Latest / Becker’s Healthcare Podcast / Daniel M. Sciubba, MD, MBA, Lucille & Milton Cohn Professor and Chair of Neurosurgery at the Zucker School of Medicine at Hofstra/Northwell and Senior Vice President of the Neurosurgery Service Line at Northwell Health

Transcript

Daniel M. Sciubba, MD, MBA, Lucille & Milton Cohn Professor and Chair of Neurosurgery at the Zucker School of Medicine at Hofstra/Northwell and Senior Vice President of the Neurosurgery Service Line at Northwell Health · Becker’s Healthcare Podcast

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  - This is Laura Dirda with the Becker's Healthcare
• 1:01
  - podcast. I'm thrilled today to be joined by
• 1:03
  - doctor Dan Schuba, who's the Lucille and Milton
• 1:06
  - Cone professor and chair of neurosurgery at the
• 1:08
  - Zucker School of Medicine at Northwell Hofstra and
• 1:11
  - senior vice president of neurosurgery service line as
• 1:13
  - well as co director of the Institute for
• 1:15
  - Neurology and Neurosurgery at Northwell Health. Doctor Shuba,
• 1:17
  - it's a pleasure to have you on the
• 1:18
  - podcast today.
• 1:20
  - Thank you so much for the invite. You
• 1:22
  - you run such a wonderful
• 1:23
  - institution, and we all look to listening to
• 1:26
  - you and and our colleagues. So thank you
• 1:28
  - for inviting me.
• 1:30
  - Absolutely. Well, I'm excited to have you on
• 1:32
  - the line because I know there's so much
• 1:33
  - happening in health care today. And particularly, when
• 1:35
  - you're looking at the spinal neurosurgery field, there's,
• 1:38
  - some really exciting things going on. So I'm
• 1:40
  - looking forward to our conversation. But before we
• 1:42
  - dive in, can you tell us a little
• 1:43
  - bit more about yourself and your career journey?
• 1:46
  - What do you have really brought you here
• 1:48
  - to Northwell?
• 1:50
  - Yeah. Thank you for that. So for me,
• 1:52
  - even though I grew up in New Jersey,
• 1:54
  - and this is close to where I I
• 1:55
  - grew up, I as many people had a
• 1:58
  - circuitous route, I was down. I trained at
• 2:00
  - Hopkins for in neurosurgeon, then did a couple
• 2:02
  - fellowships and stayed on his faculty, and then
• 2:04
  - rose through the ranks, to full professor. And
• 2:07
  - at that point, I saw what what I
• 2:09
  - thought great health care was and and still
• 2:11
  - is at Hopkins.
• 2:12
  - What I also saw, however, was that
• 2:15
  - good delivery of health care was not just
• 2:17
  - about academic and and clinical and research, but
• 2:19
  - also the business of medicine, which is often
• 2:22
  - where we can see significant gains for patients.
• 2:24
  - And so I went and got an MBA
• 2:26
  - at Wharton
• 2:27
  - at UPenn, and then right around that time,
• 2:29
  - I started getting phone calls, for chair positions,
• 2:32
  - and I wasn't sure if that's something I
• 2:33
  - wanted to do. But then Northwell seemed to
• 2:35
  - have a combination of both the academics
• 2:38
  - and the business savvy, and it was a
• 2:40
  - very large system. And I thought this would
• 2:42
  - be a nice opportunity to learn and to
• 2:44
  - grow and to really see innovations,
• 2:47
  - not only clinically and research wise, but how
• 2:49
  - health care is delivered
• 2:51
  - is, I think, in some ways, more impactful
• 2:53
  - this day and age, because it's what the
• 2:55
  - patient sees and what the family sees. And
• 2:57
  - so this is where I've been. I've been
• 2:58
  - here five years, and I really do enjoy
• 3:00
  - it. And,
• 3:01
  - I'm looking forward to every new challenge we
• 3:03
  - have with with optimism, actually.
