about a person?  How do we incorporate social determinants of health? How can we assemble entire medical records of patients when unique markers like Social Security numbers can’t be used to identify them?

Data scientists are diligently working on addressing these challenges as well as identifying how patterns shift when certain data elements are added or removed from data models. And with the help of evolving artificial intelligence solutions, technicians can analyze data models - with 20 or 2,000 data points - in ways that are likely to transform the healthcare landscape and the delivery of care within the next few years.

We’re not walking on the moon yet when it comes to healthcare data aggregation and analytics, but the Eagle has certainly landed.

The views and opinions expressed in this content or by commenters are those of the author and do not necessarily reflect the official policy or position of HIMSS or its affiliates.

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Updated April 27, 2020