#90 How Data Science is Transforming the Healthcare Industry (with Curren Katz)

00:35:39
https://www.youtube.com/watch?v=eI6_nRFOBEI

摘要

TLDRThis episode of the "Data Framed" podcast features Karen Katzenbach from Johnson & Johnson discussing the transformative potential of data science and machine learning in healthcare, especially post-pandemic. She emphasizes the scalability of data solutions, ethical AI use, and handling the industry's operational challenges. Karen highlights the importance of ethical considerations in AI applications and interdisciplinary collaboration to ensure efficacy and fairness in outcomes. Successful use cases include optimizing patient care through better scheduling and enhancing diagnostics for early disease detection. She calls for overcoming data challenges like system interoperability and emphasizes the vital role of data literacy and transparency in using data responsibly within healthcare environments. She also talks about her experiences at J&J, sharing insights into executing significant projects like COVID-19 vaccine research efficiently using predictive modeling to target clinical trials effectively. Karen underlines the importance of understanding user needs in developing solutions and advocates for innovative approaches that include partnerships across different sectors.

心得

  • 🎯 Applying data science can solve crucial healthcare challenges effectively.
  • 🤝 Interdisciplinary collaboration is key to healthcare solutions.
  • 💡 Ethical AI considerations are necessary for effective healthcare applications.
  • 🔍 Diagnostic improvements and operations can benefit from AI.
  • 📈 Scalability in data solutions enhances healthcare impact.
  • ⚙️ Data interoperability poses challenges but can be overcome with innovative solutions.
  • ☑️ Data literacy is essential for successful data science use.
  • 🔗 Cross-industry learning boosts healthcare applications.
  • 📊 Predictive modeling can optimize healthcare trials.
  • 🌟 Focus on patient-centric outcomes for impactful health solutions.

时间轴

  • 00:00:00 - 00:05:00

    Introduction to the podcast, highlighting its focus on data science trends and insights within healthcare, hosted by a data science educator and evangelist. The episode guest, Curran Katz from Johnson & Johnson, has extensive experience in healthcare data science.

  • 00:05:00 - 00:10:00

    Curran shares her background, career journey, and how she became engaged in the data science field. Her journey moved from cognitive neuroscience to roles in healthcare companies, eventually becoming a leader in data science innovation within large organizations.

  • 00:10:00 - 00:15:00

    Discussion on the current landscape of data science in healthcare, noting its slower evolution compared to other industries due to unique challenges and risks. However, there's a growing acceptance and deployment of data science models to drive business decisions.

  • 00:15:00 - 00:20:00

    Curran emphasizes the operational impact of data science in healthcare, such as improving patient scheduling for chemotherapy treatments, and the importance of operational efficiency in creating immediate impacts on patient experiences.

  • 00:20:00 - 00:25:00

    Exploration of main challenges in healthcare data science, such as data access, ethics, and sensitivity. Curran explains the importance of addressing ethical concerns and biases when using AI and machine learning, emphasizing collaborative, cross-functional approaches.

  • 00:25:00 - 00:30:00

    Curran discusses the importance of empathy and human-centered design in developing AI healthcare solutions. She highlights the need to focus on use cases that minimize harm and improve clinician decision-making rather than replace it.

  • 00:30:00 - 00:35:39

    Curran shares insights into managing data science teams within Johnson & Johnson, focusing on aligning with long-term goals and patient impact. She talks about leveraging resources within large organizations for developing data science solutions that enhance healthcare processes.

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思维导图

Mind Map

常见问题

  • What are operational challenges in healthcare?

    It refers to using data science to optimize backend processes, such as scheduling, to enhance healthcare efficiency and patient experience.

  • How has the pandemic affected data science in healthcare?

    The pandemic has highlighted opportunities for using data science in drug discovery, operational innovation, disease prevention, and more.

  • What trends has Karen noticed in data science and healthcare lately?

    She notes scalable implementation, ethical AI use, and expanding capabilities in diagnostics and operations.

  • How was data science used in J&J's COVID-19 vaccine trials?

    Using data science helps predict clinical trial locations where COVID-19 cases are rising, speeding up research processes.

  • How can healthcare overcome data challenges?

    Multiple departments must work together, understand patient data interactions, and embrace innovative methods from other industries.

  • What is necessary for a successful data science project in healthcare?

    A well-understood problem, collaborative implementation, and engagement from solution users are essential for success.

  • How does J&J ensure effective use of data science in their R&D department?

    Interdisciplinary collaboration and focusing on unmet needs were key approaches.

  • How does Karen manage short-term wins and long-term projects in data science?

    Finding clear problems that data science can address and creating timelines that align with business priorities.

  • What excites Karen about fairness in data science in healthcare?

    It's about understanding and mitigating model biases while making real-time adjustments to ensure fair healthcare outcomes.

  • What main advice does Karen give to data science professionals?

    She loves using data science to solve impactful healthcare issues and emphasizes the need for ethical consideration.

