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Will computers replace doctors? · Stuff You Should Know

0:00
Speaker 1: Welcome to you stuff you should know from house Stuff
0:04
Speaker 1: Works dot com. Hey, and welcome to the podcast. I'm
0:12
Speaker 1: Josh Clark. There's Charles W. Chuck Bryant. Our guest producer
0:15
Speaker 1: Noel is here. Yeah, Jerry needs a buffer day from
0:19
Speaker 1: her Christmas break. I can't say that again. No, she's
0:23
Speaker 1: at home on her buffer day. And the freezing cold
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Speaker 1: because we record these somewhat in advance, we are in
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Speaker 1: the midst of the polar vortex and um, yeah, everyone's
0:34
Speaker 1: just talking about how cold is We're back. It is
0:36
Speaker 1: our first recording after the holidays. It's literally freezing cold up.
0:40
Speaker 1: So welcome back, buddy, Thanks, Welcome back to you too.
0:42
Speaker 1: Even though this will be what like late January, it
0:45
Speaker 1: will be a balmy sixteen Atlanta. I think the end
0:47
Speaker 1: of the end of January is when this one comes out.
0:49
Speaker 1: All right, Well, happy New Year, and happy New Year
0:52
Speaker 1: to YouTube, and happy holidays to you. Thanks. Um Chuck, Yes, sir,
0:57
Speaker 1: you're feeling good. You're loose, You're ready. I'm loose. So
0:59
Speaker 1: you see this. You've seen this before. Yeah, you're fit.
1:02
Speaker 1: My fit bit is that buzz marketing? Not really, It's
1:05
Speaker 1: just a really good example. Um I've felt like Fitbit,
1:09
Speaker 1: I'm not like necessarily loyal to it or anything like that.
1:13
Speaker 1: They don't pay me money to mention the podcast. Sometimes
1:17
Speaker 1: it's be like, stop staring at me fit Bit. Um.
1:20
Speaker 1: But no, I like it. I'm happy with it. Um.
1:23
Speaker 1: I I pointed out though, because it's part of this
1:27
Speaker 1: to me, and I don't think it's over confirmation bias.
1:29
Speaker 1: It seems like there really is a growing desire among
1:34
Speaker 1: just average ordinary people to be able to track there
1:40
Speaker 1: their health, their well being, their activity, um, and to
1:44
Speaker 1: do it easily. Yeah, we have tools now that make it,
1:47
Speaker 1: like that thing super convenient. Yeah, it's and Fitbit's not
1:51
Speaker 1: the only one. There's like Nike fuel Band. There's Jawbone
1:53
Speaker 1: is another really good one. There's others like um that
1:56
Speaker 1: track uh, your galvanic response, so they're able to put
2:01
Speaker 1: that together with respiration and heartbeat and come up with
2:04
Speaker 1: a pretty good assessment of how many calories you're burning
2:07
Speaker 1: at any given time, which is like kind of a
2:09
Speaker 1: holy grail with this kind of thing right now. Um,
2:12
Speaker 1: there's others that check your sleep. There's apps out there
2:15
Speaker 1: that let you um check your mood. Um. There's sites
2:19
Speaker 1: like Quantified Self which are basically like people trying to
2:23
Speaker 1: push wearable technology like this further into the future. There's
2:28
Speaker 1: entire websites like share care dot com that are dedicated
2:31
Speaker 1: to health information and health um uh support self advocacy. Yeah,
2:38
Speaker 1: and there's this. It seems to me, this desire to
2:41
Speaker 1: kind of say, hey, this is my health, this is
2:43
Speaker 1: my body. I want to know more about it, you know,
2:47
Speaker 1: like I don't want to necessarily cut out doctors, but
2:51
Speaker 1: I I I want to decide if I should go
2:54
Speaker 1: to the doctor, if it's time or not, and I
2:56
Speaker 1: want to use data to do that. Yeah. I imagine U.
3:00
Speaker 1: I frustrate a lot of doctors because I'm one of
3:02
Speaker 1: those obnoxious people that goes in and it's like, well,
3:06
Speaker 1: here's what I think I have based on my research.
3:09
Speaker 1: There's nothing wrong with that. Yeah, that is what you're
3:12
Speaker 1: an informed patient. That's true, exactly what you're supposed to do.
3:14
Speaker 1: And if you're getting on your doctor's nerves, then go
3:17
Speaker 1: see another doctor. Yeah, I agree. Actually in search of
3:21
Speaker 1: a new GP right now for those because and others
3:24
Speaker 1: you got on his nerves other reasons too. Really cold
3:28
Speaker 1: cold hands like poor bedside man are never seen the doctor,
3:31
Speaker 1: like here's my intern from Emory. Yeah, which great. You know.
3:37
Speaker 1: I love them getting experienced, but I would like them
3:39
Speaker 1: both to be in there, not just like smell you
3:41
Speaker 1: later and the doctor leaves. Well that's another thing too.
