Latest / Becker’s Healthcare Podcast / Turning Healthcare Data Into Actionable Clinical Insights with Dr. Tinu Tadese

Transcript

Turning Healthcare Data Into Actionable Clinical Insights with Dr. Tinu Tadese · Becker’s Healthcare Podcast

• 0:00
  - What we wanna do with AI is help
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  - clinicians go from being reactive
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  - to proactive.
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  - At Philips, we can recognize patterns in monitoring
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  - data across populations
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  - and apply those insights to specific patients.
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  - Safe and responsible AI can support sharper insights,
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  - pave the way for faster diagnosis,
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  - and help streamline care.
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  - That can benefit clinicians, it can benefit health
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  - systems, and most importantly, it can benefit patients
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  - and their families.
• 0:33
  - Phillips.
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  - Hello, everyone. This is Jeremy with the Becker's
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  - Healthcare Podcast.
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  - Thank you so much for tuning in to
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  - the Becker's Healthcare Podcast series recorded live at
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  - the eleventh annual IT and revenue cycle conference,
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  - the future of AI and digital health. I'm
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  - joined today by doctor Tinu Tadesha,
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  - system chief medical informatics officer at Boston Medical
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  - Center.
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  - Tinu, how are you doing today? I am
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  - doing well, Jeremy. Thank you very much for
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  - having me on this podcast. Can Can you
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  - take a brief moment to introduce yourself and
• 1:00
  - tell a little bit about your, career journey
• 1:01
  - so far?
• 1:02
  - Thank you, Jeremy. My name again is Tinu,
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  - and,
• 1:06
  - I have been,
• 1:07
  - in informatics for quite a bit now. I
• 1:10
  - am a pediatrician
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  - by training.
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  - And right now, I am vice president and
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  - system chief medical information officer at Boston Medical
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  - Center.
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  - Over the... My career, I have been on
• 1:22
  - the vendor side of things being,
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  - executive physician at Sona, which is now Oracle
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  - Health.
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  - And then I've then gone into roles such
• 1:31
  - as VP,
• 1:33
  - chief medical information officer
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  - in three or four health systems.
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  - My bent is really operational informatics,
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  - how we make technology
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  - work for us. Mhmm.
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  - As clinicians,
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  - I sit in that space between IT
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  - and the clinical space and operations.
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  - So that's what I do today. I'm excited
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  - to be here.
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  - Awesome. Well, health systems are receiving a growing
• 1:59
  - stream of patient data from bedside monitoring,
• 2:02
  - ambulatory devices, and care delivered at home.
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  - Where is that data creating actionable insights for
• 2:08
  - clinicians,
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  - and where does that still create more work
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  - than insight?
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  - That's a great question. We have data streaming
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  - in into the electronic health record or different
• 2:18
  - applications in our health systems.
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  - Right now, we have actionable insights in areas
• 2:24
  - such as,
• 2:25
  - continuous glucose monitoring.
• 2:27
  - That's an example where a patient
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  - wears what we call the CGM
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  - patches, and we start to find out what
• 2:35
  - their blood glucose level is like,
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  - either at home, at night,
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  - or after a meal. We're able to act
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  - on that when that patient comes in because
• 2:45
  - we have the data in front of us.
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  - Or take take,
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  - bedside monitoring,
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  - a patient in the ICU.
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  - This is how we are able to track
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  - their data. Are they doing well? Are they,
• 2:57
  - decompensating?
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  - So right now, we have actionable data from
• 3:01
  - different data sources, and,
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  - we're able to use them.
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  - We're starting to find areas where we just
• 3:09
  - have data. Having data available does not mean
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  - they're actionable. It just means more work Mhmm.
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  - For the clinician.
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  - So when clinicians are trying to reach a
• 3:17
  - diagnosis, they often must piece together information from
• 3:20
  - radiology, cardiology, pathology, urology, and other systems. Where
• 3:24
  - does that fragmentation create the most friction in
• 3:27
  - your organization,
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  - and how do you bring together data sources
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  - and address that fragmentation or close these gaps?
• 3:33
  - Yeah. That's a great question.
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  - Again,
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  - the... Right now, take thirty years ago or
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  - maybe twenty years ago when almost all health
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  - systems were
• 3:44
  - on,
• 3:45
  - paper.
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  - You didn't have that problem. But what you
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  - also did not have was ability to synthesize
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  - data. It took you a long time as
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  - a physician
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  - patient.
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  - Now we have data streaming in from everywhere.
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  - We have imaging. We have cardiac data. We
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  - have just regular wearables where you're monitoring
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  - your,
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  - your steps,
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  - blood blood pressure,
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  - monitors, or even your bathroom scale is speaking
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  - to you. Now the fragmentation
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  - is there, and then the question is, what
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  - is the clinician supposed to do with this?