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  • 00:00:01
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    hello everyone this is adele data
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    science educator and evangelist at
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    two years into the pandemic the
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    potential for data science and machine
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    learning in health care has never been
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    more apparent
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    whether it's drug discovery acceleration
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    operational innovation virtual
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    assistance and disease prevention the
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    margin of opportunity for data science
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    and health care is massive
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    however that doesn't come without its
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    own set of unique challenges and risks
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    that require unique solutions
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    this is why i'm excited to have current
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    cats on today's episode of data framed
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    curran is a senior director for data
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    science portfolio management at johnson
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    johnson she has decades of experience at
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    the intersection of healthcare and data
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    science and is deeply attuned to the
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    state of data science and healthcare
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    today
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    throughout our conversation we discuss
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    where the landscape of data science and
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    healthcare is today the unique
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    challenges of applying data science and
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    healthcare the importance of ethical ai
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    when working on healthcare use cases how
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    to solve some of the data challenges of
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    the healthcare industry use cases she's
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    been excited about how data science was
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    used to tackle covet 19 and much more if
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    you enjoyed this podcast make sure to
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    rate us and subscribe and add a comment
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    but only if you enjoyed it now let's
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    dive right in
  • 00:01:47
    karen it's great to have you on the show
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    yeah great to be here thank you for
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    having me i'm excited to talk to you
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    about data science and machine learning
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    and healthcare your experience leading
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    data teams and complex organizations and
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    how you've led r d at johnson johnson
  • 00:02:01
    but before i'd love to learn more about
  • 00:02:02
    your background and what got you into
  • 00:02:04
    the data space yeah absolutely
  • 00:02:07
    so
  • 00:02:08
    i guess like most people i've always
  • 00:02:10
    loved data and my first statistics
  • 00:02:13
    courses i started to think oh this could
  • 00:02:15
    be really really fun and especially when
  • 00:02:17
    i started applying it to data i had
  • 00:02:19
    collected as a research assistant it was
  • 00:02:22
    pretty addictive
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    and then as i moved along in my career
  • 00:02:26
    i'm a cognitive neuroscientist by
  • 00:02:27
    training but did fmri research as well
  • 00:02:30
    as looking at some like large
  • 00:02:33
    epidemiology data sets and
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    20 years ago wrote a paper on predictors
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    of suicide attempts not exactly an ai ml
  • 00:02:40
    approach to it but that interest in like
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    how can we predict some event and then i
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    had been in neuroscience studying neural
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    networks all of these things and
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    applying
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    actually machine learning techniques to
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    fmri images which are images while
  • 00:02:57
    someone's doing something so it's a
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    fairly complex although clean data set
  • 00:03:02
    got me really excited and then i've
  • 00:03:03
    always been passionate about healthcare
  • 00:03:05
    and solving problems in healthcare
  • 00:03:07
    and my first corporate data science job
  • 00:03:11
    was at highmark health so i started on
  • 00:03:13
    the payer side building a bunch of
  • 00:03:16
    models and seeing how those models
  • 00:03:18
    impacted care and was hooked and then
  • 00:03:20
    moved to the parent company it's an
  • 00:03:22
    integrated health care system second
  • 00:03:24
    largest integrated payer provider system
  • 00:03:26
    in the u.s
  • 00:03:27
    and started a data science department at
  • 00:03:30
    that parent company looking at the payer
  • 00:03:32
    the insurance side the provider side and
  • 00:03:34
    a few other diversified healthcare
  • 00:03:35
    businesses and then came to johnson
  • 00:03:38
    johnson where i am now and it's been a
  • 00:03:40
    really exciting career where i get to
  • 00:03:42
    see a lot of impact from data science to
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    start off our conversation i'd love to
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    understand the current state of data
  • 00:03:48
    science machine learning in healthcare
  • 00:03:50
    early in my career about five years ago
  • 00:03:52
    and that's not too long ago healthcare
  • 00:03:54
    was often and still is talked about as
  • 00:03:57
    an industry with a large margin of
  • 00:03:59
    opportunity for data science but it
  • 00:04:00
    comes with its own unique sets of
  • 00:04:02
    challenges which makes it slower in
  • 00:04:04