3:44
Speaker 1: It's kind of like, um, doctor, okay, let's let's just
3:48
Speaker 1: lay it on on the table here. Yeah, what what
3:50
Speaker 1: you've just mentioned and what I was talking about. If
3:52
Speaker 1: you put it all together, the medical field physicians in particular,
3:58
Speaker 1: are currently in a what's the beginning of what's possibly
4:02
Speaker 1: a really pickle of a state for them. I think
4:06
Speaker 1: a transition period, yes, but they may be transitioned right
4:09
Speaker 1: out of existence in large parts. Yes, some may for sure,
4:12
Speaker 1: depending on who you talk to. There's like this whole
4:14
Speaker 1: question now, like what is the future of medicine and
4:19
Speaker 1: more specifically, in the case of this episode that we're
4:21
Speaker 1: talking about, do human physicians factor largely into that future?
4:27
Speaker 1: And the answer is I don't know, yeah, depending on
4:32
Speaker 1: who you ask, Like I said, um, we there's this
4:34
Speaker 1: one guy, Dr Kent Bottles, who um he feels that
4:40
Speaker 1: GPS might go the way of the DODO and be
4:43
Speaker 1: replaced by diagnostic computers, maybe with avatars. Then there's other
4:49
Speaker 1: people like Farhad Manjou's a technical writer, his wife as
4:52
Speaker 1: a pathologist. He thinks, no, no no, no, the gps are
4:55
Speaker 1: the ones that are going to be in business. The
4:57
Speaker 1: specialists are gonna be out of business because computers are
5:00
Speaker 1: really good at specializing on one single thing, not maybe
5:03
Speaker 1: so good at a general practitioner thing. So there's lots
5:08
Speaker 1: of opinions out there on how much they'll be replaced
5:11
Speaker 1: and who might be replaced. Right then, As Reclined wrote
5:14
Speaker 1: a column who basically he basically said like no, like
5:17
Speaker 1: we we will still need humans, but we mainly need
5:20
Speaker 1: humans to communicate to the other humans and facilitate the
5:25
Speaker 1: the interaction between the robots and the humans. And we
5:27
Speaker 1: already have this. They're called nurses or nurse practitioners. Yea.
5:31
Speaker 1: But he kind of as Reclined is the one that
5:33
Speaker 1: thought that a computer avatar might have a better bedside
5:36
Speaker 1: manner than a doctor. Well, let's give that one example.
5:39
Speaker 1: There's this, there was there's an example I kept finding
5:42
Speaker 1: while we were doing research for the sentence. Actually in
5:44
Speaker 1: the article on how stuff works. Um, it's uh. There
5:48
Speaker 1: was a kiosk, a medical kiosk, during a panel called
5:52
Speaker 1: man Made Minds colon living with thinking machines. When there's
5:56
Speaker 1: a colon in there, you know it's serious stuff. Um.
5:59
Speaker 1: And it was at the World Science Festival in two
6:00
Speaker 1: thousand eleven, and basically this, this computerized avatar UM interacted
6:06
Speaker 1: with a woman whose baby had diarrhea, and the woman said, hey, Avatar,
6:12
Speaker 1: my baby has diarrhea. What are you gonna do about it?
6:15
Speaker 1: And the avatar said, well, tell me all the symptoms
6:18
Speaker 1: and all this stuff, and avatar decided that the baby's diarrhea,
6:22
Speaker 1: while present, wasn't severe enough to warrant immediate medical attention,
6:26
Speaker 1: so it went ahead and made an appointment with a
6:29
Speaker 1: human doctor for later on that week. And the mother
6:33
Speaker 1: said that she preferred the treatment by the avatar to
6:37
Speaker 1: the real life nurses at the hospitals where she lived
6:41
Speaker 1: in New York. Yeah. Uh, so it is possible to
6:44
Speaker 1: create computers with better bedside manner than say, your g P. Well,
6:49
Speaker 1: it's at the very least it will be consistent. And
6:52
Speaker 1: that's one of the things that I'm not poopooing. Doctors
6:55
Speaker 1: or nurses are many, many, many, many great ones, but
6:58
Speaker 1: I've also had some pretty bad experiences, as in emergency
7:01
Speaker 1: rooms and with doctors and nurses with a computer. At
7:03
Speaker 1: least it's a consistent you know, their program to display
7:07
Speaker 1: empathy no matter what. You know, they're not too busy
7:11
Speaker 1: and they're not you know, having a bad day, so
7:14
Speaker 1: they they don't you know, they don't have any prejudices
7:17
Speaker 1: against you personally or anything like that. They're a computer.
7:20
Speaker 1: They don't hate diarrhea. But humans, humans respond to even
7:25
Speaker 1: programmed empathy, even synthetic empathy from a computer. I could
7:31
Speaker 1: see that a little bit. Like I've dove into the
7:36
Speaker 1: gaming world enough to know that, you know, the realism
7:40
Speaker 1: of a of a avatar can be convincing, and it's
7:44
Speaker 1: not like you think, oh, it's a real person, but
7:45
Speaker 1: it helps to put a human face on it, you know,
7:48
Speaker 1: exactly literally. Um, they saw a reference to a study
7:52
Speaker 1: that found um, people who are being treated for anxiety
7:57
Speaker 1: disorders tended to share more about their experiences and themselves
8:02
Speaker 1: with an avatar them with a human psychiatrists. Oh that's
8:06
Speaker 1: interesting because they're like not embarrassed to tell a real person. Yeah,
8:10
Speaker 1: that makes sense. I might open up more to to
8:12
Speaker 1: a computer. Right, So so we've got that part, like
8:14
Speaker 1: the bedside manner, it is possible that we can create
8:18
Speaker 1: machines now and are creating machines now that have at
8:22
Speaker 1: least equal, if not better bedside manner than some physicians. Yeah, okay,
8:26
Speaker 1: so bedside manner one of the big things that doctors
8:29
Speaker 1: bring to the table. Check computers have that. Yeah, it's
8:33
Speaker 1: it's different now than it was in the old days.
