How Data Science is Transforming the Healthcare Industry

Curren Katz, Senior Director for Data Science & Project Management at Johnson & Johnson, discusses how the healthcare industry presents a set of unique challenges for data science, including how to manage and work with sensitive patient informa

Jun 2022

Transcript

Guest

Curren Katz

Host

Adel Nehme

Key Takeaways

Despite its unique challenges, the healthcare industry is adopting data science at scale to drive core business decisions and solve problems in diagnostics, operations, clinical trials, patient care, and more.

Empathy is a vital expertise for data scientists in healthcare so they can accurately identify, assess, and mitigate biases in technology and algorithms before they affect patients.

Alignment on a shared vision and increasing collaboration and communication between departments is key to succeeding at in large matrixed organizations.

Key Quotes

Data literacy goes both ways in an organization. Data scientists need business literacy to understand how a clinician is inputting data and how they're interacting with an EMR system, or how on the insurance side, a care manager is identifying and reaching out to insured patients to help them coordinate their care and manage a chronic disease. Data scientists have to understand how that data comes in. Conversely, if data scientists show the value of the data to those delivering care, that part of the healthcare ecosystem is going to see the value and be able to work with them.

I'm really excited about the capabilities that are evolving around fairness, both being able to detect bias in the algorithm, and fixing that on the fly and at scale. It will empower data science, AI, and machine learning in healthcare, and it brings value to patients because we can make sure they're getting quality care that is fair. We're considering things that maybe we haven't been great at in the past and maybe this can make medicine, or any field within it, better.

Transcript

Adel Nehme: Hello everyone. This is Adel data science educator and the evangelist at data camp two years into the pandemic. The potential for data science and machine learning and healthcare has never been more apparent. Whether it's drug discovery, acceleration, operational innovation, virtual assistance, and disease prevention.

The margin of opportunity for data science and healthcare is massive. However that doesn't come without its own set of unique challenges and risks that require unique solution. This is why I'm excited to have current cats on today's episode of data. Framed current is a senior director for data science, portfolio management at Johnson and Johnson.

She has decades of experience at the intersection of healthcare and data science, and is deeply attuned to the state of data science and healthcare. Today throughout our conversation, we discuss where the landscape of data science and healthcare is. The unique challenges of applying data science and healthcare, the importance of ethical AI when working on healthcare use cases, how to solve some of the data challenges of the healthcare industry use cases, she's been excited about how data science was used to tackle COVID-19 and much more.

If you enjoyed this podcast, make sure to rate us and subscribe and add a comment, but only if you enjoyed it now let's dive right. Current. It's great to have you on the show.

Curren Katz: Yeah. Great to be here. Thank you for having me.

Adel Nehme: I'm excited to talk to you about data science and machine learning in healthcare, your... See more

experience leading data teams and complex organizations and how you've led R and D Johnson Johnson.

But before I'd love to learn more about your background and what got you into the data space.

Curren Katz: Yeah, absolutely. So I guess like, most people I've always loved data and my first statistics courses, I started to think, oh, this could be really, really fun. And especially when I started applying it to data, I had collected as a researcher. It was pretty addictive. And then as I moved along in my career, I'm a cognitive neuroscientist by training, but did SMRI research as well as looking at some large epidemiology datasets and 20 years ago wrote a paper on predictors of suicide.

Not exactly an AIML approach to it, but that interest in of like how can we predict some event? And then I had been in neuroscience studying neural networks, all of these things and applying actually machine learning techniques to FSRI images, which are images while someone's doing something. So it's a fairly complex, although clean dataset got me really excited, and then I've always been passionate about healthcare and solving problems in healthcare.

And my first corporate data science job was at Highmark health. So I started on the payer side, building a bunch of models and seeing how those models impacted care and was hooked. Move to the parent company. It's an integrated healthcare system. Second largest integrated payer provider system in the U S and started a data science department at that parent company, looking at the payer, the insurance side, the provider side, and a few other diversified healthcare businesses, and then came to Johnson and Johnson where I am now.

And it's been a really exciting career where I get to see a lot of impact from
day-to-day.

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