    comparison to other industries given
  • 00:04:06
    your experience as a data leader in
  • 00:04:07
    healthcare i'd love to first start off
  • 00:04:09
    our conversation by understanding how
  • 00:04:11
    you would describe what the current
  • 00:04:12
    landscape of data science and healthcare
  • 00:04:14
    looks like today and how has it evolved
  • 00:04:16
    in the past few years oh yeah that's an
  • 00:04:19
    exciting question and it's it has
  • 00:04:21
    evolved and different parts of
  • 00:04:22
    healthcare i'll say are probably
  • 00:04:25
    at different places and evolving and at
  • 00:04:28
    different paces out of sometimes
  • 00:04:31
    necessity and you say there's a lot of
  • 00:04:33
    opportunity in healthcare there is
  • 00:04:35
    and i think it's one of those industries
  • 00:04:38
    where
  • 00:04:38
    you have to take a bit of a careful
  • 00:04:40
    approach to anything new they're
  • 00:04:42
    practically their regulations and
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    there's a lot of risk for something
  • 00:04:46
    going wrong but huge benefits but what
  • 00:04:48
    i've seen over
  • 00:04:50
    uh the last
  • 00:04:52
    few years
  • 00:04:53
    is really a couple things that we're
  • 00:04:55
    seeing in a lot of industries
  • 00:04:57
    but in healthcare as well scale
  • 00:05:00
    as we're moving into hey data science
  • 00:05:03
    can be very very useful for solving real
  • 00:05:06
    problems in healthcare there's a focus
  • 00:05:08
    on
  • 00:05:09
    deploying these models and not just
  • 00:05:11
    having perfect
  • 00:05:12
    concepts but really using them to drive
  • 00:05:16
    core business decisions and core
  • 00:05:18
    insights and and that requires data
  • 00:05:21
    science at scale where at first
  • 00:05:23
    it was a little more experimental a
  • 00:05:25
    little more well let's just see
  • 00:05:27
    how this goes alongside what we do today
  • 00:05:29
    but we're not going to go all in and
  • 00:05:31
    really use this to drive our business
  • 00:05:33
    but we're moving towards that
  • 00:05:35
    the other
  • 00:05:37
    change i i guess are the problems that
  • 00:05:40
    that we can
  • 00:05:41
    solve or just we're realizing them right
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    we're expanding the scope of what data
  • 00:05:46
    science can do in healthcare and
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    of course there's diagnostics there's
  • 00:05:50
    also operations there's clinical trials
  • 00:05:53
    and how those are run how patients are
  • 00:05:55
    found there's so many things we can do
  • 00:05:58
    and then a third i
  • 00:05:59
    really important i wouldn't say change
  • 00:06:02
    but something that's just continues to
  • 00:06:04
    mature and we think about and i think
  • 00:06:05
    it's helped accelerate data science and
  • 00:06:08
    healthcare it's just thinking about the
  • 00:06:10
    ethics of what we're doing considering
  • 00:06:12
    it's impacting people and the care they
  • 00:06:14
    receive and it can be
  • 00:06:18
    life or death or it can either help or
  • 00:06:20
    hurt the disparities we're seeing in
  • 00:06:22
    care so really have thinking about
  • 00:06:25
    ethics which is important in healthcare
  • 00:06:27
    and then having tools and ways to
  • 00:06:29
    address that at scale
  • 00:06:31
    has really evolved over the past few
  • 00:06:33
    years
  • 00:06:34
    that's really great and i'm excited to
  • 00:06:36
    unpack these with you even more so you
  • 00:06:38
    mentioned at the beginning some of the
  • 00:06:40
    areas of impact that data science and
  • 00:06:42
    machine learning have in healthcare do
  • 00:06:44
    you mind expanding on these main areas
  • 00:06:45
    of value where you've seen data science
  • 00:06:47
    and machine learning push the envelope
  • 00:06:49
    forward within the healthcare space
  • 00:06:51
    it's hard to
  • 00:06:52
    pick a few but one i love to talk about
  • 00:06:55
    and this is something my former team did
  • 00:06:57
    and i really i loved the way they
  • 00:07:00
    approached this and i saw to impact
  • 00:07:02
    patients was looking at operations
  • 00:07:05
    so sometimes in healthcare we go at the
  • 00:07:09
    we're going to cure this disease we're
  • 00:07:10
    going to diagnose this disease and of
  • 00:07:12
    course how do we not say we're gonna put
  • 00:07:16
    every data science tool we have towards
  • 00:07:18
    cancer and we should
  • 00:07:20
    but a safer way in in a way in that
  • 00:07:23
    makes a huge impact can be the
  • 00:07:24
    operations of healthcare itself or the
  • 00:07:27
    operations of a clinical trial so i'll
  • 00:07:28
    give you an example when i was at
  • 00:07:30
    highmark health we built a tool
  • 00:07:33
    to help schedule patients receiving
  • 00:07:35
    chemotherapy and a big thing for me to
  • 00:07:38
    start with the problem we heard about
  • 00:07:40
    hey we're scheduling patients for
  • 00:07:42
    chemotherapy they have long wait times
  • 00:07:44
    which seemed not great
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    we notice we're really busy in the
  • 00:07:48
    mornings and then
  • 00:07:50
    things are empty in in the afternoon so
  • 00:07:52
    our clinicians are either overwhelmed or
  • 00:07:55
    don't have a lot of patience and we dug
  • 00:07:57
    in that was
  • 00:07:58
    two things they didn't know how long a
  • 00:08:00
    treatment could take and there could be
  • 00:08:01
    side effects and clinicians want to care
  • 00:08:03
    for their patients and make sure they
  • 00:08:05
    have plenty of time so they're blind to
  • 00:08:07
    how how long each patient might need
  • 00:08:10
    staying there in that location so if
  • 00:08:12
    we're able to predict that we can
  • 00:08:14
    start efficiently scheduling and then
  • 00:08:16
    just optimizing the scheduling
  • 00:08:18
    optimizing the operations where in the
  • 00:08:20
    calendar can this go where location wise
  • 00:08:22
    can this go and we had this tool ready
  • 00:08:25
    when the pandemic started and it became
  • 00:08:26
    even more important to space vulnerable
  • 00:08:28
    patients out it started with an
  • 00:08:30
    operational challenge though scheduling
  • 00:08:32
    very practical thing to solve and it
  • 00:08:34
    made a huge difference i i've heard and
  • 00:08:37
    stories from patients and saying hey i
  • 00:08:39
    can get on and back to my life and not
  • 00:08:41
    wait i can come at times convenient to
  • 00:08:43
    me
  • 00:08:44
    another area that i've seen
  • 00:08:46
    an impact and a lot of promises
  • 00:08:48
    diagnosis or detection early diagnosis
  • 00:08:50
    early detection to give clinicians some
  • 00:08:54
    some time to intervene we've heard about
  • 00:08:56
    this in things like sepsis or acute
  • 00:08:58
    diseases we're talking about early
  • 00:09:02
    detection of things like pulmonary
  • 00:09:03
    hypertension which is frequently
  • 00:09:04
    diagnosed late and i know that's
  • 00:09:06
    something where we're doing now these
  • 00:09:08
    are big big areas of opportunity where
  • 00:09:10
    we can treat patients