8:35
Speaker 1: I feel like just the whole quality of personal care
8:39
Speaker 1: is gone down. It's not necessarily the doctor's falts. There's
8:41
Speaker 1: a lot of reasons to place the blame, but it's
8:44
Speaker 1: not like when you were a kid and you feel
8:45
Speaker 1: like you had your family doctor who knew you, maybe
8:48
Speaker 1: even gave birthth you're my son exactly. Um, it just
8:55
Speaker 1: invested like you gotta stick with the same doctor if
8:58
Speaker 1: you want that kind of care, I think, right, And
9:00
Speaker 1: is another benefit besides bedside banner um that comes with
9:03
Speaker 1: that that kind of care, that kind of personal care
9:07
Speaker 1: is an awareness of your medical history. Not just that
9:11
Speaker 1: but oh well, your dad died of congenital heart disorders
9:15
Speaker 1: like that, so you may be at higher risk of it.
9:18
Speaker 1: To just that kind of awareness has been typically lost too.
9:22
Speaker 1: Even though we have medical histories and they're in our
9:24
Speaker 1: charts that they're in our files UM, and intimate knowledge
9:27
Speaker 1: of a patients UM medical history is pretty much lost
9:33
Speaker 1: in today's modern practice of medicine. UM. That's another thing
9:38
Speaker 1: that that computers could conceivably top doctors on UM, which
9:44
Speaker 1: basically falls under the umbrella of diagnosis or diagnostics. Yeah.
9:49
Speaker 1: I mean there's two two sides to this. There's diagnoses
9:51
Speaker 1: and treatment and some UH programs. A little bit of
9:56
Speaker 1: the history this UM goes back to the nineteen seventy
10:00
Speaker 1: is at the University of Pittsburgh. They develop software to
10:03
Speaker 1: diagnose problems. UM. MASS General since the eighties has been
10:07
Speaker 1: working on their d X plan, which provides ranked lists
10:12
Speaker 1: of diagnoses, whereas the what's the computer the Watson Watson
10:18
Speaker 1: who who won a Jeopardy Yeah, that's more based UM.
10:21
Speaker 1: It looks like on treatment options than diagnosis at this point.
10:26
Speaker 1: So well, yeah, but they said it's not They haven't.
10:29
Speaker 1: I don't think they want to leave it alone with
10:31
Speaker 1: diagnosis yet. No, and to do its thing there there's
10:34
Speaker 1: already something out there for diagnosis that's meant to support physicians.
10:41
Speaker 1: From what I understand with Watson, if there is a
10:44
Speaker 1: doctor of the future, it's Watson. Yeah. UM, he has
10:49
Speaker 1: a lot of advantages over not just um human doctors,
10:52
Speaker 1: but other artificial intelligence healthcare machines. I guess you could
10:59
Speaker 1: clumsily call he has a knack for natural language. So
11:04
Speaker 1: let's say there's like a structured formula or formulaic type
11:10
Speaker 1: of language that the medical field is supposed to use, right, yes, okay, um,
11:17
Speaker 1: health records don't always necessarily contain that language. They might
11:21
Speaker 1: contain natural language, which is really confusing for computers to
11:25
Speaker 1: take in an absorb. Yeah. You know, humans can pick
11:30
Speaker 1: up on meanings of things that robots and and software cannot,
11:35
Speaker 1: like inferences, and we might be using sarcasm, although there's
11:38
Speaker 1: probably not going to be any sarcasm in your medical records. Yeah,
11:42
Speaker 1: but like figurative language and stuff like that. But computers
11:45
Speaker 1: a language is a big part of the problem. Or
11:47
Speaker 1: more to the point, with the diagnosis, patient says he
11:50
Speaker 1: feels like he has a hive of bees in his stomach.
11:55
Speaker 1: Like that might mean something that you or me, but
11:57
Speaker 1: to a computer it's like followed the two bees or something. Right,
12:01
Speaker 1: Watson has the advantage of saying, Okay, well there's a
12:04
Speaker 1: sensation of bees in the stomach, there's not actually bees
12:07
Speaker 1: in the stomach. So let's figure this out then, Watson
12:11
Speaker 1: or anything that that he eventually becomes. UM, Well, we'll
12:17
Speaker 1: be able to go through medical records, current medical research, UM,
12:21
Speaker 1: the patient's medical history, uh, diagnostic tests that were done,
12:26
Speaker 1: blood work, instrument tests, and put it all together and
12:30
Speaker 1: then spit out a list of diagnoses with different confidence levels.
12:36
Speaker 1: So the one at the top is the one that
12:37
Speaker 1: Watson says is he is ninety eight point percent sure
12:42
Speaker 1: is what's wrong with this patient? And UM as a diagnostician,
12:48
Speaker 1: that's pretty impressive. And that's using all the available data
12:51
Speaker 1: that's that's available also to human physicians, but they simply
12:57
Speaker 1: don't have the time to make it all in. Yeah.