• 3:07
  - That's great to hear. And, you know, really
• 3:08
  - cool that you've had the opportunity to dedicate,
• 3:11
  - your career to spine,
• 3:14
  - spine field and then academic side, clinical and
• 3:16
  - research. I mean, it's such a great opportunity
• 3:19
  - to do a little bit of everything.
• 3:21
  - And I know you've been working a lot
• 3:22
  - with data as well within your practice. Can
• 3:24
  - you tell us a little bit about what
• 3:26
  - that evolution has been like and what you're
• 3:28
  - excited about when you think about data in
• 3:30
  - spine and health care?
• 3:32
  - Yeah. So thank you for that. I you
• 3:34
  - know, it is something that I often apologize
• 3:36
  - for,
• 3:37
  - but I really like numbers. And the reason
• 3:39
  - I like numbers is because I think it
• 3:41
  - tells a story. A classic thing that I
• 3:43
  - tell patients, and I'll see patients later today
• 3:45
  - in clinic, is they'll talk about risk. They'll
• 3:48
  - talk about reward. And surgeons and doctors
• 3:51
  - as just people will often say, you know,
• 3:53
  - this is risky, or I don't think it's
• 3:55
  - that risky. And and I really don't know
• 3:57
  - what that means. Right? You don't you the
• 3:58
  - patient doesn't know what that means, and not
• 3:59
  - that we can ever fully tell them the
• 4:01
  - future. But a classic example is if I
• 4:03
  - said to you, tell me the the your
• 4:05
  - favorite restaurant in your hometown, and you tell
• 4:07
  - me, and then you say, but, Dan, it
• 4:09
  - might actually be pretty expensive.
• 4:11
  - And I might just say back to you,
• 4:12
  - don't tell me it's expensive. Tell me just
• 4:14
  - what the prices are, are, and I'll make
• 4:15
  - the decision. So I think what we do
• 4:17
  - with data is instead of editorializing it, the
• 4:20
  - first thing we do is we just gotta
• 4:21
  - get the data. What is the price? What
• 4:23
  - is the time that they waited? What is
• 4:25
  - the score that they gave? What is the
• 4:27
  - complication, and and how bad was it?
• 4:30
  - What is the recovery time?
• 4:32
  - As opposed to I think you'll do great,
• 4:34
  - and I think you'll do better, and I
• 4:35
  - think this will be in your best interest.
• 4:37
  - I think the patients who are very savvy,
• 4:40
  - about their own life, they know their life
• 4:42
  - and their their constraints and their goals better
• 4:44
  - than we do. And if you tell them
• 4:46
  - this is what you seek to gain and
• 4:47
  - this is what you seek to lose,
• 4:50
  - at this time, and this could change with
• 4:51
  - time,
• 4:52
  - they can make those decisions to the best
• 4:54
  - of their ability. We all can. So when
• 4:56
  - I look at data,
• 4:57
  - it's you know, there's tons of data, but
• 5:00
  - the data in theory is to help us
• 5:03
  - inform our decisions. And the data doesn't make
• 5:05
  - the decisions.
• 5:06
  - It just gives us the vantage point and
• 5:08
  - the and the what I would call the
• 5:09
  - line of sight to make it. And I
• 5:11
  - I will tell you overwhelmingly, except in extremely
• 5:14
  - rare situations of patients maybe who are extremely,
• 5:17
  - disadvantaged or extremely,
• 5:20
  - disabled,
• 5:21
  - patients make very thoughtful decisions,
• 5:24
  - but you have to give them, as much
• 5:26
  - data. And I think physicians make great decisions,
• 5:28
  - but you have to give them the data
• 5:29
  - as opposed to,
• 5:31
  - being told this is in your best interest.