  • 00:09:12
    because we can detect these diseases and
  • 00:09:14
    diagnose them
  • 00:09:15
    and then the third is patient's own
  • 00:09:18
    experience like with the operational
  • 00:09:21
    component of course that had a patient
  • 00:09:23
    experience
  • 00:09:24
    piece but just understanding patients
  • 00:09:28
    their journeys where they're facing
  • 00:09:30
    challenges how they're experiencing the
  • 00:09:32
    healthcare system and where we're not
  • 00:09:34
    maybe delivering care in the way we
  • 00:09:35
    should
  • 00:09:36
    data can help us see that
  • 00:09:38
    and help us deliver a better experience
  • 00:09:40
    deliver more personalized tailored
  • 00:09:42
    experience on a biological level as well
  • 00:09:46
    as
  • 00:09:47
    just
  • 00:09:48
    an individual level preferences ways of
  • 00:09:50
    interacting and ways of receiving care i
  • 00:09:53
    love how you frame the operations
  • 00:09:54
    component here because whenever we talk
  • 00:09:56
    about data science and machine learning
  • 00:09:57
    in healthcare we always talk about
  • 00:09:59
    aspirational use cases that i think
  • 00:10:01
    we're all in agreement are extremely
  • 00:10:02
    important for example i'm very excited
  • 00:10:04
    to see the impacts of deep minds alpha
  • 00:10:06
    fault and direct discovery but that
  • 00:10:08
    doesn't mean we cannot create impact on
  • 00:10:10
    people's lives right now with data
  • 00:10:12
    science just by solving operational
  • 00:10:14
    challenges when talking about data
  • 00:10:16
    science and healthcare we often talk
  • 00:10:18
    about challenges unique to the
  • 00:10:19
    healthcare space such as access to
  • 00:10:21
    relevant interoperable data ethics of ai
  • 00:10:24
    and a host of other challenges i'd love
  • 00:10:26
    it if you can break down what are the
  • 00:10:28
    main data challenges you think that the
  • 00:10:30
    healthcare industry is facing today
  • 00:10:32
    i talk to my colleagues across
  • 00:10:34
    industries everything manufacturing
  • 00:10:37
    automotive
  • 00:10:38
    just
  • 00:10:39
    very different industries and no one
  • 00:10:41
    tells me
  • 00:10:42
    our data is perfect clean haven't really
  • 00:10:45
    had a problem there or thought about it
  • 00:10:47
    of course you're not surprised to hear
  • 00:10:48
    this and in healthcare we base that as
  • 00:10:51
    well and interoperability and different
  • 00:10:53
    formats of data we're facing the same
  • 00:10:55
    things but i think we're realizing that
  • 00:10:58
    a other industries that face this and be
  • 00:10:59
    you know there are solutions that will
  • 00:11:01
    work here as well it's the whole topic
  • 00:11:04
    the ethics of ai is is huge a huge one
  • 00:11:08
    here
  • 00:11:08
    and really really important so
  • 00:11:11
    this becomes crucial in in healthcare
  • 00:11:14
    i'm not saying if if you're selling a
  • 00:11:16
    consumer good of course you don't want
  • 00:11:18
    to make a mistake but if i get a
  • 00:11:19
    recommendation to buy
  • 00:11:21
    a toaster oven and i just bought a
  • 00:11:23
    toaster oven so i'm probably not going
  • 00:11:25
    to buy a second one and this just
  • 00:11:26
    happened to me it's not a big deal it
  • 00:11:28
    didn't really affect my life you can
  • 00:11:30
    experiment with those algorithms get
  • 00:11:31
    them out there and get them out there
  • 00:11:34
    quickly and in healthcare we've
  • 00:11:36
    obviously had to
  • 00:11:37
    think and other industries face this as
  • 00:11:39
    well
  • 00:11:40
    there's risk so you have to really think
  • 00:11:43
    through
  • 00:11:44
    what you're doing and what could happen
  • 00:11:47
    and how this algorithm is going to work
  • 00:11:48
    what how you're going to build this
  • 00:11:50
    process
  • 00:11:51
    and get it right that's not to say there
  • 00:11:53
    aren't things we can do there's a lot
  • 00:11:55
    because there are a lot of problems and
  • 00:11:56
    things we're not doing
  • 00:11:58
    really well today so as long as we're
  • 00:12:00
    not making it worse we should try some
  • 00:12:01
    things but that's always going to be
  • 00:12:05
    a pretty big challenge and an important
  • 00:12:07
    challenge that we should take on
  • 00:12:08
    relative to
  • 00:12:09
    other industries
  • 00:12:11
    it's just talking about the data
  • 00:12:12
    obviously the sensitivity of the data
  • 00:12:14
    itself
  • 00:12:15
    makes it maybe a little harder to get
  • 00:12:17
    access to data or think about how to use
  • 00:12:20
    it share it what kinds of environments
  • 00:12:22
    that data can be in
  • 00:12:24
    and it should be i mean that's a
  • 00:12:26
    challenge we should take on as a good
  • 00:12:28
    challenge and the one we say we were
  • 00:12:30
    never good enough because this is the
  • 00:12:32
    most sensitive data in people's lives so
  • 00:12:35
    that we should be continuously improving
  • 00:12:38
    and thinking about how we protect this
  • 00:12:40
    data how we use it how we make sure
  • 00:12:43
    we're using it in a way
  • 00:12:45
    that decreases inequalities in how we
  • 00:12:48
    deliver care which i think it can but we
  • 00:12:50
    have to use the data responsibly and
  • 00:12:52
    consider it is very very sensitive data
  • 00:12:55
    maybe more so than if there's a
  • 00:12:57
    a leak of that i bought a toaster oven
  • 00:13:00
    not that exciting
  • 00:13:02
    i bought a coffee baker
  • 00:13:04
    not that
  • 00:13:05
    not that exciting but this this is a
  • 00:13:07
    pretty big one
  • 00:13:08
    i completely agree here and let's spark
  • 00:13:10
    the chat a bit and talk about the ethics
  • 00:13:12
    of ai in healthcare when we talk about
  • 00:13:14
    using machine learning and ai in
  • 00:13:16
    healthcare there's this aversion that
  • 00:13:18
    whatever we develop will end up creating
  • 00:13:20
    harmful outcomes or that it could be
  • 00:13:22
    used irresponsibly and oftentimes the
  • 00:13:24
    response is not to leverage machine
  • 00:13:26
    learning in ai so i'd love to understand
  • 00:13:28
    how you evaluate the risk of harmful
  • 00:13:31
    outcomes of machine learning and ai in
  • 00:13:32
    healthcare and how do you go about
  • 00:13:33
    minimizing it
  • 00:13:35
    well a great question one big thing to
  • 00:13:38
    understand the potential harmful
  • 00:13:41
    outcomes you have to understand the
  • 00:13:43
    problem that you're solving
  • 00:13:45
    be working collaboratively with a
  • 00:13:48
    cross-functional team with clinicians
  • 00:13:50
    with whoever is using and implementing
  • 00:13:53
    and acting on your model with patients
  • 00:13:55
    you have to have everyone in the room
  • 00:13:56
    and involved in this process
  • 00:13:59
    and understand that end-to-end because
  • 00:14:01
    that's the only way you're going to find
  • 00:14:03
    where the risks might lie you have to
  • 00:14:05
    understand how how they're going to use
  • 00:14:08
    this information and make a decision
  • 00:14:10
    what mitigations can you build in where
  • 00:14:12
    are the risks at every point in this
  • 00:14:14