13:01
Speaker 1: I think some research said that eighty percent of doctors
13:04
Speaker 1: spend less than five hours a week reading medical journals
13:08
Speaker 1: a month a month. Yeah, so that's these things can
13:12
Speaker 1: read thousands in seconds. So it's it's sort of a
13:16
Speaker 1: matter of of efficiency really, and like if doctors don't
13:20
Speaker 1: have time to read all this stuff, I know, we
13:23
Speaker 1: we looked into this one. Uh sort of a savant
13:26
Speaker 1: diagnoser is that a word? Diagnostician? Uh? Doctor uh Dolly
13:33
Speaker 1: Wall in San Francisco. He's sort of legendary for diagnosing things,
13:38
Speaker 1: to the point where he does it on stage as
13:40
Speaker 1: almost like a parlor trick would do. They give him
13:44
Speaker 1: forty five minutes and and a bunch of symptoms basically
13:49
Speaker 1: like really confusing because they're trying to stump him, and
13:52
Speaker 1: generally he comes out on top. But he even uses
13:56
Speaker 1: a program, a diagnostic program called Isabelle. Right, that's the
13:59
Speaker 1: one I said every here that's already here. So doctors
14:02
Speaker 1: are using these to help themselves out. But he says
14:05
Speaker 1: that he's never had Isabelle offer a diagnosis that he
14:09
Speaker 1: has missed. Right, But he's like the dude that yeah,
14:12
Speaker 1: And he also admits that he's like like I'm a
14:15
Speaker 1: freak of nature. Go ahead, quiz me exactly. He also
14:18
Speaker 1: reads like case histories, like for fun, that kind of stuff.
14:22
Speaker 1: He's not, he's not a normal physician. He's a complete
14:24
Speaker 1: and total outlier. Um, if he were. If every physician
14:30
Speaker 1: were like this guy, then they're there probably wouldn't be
14:34
Speaker 1: this conversation going on right now. But most physicians aren't.
14:38
Speaker 1: And it's not just with current medical research that they're
14:41
Speaker 1: just not aware of because they haven't had time to
14:43
Speaker 1: pick up the lancet the last few months, but it's
14:45
Speaker 1: also their training to Like, if a doctor is in
14:47
Speaker 1: practice for twenty years, the brain, and the human brain
14:51
Speaker 1: tends to create habits because it likes to expend as
14:54
Speaker 1: little energy as possible. It's it's trying to be as
14:57
Speaker 1: ficient as possible. And I think the same thing happens
15:00
Speaker 1: with medical practice. You're trained, you understand, you come out
15:03
Speaker 1: of medical school with a lot of book learning, and
15:06
Speaker 1: then you put it to practice and you kind of
15:08
Speaker 1: find your niche and along the way you forget a
15:10
Speaker 1: lot of the stuff that you haven't done in twenty
15:12
Speaker 1: years or haven't learned about in twenty years. So it's
15:14
Speaker 1: not just current stuff, it's old stuff too. And if
15:17
Speaker 1: you feed the physician's desk reference into Watson or one
15:20
Speaker 1: of his his compatriots, like all of that knowledge can
15:24
Speaker 1: be quickly index and research to try to spit out
15:28
Speaker 1: a more accurate diagnosis. Yeah, I think that's a great idea.
15:32
Speaker 1: It's like a partnering up with computers. It is sarily replacing,
15:36
Speaker 1: but what they're doing with Watson is is very much
15:39
Speaker 1: moving towards replacing doctors in that sense. Well, here's a
15:42
Speaker 1: scary stat um one in five diagnoses in the United
15:45
Speaker 1: States are incorrect or incomplete one in five and a
15:50
Speaker 1: lot of times. It's not that the doctor's a jerk
15:53
Speaker 1: or not any good, but like you said, they just
15:55
Speaker 1: maybe haven't seen these cases that were written about in
15:59
Speaker 1: some obscure medical journal that the computer has scanned an index.
16:03
Speaker 1: Ya and Dolly Wall Dr Dolly Wall himself at Freak
16:06
Speaker 1: Diagnostication Dollywood, Yeah, pretty close, which is a wonderful place.
16:10
Speaker 1: By the way, um Dr Dolly wall Uh himself says
16:16
Speaker 1: a lot even with me, A lot of it is intuition,
16:20
Speaker 1: and intuition can be wrong. That's a criticism though, of
16:25
Speaker 1: computers as doctors. They lack intuition. Like there's kind of
16:29
Speaker 1: even a larger even larger than this computer's replacing doctor's
16:33
Speaker 1: conversation going on. It's kind of a conversation or a
16:38
Speaker 1: debate over whether intuition or data. Yeah, Trump's one or
16:43
Speaker 1: the other. Which one is the right way to go? Yeah?