• 5:33
  - Just give them the data that they can
• 5:34
  - make those decisions. That's that's the impetus for
• 5:36
  - it. And then, obviously, you get into the
• 5:38
  - granular stuff, whether it's about,
• 5:41
  - about cost savings, whether it's about value drive,
• 5:43
  - whether it's about programs that are new, whether
• 5:45
  - it's about innovative technology, whether it's about the
• 5:48
  - cost of this technology. Well, tell me what
• 5:50
  - the value of the technology is. Tell me
• 5:52
  - what the values over time, and then the
• 5:54
  - cost curve can be bent. So the the
• 5:56
  - data, the big picture is as what I
• 5:57
  - said is about informed decisions,
• 5:59
  - and then the details become what are you
• 6:01
  - interested in today. And we can make that
• 6:03
  - that decision based on the data.
• 6:06
  - That's amazing here. And, you know, certainly having
• 6:08
  - that information at your fingertips and being able
• 6:10
  - to use it to tell that story to
• 6:11
  - the patients and make the decision about next
• 6:14
  - steps,
• 6:14
  - I can imagine really makes a big difference
• 6:16
  - and and helps along those conversations as well
• 6:18
  - as your relationship and credibility with the patients.
• 6:21
  - And I'm curious. When you look at having
• 6:24
  - so much data, as you mentioned, there's a
• 6:26
  - lot of opportunities to get a a variety
• 6:28
  - of different kinds of information these days, with
• 6:31
  - AI and and more analytics. But how do
• 6:33
  - you really refine into the things that are
• 6:36
  - most important to you and able to kind
• 6:38
  - of,
• 6:39
  - get the right data at the right time
• 6:41
  - and information, so you can pull the levers
• 6:43
  - with patients as well as the decisions you're
• 6:44
  - making in your own practice?
• 6:47
  - Yeah. So it's a great question. So, you
• 6:48
  - know, the way I often think about these
• 6:50
  - things, and it's probably a false duality. It
• 6:52
  - probably does just two things. But I often
• 6:55
  - think about stuff from top down, and I
• 6:57
  - think of things from bottom up. And we
• 6:58
  - all live in both of these, at least
• 7:00
  - I do.
• 7:01
  - Top down is
• 7:03
  - your institution should, quote, unquote, invest in this
• 7:06
  - or should provide this or should be focused
• 7:08
  - on this. Should you have more data available?
• 7:11
  - Should AI be part of your
• 7:13
  - system? Should your leaders be giving you more
• 7:16
  - dashboards
• 7:17
  - so that you can look at this?
• 7:19
  - You ask for it and you might get
• 7:21
  - it. You might get exactly what you asked
• 7:22
  - for and also you have all these dashboards
• 7:24
  - with a million lines of data and you're
• 7:25
  - going, oh, dear. This is this is more
• 7:28
  - than I wanted. In fact, I was in
• 7:29
  - a meeting recently where we were talking about
• 7:30
  - we wanted more data on something. We said
• 7:32
  - to one of our our, data,
• 7:35
  - experts and they said, I can give you
• 7:37
  - more data than than you can imagine. So
• 7:39
  - just tell me what you want. So on
• 7:41
  - one hand, we have to be prepared to
• 7:42
  - say, hey. We want this information from top
• 7:44
  - down. Just be prepared you might get it,
• 7:46
  - and then you have to either learn how
• 7:47
  - to deal with it or you have to
• 7:49
  - learn how to manage it. The second thing
• 7:50
  - is the bottom up, which is it still
• 7:52
  - comes down to the to the left foot,
• 7:54
  - right foot, just getting through the day with
• 7:57
  - patients. So you have all this data, and
• 7:59
  - you put it on population data or your
• 8:01
  - practice data or your region or your market
• 8:04
  - data. And then you're sitting there going, I'm
• 8:05
  - talking to missus Jones. And missus Jones is
• 8:07
  - different or missus Jones is special. And both
• 8:10
  - of those are true. So the question becomes,
• 8:13
  - can you somehow use some of the big
• 8:14
  - stuff top down
• 8:16
  - with this very personal
• 8:18
  - relationship and say, this is where you fit
• 8:20
  - into that without saying, I'm gonna treat you
• 8:23
  - like a number,
• 8:25
  - which is which is not humanistic.