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  - How do they walk with this to be
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  - able to provide
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  - safe patient care? The patient sitting in front
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  - of you, what do I do with all
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  - this data? So that fragmentation
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  - is certainly there, and every day we have
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  - more data being produced. I'll take for example,
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  - one of the things we went through at
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  - our health system.
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  - We acquired two community hospitals, and we are
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  - mainly an academic
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  - health center. We had to,
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  - onboard these community hospitals at a very compressed
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  - timeline,
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  - something that should have taken eighteen months, we
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  - did in six months. The question then is
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  - if a patient walks into
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  - a ER day one after go live, all
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  - this data
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  - from three different hospitals,
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  - what do we need and what can we
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  - do?
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  - Do we have enough data?
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  - We had that challenge because data takes time
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  - to ingest,
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  - and so we have to determine
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  - what is necessary by each service line. So
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  - if I'm cardiology and a patient comes into
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  - the ER, what must I do? So what
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  - can I migrate
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  - deliberately into the EHR
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  - from the from the current EHR to our
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  - own new?
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  - Which is new to the community hospitals?
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  - Imaging,
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  - radiologist has seen that patient, is trying to
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  - figure out
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  - what was the prior image
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  - oncology
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  - cancer, what was the previous pathology test. All
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  - of these are fragmented data, and we had
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  - to deliberately, intentionally
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  - determine if I were the patient walking into
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  - the ER, what would my doctor need to
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  - see me? So we had to deliberately pull
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  - all of that together, what stays in archival
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  - status, what's in an interim status, and what
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  - must be in the EHR on day one.
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  - So fragmentation is a big problem. Again, having,
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  - data available
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  - does not mean that the patient is safe
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  - or that you can deliver safe care. You
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  - actually literally
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  - have to deliberately determine the patient journey and
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  - what will be needed Mhmm. At that time.
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  - Mhmm.
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  - And so patients are, they increasingly move among
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  - the hospital, outpatient settings, and home. Where does
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  - clinical context most often get lost during those
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  - transitions, and what would it take to give
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  - care teams a more continuous view of the
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  - patient?
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  - That's a great question.
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  - I refer to that project that we had,
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  - at our health system where we had to
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  - ingest data. Now think about this patient who
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  - has been hospitalized
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  - in one of our three hospitals.
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  - This patient then goes back home, and the
• 6:56
  - admitting physician or discharging physician say see your
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  - primary care physician at such and such a
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  - date.
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  - The challenge
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  - is what's the context? Mhmm. Did that patient
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  - actually follow-up? These are the things that are
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  - lost in translation.
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  - A test was ordered. Who actually follows up
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  - to make sure that that test was read?
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  - Oh, a medication that the patient was on
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  - previously was stopped.
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  - Well, is that patient supposed to start taking
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  - it after,
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  - a period of time? It's context that is
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  - lost Mhmm. In transitions of care. Not necessarily
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  - the data, but why do we have this
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  - data? And that's what we have to pay
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  - rigorous attention to. Otherwise, then we cannot deliver
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  - safe patient care.
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  - Patient are left holding
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  - the bag, really. They... And they... The physician
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  - is asking, why did your doctor stop this?
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  - They don't know. And so this is why
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  - that's... We need to pay attention to clinical
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  - context and continuity,
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  - when we're changing care settings and care teams.
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  - Mhmm. Mhmm.
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  - And with so many AI use cases
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  - competing for attention, how do you distinguish a
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  - compelling pilot from something ready to become part
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  - of everyday care, and what must be in
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  - place to scale it responsibly?
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  - That's a great question. Slightly different from what
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  - we have been, talking about. But, of course,
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  - in the... In this,
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  - time of AI,
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  - we have every pilot you can think of
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  - coming through... Well,
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  - proposals for pilots coming through
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  - through our doors and, AI committee
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  - having to wait through. So
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  - the question is,
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  - was that pilot, the one we determined to
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  - start, was it successful? So you have to
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  - now
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  - look back to what was the original need
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  - or
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  - problem you were trying to solve? Did it
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  - solve it? What were the KPIs you were
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  - looking? Was it a financial,
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  - solution you were looking for? Was it clinical?
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  - Was it operational?
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  - If the pilot is able
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  - to actually solve that, then we say, yes.
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  - This pilot is successful. However, that does not
• 9:07
  - mean that it's scalable. Mhmm. Being... Having a
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  - successful pilot does not mean that operations
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  - are ready
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  - for what it needs to take. So you...
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  - Maybe you you piloted with 30 clinicians.