16:47
Speaker 1: This one step too, it says according to an expert,
16:49
Speaker 1: I'm not sure what that means. That sounds sinky, but
16:52
Speaker 1: they said, only of the knowledge of physicians use to
16:55
Speaker 1: diagnose is evidence based, so that means intuition, which which
17:02
Speaker 1: also jibs in dovetails with that one in five being
17:05
Speaker 1: wrong or one in five being right. I'd like the
17:08
Speaker 1: idea of intuition to a certain degree, for sure, but
17:11
Speaker 1: there's also got to be like data backing it up. Sure,
17:15
Speaker 1: you know so in your perfect world. Then it sounds
17:18
Speaker 1: like we still have physicians, but they go back and
17:21
Speaker 1: double check themselves using a program. Yeah, but I could
17:25
Speaker 1: also be down with um simple what is it? What
17:29
Speaker 1: do they call it in here? Um? Something based diseases,
17:35
Speaker 1: rules based chronic diseases. Yeah, like minor things that are
17:39
Speaker 1: pretty easy to diagnose, so they're not even necessarily minor.
17:42
Speaker 1: We just understand them so fully that we say type
17:45
Speaker 1: two diabetes is going to behave and present itself like this. Yeah,
17:48
Speaker 1: but I wouldn't mind going like it seems like once
17:51
Speaker 1: a year I get, like an upper respiratory infection. It's
17:53
Speaker 1: been three or four years in a row, and I
17:55
Speaker 1: know what the treatment is, I know how it feels.
17:58
Speaker 1: It would be great to go and to a machine
18:00
Speaker 1: and have them take some stats and blow into it
18:04
Speaker 1: and hear my wheezing and give me a a steroid
18:08
Speaker 1: shot and a Z pack and a breathing treatment and
18:12
Speaker 1: send me on my way. So it's always what clears
18:14
Speaker 1: it up. Would you care if it was a robot
18:16
Speaker 1: that gave you that shot? Not at all, um, but
18:21
Speaker 1: I definitely would want more personal care if it was something.
18:24
Speaker 1: What if it was a robot with a nice avatar,
18:26
Speaker 1: sexy avatar or just a friendly one. Yeah, that was
18:30
Speaker 1: a little, a little, a little it would touch your
18:32
Speaker 1: forearm here there. Yeah, well that might be a little creepy. Yeah,
18:36
Speaker 1: it's like it was an old timey doctor who like
18:38
Speaker 1: gave you some epocac if you had diarrhea, just send
18:41
Speaker 1: you on your way, drink a coke. But it wouldn't
18:44
Speaker 1: send you on your way to give you ip acac
18:45
Speaker 1: and then it wouldn't let go of your forearm. Yeah. Well,
18:50
Speaker 1: surgical robots, that's a that's a thing. I mean, we're
18:52
Speaker 1: getting around, but they've been performing they've been performing robotic
18:57
Speaker 1: surgery since the early eighties. Um doctor assisted until two
19:02
Speaker 1: thousand ten, where they were in Montreal. They performed the
19:04
Speaker 1: first fully robotic surgeries when they removed a prostate with
19:10
Speaker 1: the fully robotic UH surgeon and fully robotic anentusiologist Dr
19:17
Speaker 1: mc sleepy Dr mc sleepy. Yeah, and then that's the
19:20
Speaker 1: real name the robot surgeon was da Vinci, which is
19:22
Speaker 1: like the basically gold standard for robotic surgical or surgical robots. Yeah,
19:28
Speaker 1: they had in two thousand, thirteen fifty thousand robotic surgeries
19:33
Speaker 1: performed in the US. So it's it's big and um.
19:37
Speaker 1: But the da Vinci is a doctor basically sitting in
19:41
Speaker 1: a little uh it looks like an arcade game and
19:44
Speaker 1: using um robotic arms to mimic his or her movements
19:50
Speaker 1: on more microscopic levels. Right, So the robot has more
19:53
Speaker 1: precise movements and can make smaller movements, um than the doctor.
19:58
Speaker 1: It's tell it's and what's the opposite of telescoping, like
20:02
Speaker 1: going downward in scale? Whatever that is. It's taking the
20:06
Speaker 1: movements of the doctor and reducing them in scale. Let's
20:09
Speaker 1: call it reverse telescoping, reverse telescoping those movements, um, which
20:13
Speaker 1: is a pretty awesome achievement in and of itself. That
20:16
Speaker 1: doctors being fed three d um graphics of what the
20:21
Speaker 1: robot is seeing, uh, and just kind of working from there. Uh.
20:26
Speaker 1: What we're moving towards apparently is fully robotic size surgeries.
20:32
Speaker 1: I was talking to Joe McCormick from Forward Thinking, and
20:36
Speaker 1: he was saying that, um, there was there's something called
20:39
Speaker 1: the Raven four, I believe. Uh. And basically you just say,
20:44
Speaker 1: this is going to be a gall bladder surgery on
20:46
Speaker 1: a six ft six male age, you know whatever, and
20:51
Speaker 1: here's his here's the cat scan of his abdomen um.
20:56
Speaker 1: So go removes gallbladder and you press enter, and thing
21:00
Speaker 1: goes in there and like removes the guy's gallbladder and
21:02
Speaker 1: sews him up. Yeah, that's fully robotic, like fully autonomous
21:08
Speaker 1: robotic surgery. Press the button and it does it. You're
21:11
Speaker 1: not actually controlling a machine that does it exactly the
21:14
Speaker 1: machines doing it at your behest, but you're not controlling it. Yeah. Um,
21:18
Speaker 1: and we're right on the cusp of that, and apparently
21:20
Speaker 1: it's already happening. Uh yeah, but there are some issues. Um.
21:25
Speaker 1: I looked into it and found that a lot of
21:28
Speaker 1: injury reporting and robotic surgery is um not being reported.