• 8:27
  - But if you treat everyone as if they're
• 8:30
  - a completely different snowflake fingerprint,
• 8:33
  - you make no you make no,
• 8:36
  - you know, gains in the system. You make
• 8:38
  - no gains in the field if every day
• 8:40
  - it's like a new artwork. And you can't
• 8:42
  - learn from other experience going, you know, patients
• 8:44
  - kinda like you who have similar things to
• 8:46
  - you do not like this treatment.
• 8:48
  - They do not like this experience.
• 8:50
  - Doctors like you. I'm not putting you in
• 8:52
  - that category, but maybe a junior doctor who
• 8:54
  - does this thing versus a senior doctor does
• 8:56
  - this thing. Usually, based on the data likes
• 8:58
  - this more, it serves as a template to
• 9:01
  - start the conversation.
• 9:02
  - And I will tell you right now, I
• 9:04
  - don't wanna wear
• 9:05
  - a a a size that one fits all.
• 9:07
  - I want a shirt that fits me the
• 9:09
  - right way. I want it to be to
• 9:10
  - be as,
• 9:11
  - you know, as appropriate for me as possible
• 9:14
  - versus my neighbor. In addition, I might change
• 9:17
  - my size in the next few years. So
• 9:18
  - please don't treat me once and the same.
• 9:20
  - And when I'm 20 years old, you're treating
• 9:22
  - the same way. So that same tailor made
• 9:24
  - approach
• 9:25
  - should exist, but it should exist on some
• 9:27
  - principles.
• 9:28
  - So it's not like you're starting every day
• 9:31
  - as if you'd you know, you're learning for
• 9:32
  - the first time. So top down helps us
• 9:34
  - have principles. Bottom up helps us have the
• 9:36
  - humanistic way. And then it's just about figuring
• 9:39
  - this out together. With regards to AI, it's
• 9:41
  - gonna keep getting more intuitive. We've all seen
• 9:44
  - that.
• 9:45
  - It still has to be applied. It's still
• 9:46
  - a tool. So AI is not gonna solve
• 9:48
  - the problem. It's just gonna be a tool.
• 9:50
  - So if we don't connect with the patient,
• 9:52
  - AI is not gonna help us connect any
• 9:54
  - better. And if we don't think about principles,
• 9:57
  - AI is not gonna tell us what's important.
• 9:59
  - So it's another tool. So I hope that
• 10:00
  - I didn't drone on too long for that.
• 10:03
  - Yeah. Yeah. That's
• 10:04
  - really an helpful way to think about things
• 10:06
  - and, certainly makes a lot of sense when
• 10:08
  - you are bringing those types of data and
• 10:11
  - tools into your practice and and how, you
• 10:13
  - can use them to still,
• 10:15
  - funnel that information, but you as the human
• 10:17
  - are are making those connections and,
• 10:20
  - you know, truly making those decisions of how
• 10:22
  - you,
• 10:23
  - use that information in a smart way, but
• 10:25
  - still personalize and customize,
• 10:27
  - to the patient that's in front of you.
• 10:29
  - And I I'm curious. When you look at
• 10:31
  - AI, I mean, there's so much happening right
• 10:33
  - now. It's a little bit of a wild
• 10:35
  - west out there in terms of the technology
• 10:36
  - evolving. And,
• 10:38
  - how do you think about all of those
• 10:40
  - things and, especially when you look at the
• 10:42
  - next two, three years or so?
• 10:44
  - Is it exciting for you? Is it nerve
• 10:46
  - wracking? How are you, really thinking about,
• 10:49
  - AI and and what the possibilities are for
• 10:51
  - the future?
• 10:53
  - Yeah. So I'll I'll give you some examples.
• 10:55
  - So I'm not talking in complete,
• 10:57
  - theoretical, but then I'll also talk about the
• 10:59
  - theoretical. So the simple things that we have
• 11:01
  - is, you know
• 11:03
  - you know, writing letters, writing,
• 11:07
  - discharge,
• 11:08
  - things, having patients know what they should and
• 11:10
  - shouldn't do when they leave the hospital.