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  - 30 physicians use this pilot. Now you wanna
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  - scale to 2,000 physicians. Mhmm. Do we have
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  - all the support staff in place? Do we
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  - have operational leaders in place?
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  - Do we have the KPIs we're looking for?
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  - Who is gonna monitor? Who is going to
• 9:37
  - determine that this is still safe there? AI,
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  - for example,
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  - these are models that change on the server
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  - side that you have no control over Mhmm.
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  - In your health system. Who's going to continuously
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  - monitor this? So when you think about all
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  - of this,
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  - a successful pilot does not mean it's ready
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  - for scale. But if you can guarantee that
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  - we have the operational readiness for it, then
• 10:02
  - it's time to scale. Okay.
• 10:04
  - And as technology,
• 10:06
  - relationships
• 10:07
  - move beyond one time purchases,
• 10:09
  - what should a partner remain accountable for after
• 10:12
  - go live to make the relationship genuinely,
• 10:15
  - valuable over time?
• 10:16
  - That's a great question. Remember, part of my
• 10:18
  - history or my journey in informatics was I
• 10:21
  - did work on the vendor side, which where,
• 10:24
  - what is now Oracle Health. And and so
• 10:27
  - I kinda,
• 10:28
  - have an idea of the things that you
• 10:30
  - really should bring to my table as a
• 10:32
  - system CMIO.
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  - I wanna see you walk with me, stay
• 10:37
  - with me to make sure that the value
• 10:39
  - we determined we were able to actually actually
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  - happens. Mhmm. Don't leave me in the lodge.
• 10:44
  - Don't tell me that the technology works. Go
• 10:47
  - figure it out. Mhmm. And on the flip
• 10:49
  - side of that, the vendor does not own
• 10:51
  - your operational readiness. The vendor does not own
• 10:54
  - your change management.
• 10:55
  - The vendor can advise and say to you,
• 10:58
  - in other... With our other customers, this is
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  - what we've seen that was successful. So bring
• 11:03
  - me that intelligence, bring me that insight so
• 11:06
  - that I can continue to succeed. So it...
• 11:08
  - It's a partnership like you mentioned. They are
• 11:11
  - partners,
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  - and we need to do our part. They
• 11:14
  - need to do their part, but they need
• 11:16
  - to make sure that what we said was
• 11:18
  - gonna happen actually happens. At least work with
• 11:20
  - us to make sure it happens. Right. Right.
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  - Right. And as care increasingly moves beyond hospital
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  - walls, how are you using connected technologies and
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  - AI to identify patients who need earlier follow-up,
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  - support care coordination teams, and close gaps in
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  - post discharge care? That is great. If if
• 11:36
  - we... There's a lot of this...
• 11:39
  - What we talk about technology, but what we
• 11:41
  - really wanna talk about really is, do you
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  - have the workflows in place? Mhmm. Because it's
• 11:46
  - very easy to build,
• 11:49
  - the
• 11:50
  - technology that would alert you that a patient
• 11:53
  - is deteriorating.
• 11:54
  - Mhmm. The question then is,
• 11:56
  - who
• 11:57
  - picks up that alert? Right. Who does
• 12:01
  - what needs to be done when that patient
• 12:03
  - is deteriorating?
• 12:04
  - Who is responsible?
• 12:06
  - And so, yes, we have technologies. For example,
• 12:09
  - we admission.
• 12:10
  - Do we have the risk,
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  - algorithms in place so that we can detect
• 12:15
  - even before the patient
• 12:17
  - leaves the hospital, this patient is probably gonna
• 12:19
  - be readmitted? And if that's the case, what
• 12:22
  - is our follow-up to make sure the patient
• 12:24
  - is not readmitted?
• 12:25
  - Is it a social determinant of health issue,
• 12:28
  - or is it the fact that this patient
• 12:30
  - just is noncompliant?
• 12:31
  - Mhmm. Since we already know that technology alerts
• 12:35
  - us based on algorithms we have developed, What
• 12:37
  - then do we do with it? And so
• 12:39
  - it's not just technologies, both technology
• 12:42
  - and operational workflows and responsibility on the care
• 12:45
  - team. Right. Right.
• 12:47
  - Well, I wanna thank you so much again
• 12:49
  - for joining us today and sharing your insights.
• 12:52
  - We also wanna thank our podcast sponsor, Philips.
• 12:54
  - You can turn into more podcasts from Becker's
• 12:56
  - Healthcare by visiting our podcast page at beckershospitalreview.com.
• 13:00
  - Thank you so much, Jeremy. I'm glad to
• 13:01
  - have come on your podcast. Thank you so
• 13:03
  - much. It was an absolute pleasure.