21:32
Speaker 1: It's it's substandard. And uh, this woman, Sheina Wilson, had
21:37
Speaker 1: robotic surgery for a hysterectomy in two thousand thirteen and
21:41
Speaker 1: apparently this uh intuitive surgical system had there had been
21:46
Speaker 1: a bunch of injuries that she didn't know about, and
21:49
Speaker 1: she had her wrectum burned badly and said, if I
21:53
Speaker 1: would have known that this system had these issues, would
21:57
Speaker 1: not have elected to take part in it. So there's
21:59
Speaker 1: a lot of under reporting. UM. The f d A, UM,
22:03
Speaker 1: they have no authority to force a doctor to do this,
22:06
Speaker 1: and apparently there's every reason in every link in the
22:10
Speaker 1: chain not to report these things, you know, and the
22:14
Speaker 1: f d A not enforcing this kind of thing, not
22:17
Speaker 1: enforcing reporting is ridiculous. Yeah, you know. The thing is
22:23
Speaker 1: that things like that happen, and there's under reporting um
22:27
Speaker 1: with human surg surgeons as well. Sure, not just robotic,
22:31
Speaker 1: it's like overall apparently surgical injury and accident reporting is
22:35
Speaker 1: not compulsory. Yeah, and here's here's a few points though.
22:39
Speaker 1: Counterpoints I guess is one, it's not always the robotic
22:43
Speaker 1: component of the surgery that was the cause to a
22:47
Speaker 1: lot of times they say they don't know about this
22:49
Speaker 1: until like a lawsuit is filed, so it could be
22:52
Speaker 1: weeks or months later with the physician doesn't know about it,
22:55
Speaker 1: the f d A might not get report on it,
22:58
Speaker 1: and like six months later you follow lawsuit and that's
23:00
Speaker 1: how it comes to light. UM. But the FDA is
23:03
Speaker 1: definitely concerned and are supposedly working to improve this. That's
23:08
Speaker 1: very concerned. They're very concerned. H And another problem too
23:12
Speaker 1: in that same article, a lot of these robotic surgical systems,
23:17
Speaker 1: you still have to have the correct amount of training
23:20
Speaker 1: and UH. The feeling of some experts is that UM
23:24
Speaker 1: or at least this one guy, Enrico Benedetti, he's a
23:27
Speaker 1: head of surgery at the University of Illinois, Chicago, says
23:30
Speaker 1: a lot of it just comes back to training these
23:32
Speaker 1: some of these doctors aren't getting adequately trained in these
23:35
Speaker 1: machines enough to perform the surgery. Like what happens when
23:38
Speaker 1: I do this? Oh, that happens. That's not good. I've
23:43
Speaker 1: got another alarming stat for you to hold on. Hold on,
23:45
Speaker 1: hold on. Before that, let's do a message break rel okay,
23:56
Speaker 1: tell me you're alarming stat alright. JOHNS. Hopkinsider study that
23:59
Speaker 1: found as many as forty thou patients die in intensive
24:03
Speaker 1: care each year in the US due to misdiagnosis MAN
24:08
Speaker 1: and UM. Another study found that system related factors like UH,
24:13
Speaker 1: lack of teamworking, communication, or just poor processes were involved.
24:17
Speaker 1: In six of diagnostic error and cognitive factors in with
24:24
Speaker 1: premature closure is the most common, which is basically just
24:27
Speaker 1: sticking to that admitsial diagnosis and not being open minded
24:30
Speaker 1: to other like second opinions. Yeah, so there's this thing
24:33
Speaker 1: called anchoring bias. That UM was in that New York
24:37
Speaker 1: Times article. Dr dollar Wall the guy who created this
24:40
Speaker 1: program that's now around to support diagnostics where a physician
24:45
Speaker 1: will say, I think it's this, but let me put
24:47
Speaker 1: in the symptoms and ask Isabelle um which is the
24:50
Speaker 1: name of the program, and it's named after the guy
24:53
Speaker 1: who created the Program's daughter, oh man that yeah, when
24:56
Speaker 1: she was three, took her to the hospital and the
24:58
Speaker 1: doctors said, well, she has chicken pox. And she did
25:01
Speaker 1: indeed have chicken pox, but that's all they looked at.
25:04
Speaker 1: They completely missed a pretty nasty case of necrotizing fasciitus,
25:08
Speaker 1: which we've talked about before, flush eating bacteria, and um,
25:13
Speaker 1: she almost died from it. It was it was disfigured
25:16
Speaker 1: from it as a result, so that her father, who
25:18
Speaker 1: is a money manager, said I'm going to take whatever
25:21
Speaker 1: computer programming skills I haven't put it towards this program.