• 11:12
  - These can be done such that it's not
• 11:14
  - the exact same for every patient, but it's
• 11:16
  - not like a handwritten letter that you did
• 11:18
  - that day. But it can come off like
• 11:19
  - a handwritten letter. In other words, you can
• 11:21
  - dial in some things into AI and it
• 11:23
  - can say, you know, so for someone like
• 11:25
  - me, for Dan Schuba, my problem, my age,
• 11:27
  - my issues, my concerns can be addressed and
• 11:30
  - say, if, you know, you know, you can
• 11:33
  - guide them with specific
• 11:35
  - recovery
• 11:35
  - and rehabilitation and warning signs to think about
• 11:38
  - that are not just, hey. Call your doctor
• 11:40
  - if you have a fever. Hey. Call call
• 11:42
  - this person, and call 911 if you have
• 11:44
  - emergency. That's not as helpful. So that's gonna
• 11:46
  - be nice. There are other things where there's
• 11:48
  - there's some efficiencies
• 11:50
  - where patients maybe can be responded to either
• 11:53
  - in clinic or after clinic with
• 11:55
  - some agents, some AI agents that say, oh,
• 11:58
  - what was your question? You had to wait
• 12:00
  - on the phone to talk to someone, but
• 12:02
  - really the question was about what time am
• 12:03
  - I showing up,
• 12:05
  - or where is the parking garage,
• 12:07
  - or I forgot am I taking stopping taking
• 12:09
  - this medicine. Some of those things are gonna
• 12:11
  - be automated, which can can save time. And
• 12:13
  - if it worked for me, if I'm a
• 12:14
  - patient, I'd love it. I do not wanna
• 12:16
  - be talking to a computer. That's dumb. But
• 12:18
  - I also don't wanna wait on the phone
• 12:19
  - forever talking to waiting for a patient a
• 12:22
  - person. So if that can match that, that
• 12:24
  - would be efficient not only for the physicians
• 12:27
  - and the providers,
• 12:28
  - but for me as a patient. In terms
• 12:30
  - of the the and so those are gonna
• 12:31
  - keep happening. These ways to make the clinical
• 12:33
  - faster. And then there's gonna be AI in
• 12:35
  - surgery
• 12:37
  - where everything
• 12:39
  - with time I see being monitored more closely.
• 12:41
  - In other words, we're gonna have cameras everywhere.
• 12:44
  - Everything we're gonna do is is gonna be
• 12:46
  - looked at,
• 12:47
  - at least in the bigger centers to start.
• 12:48
  - And what I mean by that is if
• 12:50
  - you're an athlete, and I think surgery is
• 12:52
  - not that different than that, it's a technique
• 12:54
  - that should be perfected or or worked on,
• 12:57
  - those athletes look at the the video of
• 12:59
  - them after the thing and say, hey. Listen.
• 13:01
  - You were you were dropping your shoulder on
• 13:03
  - that follow through, and that's why the the
• 13:04
  - ball didn't go the way you want it.
• 13:06
  - That's gonna start having an issue. It's not
• 13:08
  - gonna be curated
• 13:10
  - necessarily by surgeons. It's gonna be curated by
• 13:12
  - some automated AI saying, why did you do
• 13:15
  - that step in 10 maneuvers
• 13:17
  - when you can do that step in one
• 13:19
  - maneuver? And that's already being done in some
• 13:22
  - places.
• 13:23
  - I think joints is doing that. They are
• 13:24
  - looking per minute. If anyone's watched the movie
• 13:27
  - f one, I have no disclosure to make,
• 13:29
  - but that's about formula one racing. And they
• 13:31
  - talk about every single difference makes, you know,
• 13:34
  - a tenth of a second in a race,
• 13:36
  - which could be the difference between winning and
• 13:38
  - losing.