    system in that is sometimes something
  • 00:14:17
    data scientists especially when they get
  • 00:14:19
    started they're excited to build models
  • 00:14:22
    and they skip over this piece of it
  • 00:14:24
    unintentionally and when i read about
  • 00:14:26
    you know resumes from the hr world like
  • 00:14:29
    the algorithm is going to learn
  • 00:14:31
    what you feed it and historically data
  • 00:14:34
    reflects our human biases so the
  • 00:14:37
    algorithm if you don't
  • 00:14:39
    think about it and you don't account for
  • 00:14:40
    that is going to learn to do exactly
  • 00:14:42
    what people have done which is not
  • 00:14:44
    uh
  • 00:14:45
    really necessarily ethical but
  • 00:14:49
    when with data and with an algorithm we
  • 00:14:51
    have an ability to fix that and to
  • 00:14:53
    control that a bit more than than we do
  • 00:14:55
    in people but i always think about the
  • 00:14:57
    end end how the decisions being made it
  • 00:14:59
    can't just be about the algorithm and
  • 00:15:01
    another part is it sounds kind of simple
  • 00:15:04
    but empathy and the human centered
  • 00:15:06
    design thinking approach is very
  • 00:15:07
    valuable for data science because you
  • 00:15:09
    start
  • 00:15:10
    putting yourself in the shoes of
  • 00:15:13
    the the person who's affected by this
  • 00:15:15
    the patient all of the things they may
  • 00:15:17
    be facing and all of the things that may
  • 00:15:19
    happen based on the
  • 00:15:21
    algorithm so you've got to really think
  • 00:15:23
    about it from that angle and
  • 00:15:26
    then it's of course the technology the
  • 00:15:28
    data itself
  • 00:15:30
    what biases are there the algorithms
  • 00:15:32
    you're choosing the ways you can
  • 00:15:34
    mitigate and correct it can you and
  • 00:15:37
    that's job a technical expertise a data
  • 00:15:39
    scientist has to have and it's essential
  • 00:15:42
    now especially in in healthcare but
  • 00:15:44
    everywhere we want to think about that
  • 00:15:46
    the other obvious one is really going
  • 00:15:49
    way back and saying did we pick the
  • 00:15:50
    right use case and like the operations
  • 00:15:52
    example there's a lot of problems to
  • 00:15:54
    solve in healthcare we should be
  • 00:15:56
    thinking about all of them but maybe
  • 00:15:58
    the easier quick wins are ones where
  • 00:16:01
    there's a little
  • 00:16:03
    less
  • 00:16:04
    opportunity for harm if it's maybe we're
  • 00:16:08
    just randomly we're communicating with
  • 00:16:10
    everyone in the same way today and maybe
  • 00:16:12
    if we try to figure out some preferences
  • 00:16:15
    and try to customize a bit and learn
  • 00:16:17
    from there that may be lower risk than
  • 00:16:19
    detecting a disease or changing the
  • 00:16:22
    course of care and in medicine and
  • 00:16:24
    healthcare this doesn't replace a
  • 00:16:25
    clinician we want this to enhance the
  • 00:16:27
    clinicians decision making that's
  • 00:16:29
    awesome and i love how you draw
  • 00:16:30
    inspiration from other fields like human
  • 00:16:32
    centered design given that do you think
  • 00:16:34
    also healthcare can draw from risk
  • 00:16:36
    management risk analysis to create ai
  • 00:16:38
    governance frameworks i think that is a
  • 00:16:41
    great question and absolutely there is
  • 00:16:44
    no industry
  • 00:16:46
    we can't learn from we have to be
  • 00:16:48
    looking outside of healthcare
  • 00:16:50
    all the time and looking across
  • 00:16:52
    healthcare to different parts of
  • 00:16:53
    healthcare but definitely looking
  • 00:16:55
    outside that's why i've
  • 00:16:57
    very intentionally hired people from
  • 00:16:58
    other industries
  • 00:17:00
    on my teams i've wanted people from
  • 00:17:02
    manufacturing and and it has worked
  • 00:17:05
    they've come in and looked at things and
  • 00:17:06
    said this is not an easy but a pretty
  • 00:17:08
    easy problem to solve we deal with this
  • 00:17:10
    all the time and
  • 00:17:12
    something that someone my background is
  • 00:17:14
    mainly in healthcare i would think
  • 00:17:17
    certainly movement of chemotherapy drugs
  • 00:17:19
    around to different locations that i i
  • 00:17:21
    thought as though that's a pretty big
  • 00:17:22
    challenge but i knew that other
  • 00:17:24
    industries had solved it and so i looked
  • 00:17:27
    to people from those industries to come
  • 00:17:29
    in and bring some of that thinking to
  • 00:17:30
    healthcare risk management of course
  • 00:17:33
    that is something we do we have uh risk
  • 00:17:37
    mitigation plans for everything we do
  • 00:17:39
    think through everything early
  • 00:17:41
    the every industry we need to be looking
  • 00:17:44
    outside all the time in healthcare when
  • 00:17:46
    thinking about some of the other
  • 00:17:47
    obstacles that are unique to healthcare
  • 00:17:49
    such as data access interoperability and
  • 00:17:51
    collection what needs to change so that
  • 00:17:54
    data science healthcare innovation
  • 00:17:55
    accelerates here is it regulatory
  • 00:17:57
    innovation industry standards that need
  • 00:17:59
    to evolve
  • 00:18:00
    the regulatory component is there it's
  • 00:18:03
    important there's collaborative work and
  • 00:18:05
    discussions going on across healthcare
  • 00:18:08
    to make sure the the regulatory
  • 00:18:11
    environment meets the needs of data
  • 00:18:14
    science that's an ongoing process
  • 00:18:16
    another one though that
  • 00:18:18
    maybe is every industry but i see it a
  • 00:18:21
    lot in healthcare the systems are very
  • 00:18:23
    complex
  • 00:18:24
    we have different
  • 00:18:26
    emr systems those have a lot of steps
  • 00:18:28
    and pieces data scientists don't always
  • 00:18:31
    understand
  • 00:18:32
    how a clinician interacts with that
  • 00:18:34
    system yet that's that may be the place
  • 00:18:36
    where their
  • 00:18:37
    solution is delivered and acted on where
  • 00:18:39
    the value is realized but they're very
  • 00:18:42
    complicated systems and to get them all
  • 00:18:44
    to connect maybe we want to use
  • 00:18:46
    multimodal data from multiple sources
  • 00:18:49
    imaging
  • 00:18:51
    devices everything to really get a full
  • 00:18:53
    picture of the patient at different time
  • 00:18:55
    scales
  • 00:18:56
    to really scale that solution and
  • 00:18:58
    implement it we need those systems
  • 00:19:00
    connected you can do it once grab all
  • 00:19:02
    the data put it together build a model
  • 00:19:04
    but how do you then deploy that model
  • 00:19:06
    seeing some simplification of these
  • 00:19:08
    systems and some consideration of hey
  • 00:19:11
    it's very important to use this data to
  • 00:19:13
    deploy solutions and to seamlessly
  • 00:19:15
    connect and simplify things
  • 00:19:17
    would be great to see and i think we're
  • 00:19:20
    probably going to see that and i
  • 00:19:21
    as i said it probably exists in in other
  • 00:19:24
    industries as well
  • 00:19:26
    um the other one is experience with data
  • 00:19:28
    science data literacy or ai literacy
