This month’s episode of CTO Talk is with my guest Heidi Dohse. Heidi’s story is nothing short of amazing. She’s an endurance athlete who clocks 200 miles on her mountain bike in a single race. She’s a professional heart patient whose first EKG clocked her heart at 270 beats per minute, and who has had open heart surgery and seven pace makers. And she’s the Senior Program Manager for Healthcare and Life Sciences for Google Cloud where she’s pouring her passion for data and healthcare into the Google Cloud Platform.
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Like Google, Heidi is focused on data. Growing up she was participating in sports of all kinds – swimming, windsurfing, skiing, soccer, you name it. In this episode she shares her interesting story of discovering that while commentators always say athletes like her have a lot of heart, Heidi discovered that hers was seriously flawed. She’ll share how learning key data points about her health were significantly more helpful than the age-old paradigm where we tell a doctor how we feel.
Her story is real and it’s also symbolic of a huge shift in healthcare to more patient-centered data and personalized care. By understanding the risks and rewards of our genetics and behaviors, as well as how are bodies are reacting to them, we can be in a better position to help drive healthier outcomes for ourselves. In doing so, we also build a volume of baseline information that can help with predictions, prescriptions and preventions. Data that can be accessed and understood can help us drive better decisions rather than us waiting until we feel bad long enough (with no data) that we run ourselves into a troubling position that requires more extensive and expensive healthcare.
In addition, patients armed with personal health data can help drive innovation. We’ll dive into this topic including some of the work Heidi is doing with FitBit and Flex Digital Health, and how just the data from your FitBit can change and improve the conversations you are having with your physician.
In this episode, Heidi and I expand on what happens when we in healthcare make data interoperable, and how that empowers us to actually find meaning and glean insights from it. No one is doing this at the speed and scale of Google. While sometimes spoken of as if a thing of legend, Google Scale is very real and core to the operating principles of Google. Google is built on a model of ingesting the worlds’ data, organizing it and then making it useful. To do that they had to invent tools like Tensorflow, that they are now bringing to market so we can all benefit from it. We’ll also talk about using models for testing as with the pharma sector. We used to have a problem with not having enough big data sets, but with the tools we have with machine learning we do. Big Query is processing a petabyte of data in a couple of minutes. It’s exciting seeing healthcare data expand, so we can run tests and build models from enough data that it becomes relevant.
For those in data science, I highly recommend you check out machine learning on the Google Cloud. You don’t have to be a software developer to use ML at Google Cloud, it’s as simple as dragging and dropping. You can write a SQL query and you can get to outcomes. It’s a major shift for healthcare as we all work to glean insights from data.
Heidi offers some great tips and resources to help you either advance your work in the cloud, or understand how to get started. IoT core, how to connect all this information from connected devices, use cases, best practices, she’ll share where to find everything you need to know to help advance your organization in the cloud.
I always enjoy visiting with Heidi. Her energy and enthusiasm for changing healthcare is inspiring. She is an endurance athlete, a professional heart patient and an innovator in the healthcare cloud.
About the Show
On CTO Talk, technology expert Matt Ferrari discusses the issues, challenges, and opportunities transforming healthcare technology today, all from the CTO point of view. Tune in because when CTOs talk, health IT listens.
This article was originally published on ClearDATA and is republished here with permission.