25:25
Speaker 1: Isabelle which is meant to say, yes, you're right with
25:28
Speaker 1: this diagnosis, I agree with you, or have you considered
25:31
Speaker 1: these other diagnoses? And he said, like, had Isabel been
25:35
Speaker 1: around and his daughter's doctors consulted it, they would not
25:38
Speaker 1: have missed the necrotizing fasciitus. Well it makes sense, um,
25:42
Speaker 1: as an assist. You know, Um, there's this company called
25:44
Speaker 1: life Calm that said in clinical trials that if you
25:47
Speaker 1: use a medical diagnostic program as an assist, uh, those
25:52
Speaker 1: engines were accurate without using exams or imaging or labs
25:57
Speaker 1: even really just symptom. Yeah, that's crazy, that's really really
26:04
Speaker 1: really good. Yeah, like that's a that's an A, that's
26:08
Speaker 1: a low A. It's still in a. But as an assistant,
26:12
Speaker 1: I think it's you know, it's kind of a no brainer,
26:15
Speaker 1: don't you think. Oh yeah, I think so. I don't
26:17
Speaker 1: know why. I all I can think of is possibly
26:21
Speaker 1: worrying about feeding the beasts that will take your job,
26:26
Speaker 1: or just having too much of a case load to
26:28
Speaker 1: take the time to double check your work on a
26:31
Speaker 1: computer would be the only reasons why doctors aren't using that. Well,
26:36
Speaker 1: the smartphone is becoming a potential self diagnoser. There's all
26:42
Speaker 1: these cool things on the horizon that you can use
26:45
Speaker 1: your your phone for. There's one called a live Core,
26:49
Speaker 1: which you can take your own ECG test and potentially,
26:53
Speaker 1: for the cost of getting one e c G in
26:54
Speaker 1: the hospital, you could send a year's worth of daily
26:59
Speaker 1: ECGs you took yourself to your doctor, and then you
27:02
Speaker 1: carry all that info and all of your other medical
27:05
Speaker 1: info from all of your apps that will eventually be
27:07
Speaker 1: integrated into one or two apps that will probably become
27:09
Speaker 1: preloaded on your iPhone in the next couple of years,
27:13
Speaker 1: and you've got your medical history right there. Yeah, I mean,
27:16
Speaker 1: you know. Most of these require like a little clip
27:18
Speaker 1: on like um, something called cell scope that's like you
27:21
Speaker 1: clip it onto your little camera lens essentially, and it's
27:25
Speaker 1: like what are the little magnifiers with the lights that
27:28
Speaker 1: doctors used to look in your ears and eyes? Uh yeah,
27:31
Speaker 1: it looks like one of those clipped onto your your
27:33
Speaker 1: iPhone and it produces, uh, you can do imaging for
27:37
Speaker 1: skin moles and rashes and ear infections. They have one
27:40
Speaker 1: called NTRA that you could potentially give your own eyes uh,
27:46
Speaker 1: get your own like glasses prescription done, and then you
27:50
Speaker 1: order the information to some website and they say, and
27:55
Speaker 1: then this one called adamant uh that smells your breath,
28:00
Speaker 1: that smells gases in your breath and it could detect
28:03
Speaker 1: like lung cancer even yeah, apparently you have real metabolic
28:08
Speaker 1: changes to the smell of your breath when you have
28:11
Speaker 1: different types of cancer, not just long um. Like bees
28:14
Speaker 1: can detect breast cancer. Um. If you breathe into like
28:19
Speaker 1: this uh special glass fear with bees around it, they
28:22
Speaker 1: can be trained to detect lung cancer and they come
28:24
Speaker 1: back with the correct results a lot of the time.
28:28
Speaker 1: So a lot of these are on the horizon. They're
28:29
Speaker 1: not like in heavy rotation yet, no, but it's pretty neat.
28:33
Speaker 1: All of them reveal this idea that no one cares
28:37
Speaker 1: about your particular health and well being more than you
28:42
Speaker 1: unless you're one of those dudes who doesn't really care.
28:45
Speaker 1: Then your your wife does, you know, and we probably
28:49
Speaker 1: cares more about me than me, right, But there's there.
28:52
Speaker 1: The point is the doctor, the insurance company, the the hospital,
28:57
Speaker 1: while they're all in the field because they do care
29:00
Speaker 1: about your health, of course, they can't possibly care about
29:03
Speaker 1: it more than you or your loved one does. So
29:05
Speaker 1: the idea of giving you the ability to keep all
29:09
Speaker 1: of that information yourself and easily hand it over to
29:12
Speaker 1: them or potentially down the road, a computer version of them.
29:16
Speaker 1: I can't think of any any better revolution in medicine
29:18
Speaker 1: right now than that, agreed. I think it's pretty exciting. Yeah.
29:22
Speaker 1: I think we're going to live into the triple digits, buddy. Yeah.