  • 00:19:32
    we don't need
  • 00:19:34
    clinicians and hospital administers they
  • 00:19:37
    don't need to be experts in data science
  • 00:19:39
    but
  • 00:19:40
    i think as we all bring up that level of
  • 00:19:43
    understanding and understanding
  • 00:19:46
    how data science works how some of this
  • 00:19:49
    stuff can be used and be able to speak a
  • 00:19:51
    bit of the same language that would help
  • 00:19:53
    and we're seeing that again in every
  • 00:19:55
    industry but one i think we have a good
  • 00:19:57
    chance of solving in in medicine a lot
  • 00:20:00
    of people have a scientific background
  • 00:20:02
    and
  • 00:20:03
    it's data science has the science so
  • 00:20:06
    it should be a good place and i've seen
  • 00:20:09
    a lot of engaged clinicians and a lot
  • 00:20:11
    coming in with a lot of knowledge
  • 00:20:13
    experimental design and that's moving
  • 00:20:15
    along but we could be better there and
  • 00:20:18
    we need to keep pushing and that data
  • 00:20:20
    literacy component is huge from a data
  • 00:20:22
    quality perspective because a lot of
  • 00:20:24
    healthcare professionals are the ones
  • 00:20:25
    who are inputting this data into these
  • 00:20:27
    systems and if they do not recognize the
  • 00:20:29
    role the data plays in the value chain
  • 00:20:31
    of data science then that value chain
  • 00:20:33
    will end up breaking because no one is
  • 00:20:35
    paying attention to the data quality
  • 00:20:37
    right that's a great point and it
  • 00:20:39
    actually that data literacy then it's
  • 00:20:41
    going both ways it's a business literacy
  • 00:20:44
    on the data scientist part of
  • 00:20:45
    understanding
  • 00:20:47
    how a clinician is inputting data and
  • 00:20:50
    how they're interacting with an emr
  • 00:20:52
    system or how on you know the insurance
  • 00:20:54
    side maybe a care manager
  • 00:20:56
    is identifying and reaching out to
  • 00:20:59
    members of an insurance plan to help
  • 00:21:01
    them coordinate their care and manage a
  • 00:21:04
    chronic disease but
  • 00:21:05
    we we have to understand how that data
  • 00:21:07
    comes in
  • 00:21:08
    and
  • 00:21:09
    conversely
  • 00:21:10
    if we show the value of data science the
  • 00:21:13
    the people delivering care and part of
  • 00:21:15
    that healthcare ecosystem
  • 00:21:17
    are going to be able to work with us and
  • 00:21:19
    say okay like i can i can
  • 00:21:21
    see the value of uh this distinction as
  • 00:21:25
    long as we don't take time away from
  • 00:21:26
    their interactions with patients and
  • 00:21:28
    make it harder don't want to do that
  • 00:21:30
    that's awesome and given we're
  • 00:21:32
    discussing the value of data science and
  • 00:21:33
    healthcare i'd like to pivot to discuss
  • 00:21:35
    your experience as a data and ai leader
  • 00:21:37
    at johnson and johnson i'd love to
  • 00:21:39
    understand and dig through some of the
  • 00:21:41
    most exciting use cases you've seen data
  • 00:21:43
    teams working on especially in
  • 00:21:44
    healthcare at johnson and johnson
  • 00:21:46
    especially given what must have been a
  • 00:21:48
    very interesting time for r d teams with
  • 00:21:51
    the release of the j j kovit 19 vaccine
  • 00:21:53
    yeah there there are three that really
  • 00:21:55
    come to mind and one we all are
  • 00:21:58
    so deep in it it's always a great
  • 00:22:00
    example so this is this is something i
  • 00:22:02
    think is an excellent example of using
  • 00:22:05
    data science to solve a real problem and
  • 00:22:06
    make an impact
  • 00:22:08
    when
  • 00:22:09
    clinical trials are planned as you can
  • 00:22:11
    imagine they're complex there's a lot of
  • 00:22:12
    planning and you need to decide where to
  • 00:22:14
    have those trials
  • 00:22:16
    in the case of the vaccine
  • 00:22:18
    we needed to find places where
  • 00:22:21
    kovid was spreading so that we could see
  • 00:22:23
    whether this worked quickly and get it
  • 00:22:25
    out to people
  • 00:22:26
    and what the teams were able to do using
  • 00:22:28
    data science was predict where these
  • 00:22:31
    future hotspots would be and plan the
  • 00:22:34
    clinical trials in those places and it
  • 00:22:36
    was effective and it allowed us to
  • 00:22:38
    accelerate that and be really targeted
  • 00:22:40
    and where we were doing clinical trials
  • 00:22:42
    and where we're seeing high levels of
  • 00:22:44
    covet so i think that's just a very
  • 00:22:47
    great example and it shows data science
  • 00:22:49
    can
  • 00:22:50
    rise to the challenge and really solve
  • 00:22:53
    big problems under pressure when it
  • 00:22:55
    counts with there is no bigger really
  • 00:22:57
    pressure in recent times than the whole
  • 00:23:00
    world's in this pandemic and we need to
  • 00:23:03
    do something about it with data science
  • 00:23:04
    i'm really proud of that
  • 00:23:06
    the other i think i mentioned the
  • 00:23:07
    pulmonary hypertension example but just
  • 00:23:09
    one example of how we can bring
  • 00:23:12
    data together and use ai to diagnose a
  • 00:23:15
    condition earlier and that and that's
  • 00:23:17
    something we're doing and working on
  • 00:23:18
    that's very very exciting this is an
  • 00:23:21
    under diagnosed disease or it's not
  • 00:23:23
    diagnosed early when when we could treat
  • 00:23:25
    it and make an impact so if we can bring
  • 00:23:28
    together diverse data sources and
  • 00:23:30
    predict that diagnosis we can really
  • 00:23:32
    make a difference in people's lives and
  • 00:23:34
    then the third is just generally using
  • 00:23:37
    data to accelerate what we're doing and
  • 00:23:39
    how we're doing it at every part of the
  • 00:23:41
    process we could talk about that all day
  • 00:23:43
    but using digital data and digital
  • 00:23:46
    endpoints to better measure outcomes
  • 00:23:48
    using real world data claims data ehr
  • 00:23:51
    data to really make sure we understand
  • 00:23:53
    the patients we understand their needs
  • 00:23:55
    we're developing drugs that are going to
  • 00:23:59
    to make a difference and we're doing it
  • 00:24:00
    efficiently and quickly because it
  • 00:24:02
    always strikes me that every day that
  • 00:24:04
    this is not out there a patient's not
  • 00:24:06
    getting this treatment so i love that we
  • 00:24:08
    are always focused on how do we get
  • 00:24:10
    medicines to patients faster because
  • 00:24:14
    this matters and we all either have been
  • 00:24:17
    know someone or will be affected by this
  • 00:24:20
    i absolutely love the kovit 19 use case
  • 00:24:22
    here and it's really exemplary of a data
  • 00:24:24
    science use case that requires
  • 00:24:26
    relatively simple data science that can
  • 00:24:28
    provide value now for patients and
  • 00:24:30
    healthcare providers so i'd love it if
  • 00:24:31
    you can impact that use case even more
  • 00:24:33
    and maybe discuss the methodology used
  • 00:24:35
    here
  • 00:24:36
    i think it's a general process that
  • 00:24:38
    really is important for solving any data
  • 00:24:41
    science