• 13:38
  - And we're gonna start doing that for patients.
• 13:40
  - Why not? Why not? If this can help
• 13:42
  - the patient,
• 13:43
  - why wouldn't we say these are little things
• 13:45
  - that can be done differently in the operating
• 13:46
  - room. And right now, it's being done for
• 13:48
  - a lot of the surgeries like the endoscopic
• 13:50
  - ones, the laparoscopic ones where they can videotape
• 13:52
  - the whole thing.
• 13:54
  - There's AI saying you could have made this
• 13:57
  - part more more, more efficient. In terms of
• 13:59
  - the big theoretical push,
• 14:02
  - it it almost blows the mind of what
• 14:04
  - the leaders in AI think is going to
• 14:06
  - happen. In fact, some of my colleagues who
• 14:08
  - are very involved in really some of the
• 14:10
  - the most amazing companies that are pushing this,
• 14:12
  - It's almost hard to imagine when you talk
• 14:13
  - to them. I mean, there's it's so science
• 14:15
  - fiction. But let me just say if what
• 14:17
  - I'm hearing is, it they foresee being so
• 14:19
  - disruptive
• 14:21
  - that, for example,
• 14:22
  - if you went to the financial industry and
• 14:24
  - there's all these analysts
• 14:26
  - that in theory could be, re replaced,
• 14:29
  - there's there's some people saying that maybe 30%
• 14:31
  - of the workforce
• 14:33
  - could be immediately automatized.
• 14:35
  - Now people get nervous about losing a job
• 14:37
  - or being replaced. But the fact of the
• 14:38
  - matter is we're just gonna make humans do
• 14:40
  - other more creative jobs and get away with
• 14:42
  - the the searching or the the, you know,
• 14:45
  - the you know, if your if your job
• 14:47
  - as a junior analyst at a bank is
• 14:49
  - to to check emails and to go on
• 14:51
  - the Internet and find things, well, that's gonna
• 14:53
  - be automated,
• 14:55
  - much faster just like the Internet automated going
• 14:57
  - to the library like I did when I
• 14:59
  - was writing papers. You could now search on
• 15:01
  - the Internet. So they look at truly disruptive,
• 15:04
  - issues. And if we're gonna start having more
• 15:07
  - robotics or not start, continue having more robotics,
• 15:10
  - more AI in the operating room, surgeons are
• 15:12
  - still gonna be involved, but the way we
• 15:14
  - do surgery is gonna be very different. And
• 15:16
  - there's probably some things that we ought to
• 15:18
  - do,
• 15:19
  - that are gonna be automated out of our
• 15:21
  - our of our normal curriculum and our normal
• 15:23
  - day, which I look forward to because there's
• 15:25
  - things that I really enjoy, and all of
• 15:27
  - us have to do things just to get
• 15:28
  - through the day. If those can be automated,
• 15:31
  - I think we'd all benefit, And it would
• 15:32
  - be, in theory, more efficient for the patient,
• 15:34
  - the family, and the doctor, and and, of
• 15:36
  - course, more cost savings potential.
• 15:40
  - I love that. I mean, what
• 15:42
  - a a amazing things that AI is capable
• 15:44
  - of, but also being able to understand how
• 15:46
  - it works within the whole system and then,
• 15:49
  - truly elevating the,
• 15:51
  - human workforce to do the things that they
• 15:53
  - are are best at and and, really is
• 15:55
  - exciting and rewarding and brings that fullness within
• 15:58
  - their careers. So, doctor Schubert, thank you so
• 16:01
  - much for joining us on the podcast today.
• 16:02
  - This has been such a, invigorating discussion. I
• 16:05
  - appreciate your time, and I look forward to
• 16:07
  - connecting with you again soon.
• 16:09
  - Thank you. This was a delight, and I
• 16:11
  - look forward to all the things that you
• 16:12
  - do with your great institution. And, on behalf
• 16:15
  - of all the surgeons out there, we thank
• 16:17
  - you. So thanks a lot.