29:25
Speaker 1: And I think there will always be a need for
29:27
Speaker 1: doctors and nurses. I don't think anyone will be wholly
29:29
Speaker 1: replaced but a little robot assist. Yeah. Yeah. Let me
29:35
Speaker 1: make one more point, all right, there's so you've heard
29:37
Speaker 1: of genomics. Yes, there's also this thing called proteonomics, which
29:42
Speaker 1: is basically your protein version of your your genome, your genome,
29:46
Speaker 1: and it's all of the proteins in your body that
29:49
Speaker 1: you have, that your manufacturing, that you're losing, and all
29:51
Speaker 1: the changes and fluctuations in them. And the idea is
29:54
Speaker 1: that you can get a full work up of your
29:57
Speaker 1: proteinome and your gino them and eventually you can add
30:02
Speaker 1: that to your medical history as well. What your e
30:05
Speaker 1: k G reading has been over the past year, um,
30:08
Speaker 1: any way you may have gained or lost or anything
30:10
Speaker 1: like that, what your breath smells like metabolically speaking, and
30:15
Speaker 1: not only have your current state of health, but personalized
30:21
Speaker 1: your version of that, personalized down to your genes and
30:24
Speaker 1: proteins in your body, so a treatment could be specifically
30:29
Speaker 1: tailored to you. Wow, that's gonna be really tough for
30:32
Speaker 1: a human physician to do that on their own. The
30:35
Speaker 1: top of that, yeah, be the the amount of data
30:37
Speaker 1: available already is overwhelming human doctors. When you add this
30:41
Speaker 1: other kind of stuff on it, it's just pulling away
30:43
Speaker 1: from them more and more. Yeah. And medical record keeping
30:47
Speaker 1: is uh. I know there's been issues with that and
30:50
Speaker 1: digitizing that and keeping up with medical records. And if
30:53
Speaker 1: you could be yourself advocate and keep up with your
30:55
Speaker 1: own medical records, it might be kind of nice. So
30:58
Speaker 1: I feel like we have heard the question. Yes, doctor,
31:05
Speaker 1: I don't know. I think in in the future, I
31:07
Speaker 1: will always have humans to interact between us. I think
31:11
Speaker 1: because we're always gonna want somebody to yell at or
31:14
Speaker 1: be like, what is this robot doing? Or can you
31:16
Speaker 1: help me this robot's give me some ipocac and won't
31:18
Speaker 1: let go on my arm or burn my wrectum. Yes,
31:22
Speaker 1: we're always going to need humans. It's just I don't know, well,
31:25
Speaker 1: we need physicians, and if we do, will they be
31:27
Speaker 1: super specialized like just the Supreme Court of Physicians. Who knows.
31:33
Speaker 1: It's pretty exciting, but we will see this change one
31:35
Speaker 1: way or another in the next fifteen years under my prediction.
31:39
Speaker 1: It's happening. Okay, good and Chuck. Yeah really, if you
31:46
Speaker 1: wanna learn more about computers possibly replacing doctors, you can
31:51
Speaker 1: type those words into the search bar how stuff works
31:53
Speaker 1: dot com. And since I said search bar, that means
31:56
Speaker 1: it's time for a message break. Okay, So so what
32:04
Speaker 1: do we have listener mail time? Yeah, I have one
32:10
Speaker 1: called I'm gonna call it fight Club. Okay, Hey, guys,
32:15
Speaker 1: just finished the podcast on deep refrigerating. I think I'll
32:19
Speaker 1: keep my Energy Star certified fridge. Thanks very much. But
32:23
Speaker 1: Josh did mentioned something about eating weeds and asked a
32:25
Speaker 1: somewhat rhetorical question, what are weeds anyway? Just plants we
32:28
Speaker 1: say are bad? Reminded me of some today's common uh
32:32
Speaker 1: some that some of today's common noxious weeds. How they
32:35
Speaker 1: got their reputation. Not so long ago, lawns were perfect
32:38
Speaker 1: blends of Bermuda rye and Kentucky bluegrass. They also included
32:42
Speaker 1: many types of clover, dandelion and other quote weeds. In fact,
32:45
Speaker 1: many seed mixtures specifically included white clover because it makes
32:48
Speaker 1: an excellent cover in soils where more common grasses won't
32:51
Speaker 1: grow In steps the Scott Fertilizer Company post World War
32:54
Speaker 1: two America housing tracks were popping up all over the
32:57
Speaker 1: US and new suburbia, and Scott was in urging returning
33:00
Speaker 1: gis to take pride in their new lawns and to
33:02
Speaker 1: buy their products to do so. And we're extremely high
33:05
Speaker 1: waisted pants, right. They produced fertilizers, weed killers, and other
33:09
Speaker 1: long care products, some of which had a curious side effect,
33:12
Speaker 1: killing many leafy greens that came up to the point
33:14
Speaker 1: that we're not considered weeds at the time, including white clover.
33:17
Speaker 1: Instead of reformulating, what they did was what any red
33:21
Speaker 1: blooded American corporation would do. They redefined what was a weed.
33:25
Speaker 1: White clover made that list as the dandelions, when in
33:27
Speaker 1: fact both are still in use today in cooking and medicines.
33:31
Speaker 1: Would you call that an oxious weed? No? So thanks
33:34
Speaker 1: for that, guys, and thanks for all the knowledge I've
33:37
Speaker 1: learned and have a great and that it's from Robert Paulson.
33:42
Speaker 1: Oh yeah, Robert Paulson. He's a he's a sharp dude.
33:45
Speaker 1: That's why I called the Pike Club. Remember that. Oh yeah,
33:48
Speaker 1: I think I made a joke to him about that
33:50
Speaker 1: once on Twitter and he never responded. Yeah, he's he
33:52
Speaker 1: writes in a lot now he's every time I see
33:54
Speaker 1: his name, I think and his name is Robert Paulson. Yeah,
33:57
Speaker 1: thanks a lot, Robert Paulson. We appreciate you. If you're
34:00
Speaker 1: ever shot in the head and the commission of a robbery,
34:03
Speaker 1: we will dispose of your body. Um. If you want
34:07
Speaker 1: to get in touch with me and Chuck and you
34:08
Speaker 1: have a name that you would like us to poke
34:10
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34:18
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34:29
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34:35
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34:38
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