  • 00:24:42
    problem and at a high level
  • 00:24:45
    and i've done this set up very multiple
  • 00:24:47
    companies
  • 00:24:48
    it starts with identifying a clear
  • 00:24:50
    problem in this case right it was
  • 00:24:52
    clearly we don't know where to
  • 00:24:55
    plan to have these clinical trials and
  • 00:24:57
    it's not something we can spin up in a
  • 00:25:00
    day it takes some time so how could we
  • 00:25:02
    know earlier it's finding that problem
  • 00:25:04
    that can be solved with data science
  • 00:25:07
    that's one piece that was crucial here
  • 00:25:10
    and then it's collaborating working
  • 00:25:13
    together
  • 00:25:15
    with the
  • 00:25:16
    business clinical areas
  • 00:25:18
    to
  • 00:25:19
    design and implement that solution in
  • 00:25:21
    time sometimes data science if it gets
  • 00:25:24
    too exploratory or
  • 00:25:27
    just experimental
  • 00:25:28
    we're not thinking about the urgency in
  • 00:25:30
    the timelines where we need to deliver
  • 00:25:32
    and working closely as a core member
  • 00:25:34
    across the team and to to make something
  • 00:25:37
    like this happen you have to do that
  • 00:25:39
    those are just two key things that have
  • 00:25:42
    to happen in any high impact
  • 00:25:44
    data science use case and i think ones
  • 00:25:46
    that have served well and then the third
  • 00:25:49
    a piece of advice i got very early and
  • 00:25:51
    i've always used and i've seen
  • 00:25:54
    as a component of successful projects is
  • 00:25:57
    really understanding how
  • 00:26:00
    the solution you're building is going to
  • 00:26:01
    be used and making sure the people who
  • 00:26:03
    are going to use it are involved in the
  • 00:26:05
    planning and have bought into this
  • 00:26:06
    because you
  • 00:26:08
    if you don't have adoption you're you're
  • 00:26:10
    not going to solve the problem that that
  • 00:26:12
    you wanted to solve so i think one thing
  • 00:26:14
    that's evident is that there's a lot of
  • 00:26:15
    different data teams at j doing
  • 00:26:17
    different work it's one challenge to do
  • 00:26:19
    this data science and health care but
  • 00:26:21
    it's another challenge to work in a
  • 00:26:22
    large matrix organizations where there
  • 00:26:24
    are tons of stakeholders and a lot of
  • 00:26:26
    different teams working on different
  • 00:26:27
    problems i'd love to know how you ensure
  • 00:26:30
    that you're staying effective despite
  • 00:26:31
    this complexity and some of the best
  • 00:26:33
    practices you can share
  • 00:26:35
    in managing and working with data teams
  • 00:26:38
    in large matrix organizations with other
  • 00:26:40
    data leaders i think a big one is coming
  • 00:26:43
    back to the shared mission vision what
  • 00:26:46
    you're trying to do because in a
  • 00:26:48
    healthcare organization or any
  • 00:26:50
    organization but definitely in
  • 00:26:51
    healthcare and at johnson and johnson it
  • 00:26:53
    is
  • 00:26:54
    very clear we are getting medicines to
  • 00:26:57
    patients we're saving people's lives at
  • 00:26:59
    the end of the day that
  • 00:27:01
    cuts the the matrix the complexity of a
  • 00:27:03
    large company sure it's there but the
  • 00:27:06
    culture and the focus on the patient and
  • 00:27:08
    what we're doing unifies and brings us
  • 00:27:10
    all together and breaks down those silos
  • 00:27:12
    and i think if at any company if you
  • 00:27:15
    find and focus on that the problem
  • 00:27:18
    and what you all care about how
  • 00:27:20
    everyone's benefiting
  • 00:27:21
    it it really helps the other is
  • 00:27:23
    something i i think is just crucial
  • 00:27:25
    bring people in early from across your
  • 00:27:27
    company it becomes more complex when
  • 00:27:29
    data science happens in the silo and
  • 00:27:31
    then you show up with a solution
  • 00:27:34
    and different parts of the business are
  • 00:27:36
    thinking oh no we needed to be involved
  • 00:27:38
    earlier this is slightly off here and it
  • 00:27:41
    it can be harder than it needs to be
  • 00:27:43
    which is
  • 00:27:45
    brings me to the good part of a large
  • 00:27:47
    matrix organization and why i keep
  • 00:27:50
    working for them and i love to be at one
  • 00:27:52
    i love to be the leader in a large
  • 00:27:54
    matrix organization
  • 00:27:56
    you have incredible resources
  • 00:27:59
    you have
  • 00:28:00
    experts you have legal teams you have
  • 00:28:03
    supply chain there's there's so many
  • 00:28:06
    experts in
  • 00:28:08
    the area where you're developing
  • 00:28:09
    solutions that's it is a luxury to have
  • 00:28:12
    when you're a startup i talk to
  • 00:28:13
    companies people that have great ideas
  • 00:28:16
    and they have to work so hard to just
  • 00:28:17
    get access to hey can you just tell me
  • 00:28:19
    about
  • 00:28:20
    some of the problems you have or how
  • 00:28:22
    this works and they don't have all of
  • 00:28:24
    these resources surrounding them at a
  • 00:28:26
    large company
  • 00:28:28
    you have so much support and you can
  • 00:28:31
    never reach out too much or
  • 00:28:33
    too early and think about hey you know
  • 00:28:35
    what i'm struggling a bit with maybe
  • 00:28:38
    how do you think about marketing oh we
  • 00:28:40
    have a marketing team they everybody
  • 00:28:42
    loves to get involved and they love
  • 00:28:44
    to help and most companies i think
  • 00:28:46
    you'll find this so reach out and use
  • 00:28:48
    those resources that make a large
  • 00:28:50
    company great because otherwise you're
  • 00:28:52
    going to have
  • 00:28:54
    all the bad parts of a big company and
  • 00:28:55
    none of the good parts and that why do
  • 00:28:57
    that that's great and it must be
  • 00:28:59
    especially rewarding to have access to
  • 00:29:01
    healthcare subject matter experts across
  • 00:29:03
    the value chain because this will help
  • 00:29:05
    you develop this empathy to create human
  • 00:29:08
    centered data science solutions
  • 00:29:10
    exactly no absolutely and we have that
  • 00:29:13
    easily just phone call or
  • 00:29:16
    quick message away like
  • 00:29:18
    we're
  • 00:29:19
    people are happy to talk and using that
  • 00:29:22
    is key yes wonderful to have you
  • 00:29:25
    great to use awesome so i'm sure these
  • 00:29:27
    conversations with subject matter
  • 00:29:29
    experts also influence your roadmap
  • 00:29:31
    given the importance of r d in the
  • 00:29:33
    healthcare space how do you ensure an
  • 00:29:35
    adequate split between long-term
  • 00:29:36
    research and short-term wins that can
  • 00:29:39
    help you move the needle yeah absolutely
  • 00:29:41
    and right now i'm in this r d
  • 00:29:43
    environment developing medicines and
  • 00:29:46
    it's a long-term view which is really
  • 00:29:49
    interesting to see and to have
  • 00:29:51
    that said there's a lot of short pieces
  • 00:29:55
    and wins along the way to get to that
  • 00:29:57
    end goal
  • 00:29:58
    so if you're working with the
  • 00:30:02
    clinical teams and as we do we really
  • 00:30:05
    work together or in any company you're
  • 00:30:06
    working with the business area and
  • 00:30:09
    talking about
  • 00:30:10
    what is that end to end what's the
  • 00:30:12
    ultimate kind of long-term outcomes and
  • 00:30:15
    then work backwards what are the short
  • 00:30:17
    pieces and those quick wins as you say a
  • 00:30:19
    lot to get you there
  • 00:30:20
    you get that mix and then i think it's
  • 00:30:23
    important to look at at the portfolio
  • 00:30:26
    you have for data science and go through
  • 00:30:28
    and see
  • 00:30:30
    how many of these are really
  • 00:30:32
    it's going to be years before we see the
  • 00:30:34
    value and that's something in data
  • 00:30:36
    science you need to know because you
  • 00:30:37
    have to be careful not to let that
  • 00:30:40
    timeline
  • 00:30:41
    and that
  • 00:30:42
    pace of technology and changes conflicts
  • 00:30:45
    you've got to think about it early but
  • 00:30:47
    yeah looking at how many long-term
  • 00:30:49
    projects we have how many short quick
  • 00:30:51
    wins do i have and then also
  • 00:30:54
    it's okay to have purely exploratory i'm
  • 00:30:56
    gonna play around with this data see if
  • 00:30:58
    i can develop this model that's great to
  • 00:31:01
    have too it's just looking across the
  • 00:31:03
    portfolio and making sure
  • 00:31:05
    that the
  • 00:31:06
    percentage of work that's in all of
  • 00:31:08
    these buckets is where you want it to be
  • 00:31:10
    and need it to be and how do you
  • 00:31:11
    determine which areas to research in
  • 00:31:13
    your r d agenda
  • 00:31:15
    the good thing is in an r d organization
  • 00:31:18
    that happens at such a high high level
  • 00:31:21
    but
  • 00:31:22
    to bring it back to one simple concept
  • 00:31:24
    it's unmet need and what do patients
  • 00:31:27
    need and i think it's something
  • 00:31:29
    that applies everywhere that where is
  • 00:31:31
    there an unmet need where we can bring
  • 00:31:33
    data science in but of course that's
  • 00:31:35
    goes into the planning of what do we
  • 00:31:37
    develop and it's a pharmaceutical r d
  • 00:31:39
    organization it's a big process it's the
  • 00:31:42
    core of of the business
  • 00:31:44
    and then there's the data science
  • 00:31:46
    component how does data science
  • 00:31:48
    support and accelerate and enhance
  • 00:31:52
    that that portfolio and that that r d
  • 00:31:55
    process
  • 00:31:56
    and as we mature and talk to each other
  • 00:31:59
    and data science grows which we're doing
  • 00:32:02
    at johnson johnson janssen r d which is
  • 00:32:06
    pharmaceutical companies johnson johnson
  • 00:32:08
    the data science team and capabilities
  • 00:32:11
    are just exceptional
  • 00:32:13
    jacqueline is our chief data science
  • 00:32:15
    officer has built
  • 00:32:17
    just a really incredibly advanced
  • 00:32:20
    capability and and the company is
  • 00:32:22
    putting a lot of investment
  • 00:32:24
    into data science in r d and commercial
  • 00:32:27
    and across the company
  • 00:32:28
    it's great to see and that shows me that
  • 00:32:31
    there is it right we've had the
  • 00:32:32
    discussion about this can impact
  • 00:32:35
    the r d portfolio this can um
  • 00:32:38
    help you meet your goals and we've had
  • 00:32:40
    that conversation conversations been
  • 00:32:41
    successful and that's why we're able to
  • 00:32:43
    to grow and really use data science
  • 00:32:45
    now karen as we close out i'd love to
  • 00:32:47
    have a look into the future and what you
  • 00:32:49
    think are the data trends and
  • 00:32:50
    innovations that you're particularly
  • 00:32:52
    looking forward to see within healthcare
  • 00:32:54
    one that is very important and i'm very
  • 00:32:57
    excited about is the concept of fairness
  • 00:33:00
    so we talked about the risks and reasons
  • 00:33:03
    people don't want to use ai in
  • 00:33:05
    healthcare and and this one comes up a
  • 00:33:07
    lot and it really it any kind of high
  • 00:33:11
    stakes industry it affects that industry
  • 00:33:13
    but i'm really excited about the
  • 00:33:16
    capabilities and the thinking that that
  • 00:33:18
    is evolving around fairness both
  • 00:33:20
    being able to detect bias and unfair
  • 00:33:23
    pieces of the algorithm and then even
  • 00:33:26
    fix that on the fly at scale make
  • 00:33:29
    corrections i think that has the ability
  • 00:33:31
    to allow us to really use data science
  • 00:33:34
    ai and machine learning and healthcare
  • 00:33:36
    but it really brings a ton of value to
  • 00:33:39
    to people to patients and make sure
  • 00:33:42
    they're getting
  • 00:33:43
    care that is fair that we're considering
  • 00:33:46
    things that maybe we haven't been great
  • 00:33:48
    at in the past and maybe this can make
  • 00:33:49
    medicine a bit better or any field a bit
  • 00:33:51
    better so fairness is a huge one for me
  • 00:33:54
    future trends of course i think we're
  • 00:33:56
    going to continue to see
  • 00:33:58
    scale we're going to continue to see a
  • 00:34:00
    bit of a
  • 00:34:02
    i don't want to say a ketchup but we're
  • 00:34:03
    in a nice position to leapfrog other
  • 00:34:05
    industries that have
  • 00:34:07
    really perfected or made a huge a lot of
  • 00:34:10
    the advancement in
  • 00:34:12
    embedding ai into
  • 00:34:14
    every part of their business we can take
  • 00:34:16
    the
  • 00:34:17
    technical learnings and platforms and
  • 00:34:18
    pieces and start from there in
  • 00:34:20
    healthcare and i think we're going to
  • 00:34:22
    see that continue to grow because as we
  • 00:34:24
    start making an impact we're going to
  • 00:34:25
    need to consider how this becomes a core
  • 00:34:28
    part of healthcare
  • 00:34:29
    karen it was great to have you on the
  • 00:34:31
    show do you have any final call to
  • 00:34:33
    action before we wrap up
  • 00:34:35
    you know it is to focus on the impact
  • 00:34:38
    like i just
  • 00:34:39
    always encourage data science and data
  • 00:34:42
    science leaders to think through
  • 00:34:44
    how is this data science solution
  • 00:34:47
    solving a business problem how is it
  • 00:34:48
    making an impact and how is it doing so
  • 00:34:51
    the right way so focus on impact
  • 00:34:54
    understand the context be fair but
  • 00:34:56
    really go all and make a difference
  • 00:34:58
    because data science we're ready for
  • 00:35:00
    that
  • 00:35:01
    thanks for being on data framed no thank
  • 00:35:03
    you thanks for having me
  • 00:35:11
    you've been listening to data framed a
  • 00:35:13
    podcast by data camp keep connected with
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    us by subscribing to the show in your
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    rating leave a comment and share
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    delivering insights into all things data
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    thanks for listening until next time
标签
  • Data Science
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  • Operational Challenges
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