Although better known as a nurse, Florence Nightingale was also a skilled data scientist who successfully convinced hospitals that they could improve health care by using statistics. In 1859, in honor of these achievements, she became the first woman ever elected to the Royal Statistical Society. This talk will consider the question of what Nightingale\u2019s experience can teach us about our own age, as we consider the future of artificial intelligence in health care. James Scott is Associate Professor of Statistics and Data Sciences. He is the author of a new book about artificial intelligence, AIQ: How People and Machines are Smarter Together, which explains what everyone needs to know in order to understand how AIQ is changing the world around us.<\/p>\n","protected":false},"excerpt":{"rendered":"Although better known as a nurse, Florence Nightingale was also a skilled data scientist who successfully convinced hospitals that they could improve health care by using statistics. In 1859, in honor of these achievements, she became the first woman ever elected to the Royal Statistical Society. This talk will consider the question of what Nightingale\u2019s […]","protected":false},"author":13,"featured_media":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","episode_type":"audio","audio_file":"http:\/\/podcasts.la.utexas.edu\/british-studies-lecture-series\/wp-content\/uploads\/sites\/4\/2018\/05\/18-04-20-British-Studies-Lecture-Series.mp3","podmotor_file_id":"","podmotor_episode_id":"","cover_image":"","cover_image_id":"","duration":"","filesize":"58.87M","filesize_raw":"61730282","date_recorded":"20-04-2018","explicit":"","block":"","itunes_episode_number":"","itunes_title":"","itunes_season_number":"","itunes_episode_type":""},"tags":[33,34,36,37,32,35,38],"categories":[],"series":[2],"class_list":{"0":"post-51","1":"podcast","2":"type-podcast","3":"status-publish","5":"tag-33","6":"tag-ai","7":"tag-aiq","8":"tag-data-science","9":"tag-florence-nightingale","10":"tag-healthcare","11":"tag-nurse","12":"series-bsls","13":"entry"},"acf":{"related_episodes":"","hosts":[{"ID":949,"post_author":"10","post_date":"2021-01-20 19:50:06","post_date_gmt":"2021-01-20 19:50:06","post_content":"\n
Wm. Roger Louis is head of the British Studies Lecture Series. He is an American historian and a professor at the University of Texas at Austin<\/a>. Louis is the editor-in-chief of The Oxford History of the British Empire<\/a><\/em>, a former president of the American Historical Association<\/a> (AHA), a former chairman of the U.S. Department of State's Historical Advisory Committee, and a founding director of the AHA's National History Center in Washington, D. C.<\/p>\n","post_title":"Wm. Roger Louis","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"wm-roger-louis","to_ping":"","pinged":"","post_modified":"2021-01-20 19:50:06","post_modified_gmt":"2021-01-20 19:50:06","post_content_filtered":"","post_parent":0,"guid":"http:\/\/podcasts.la.utexas.edu\/british-studies-lecture-series\/?post_type=speaker&p=949","menu_order":0,"post_type":"speaker","post_mime_type":"","comment_count":"0","filter":"raw"}],"guests":[{"ID":803,"post_author":"40","post_date":"2020-06-23 19:17:05","post_date_gmt":"2020-06-23 19:17:05","post_content":"\n James Scott is Associate Professor of Statistics and Data Sciences. He is the author of a new book about artificial intelligence, AIQ: How People and Machines are Smarter Together, which explains what everyone needs to know in order to understand how AIQ is changing the world around us.<\/p>\n","post_title":"James Scott","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"james-scott","to_ping":"","pinged":"","post_modified":"2020-06-23 19:17:05","post_modified_gmt":"2020-06-23 19:17:05","post_content_filtered":"","post_parent":0,"guid":"http:\/\/podcasts.la.utexas.edu\/british-studies-lecture-series\/?post_type=speaker&p=803","menu_order":0,"post_type":"speaker","post_mime_type":"","comment_count":"0","filter":"raw"}],"transcript":" This is so session that everyone has been looking forward to, because who knows what
\n\ue5d4
\nthe connection is between Florence NIGHTINGALE and artificial intelligence.
\nIt takes almost intellectual acrobats to connect the
\ntwo much starboard. It’s going to introduce our speaker.
\nI’ve once introduced Mike himself as being the most famous
\nmathematician at the University of Texas. And then someone said that’s an insult to the mathematics
\ndepartment. Mike? Oh, I’ve found
\nthat pretty insulting right away. OK. Well,
\nin any case, my job not only to defend myself, but to introduce James Scott here.
\nSo James James is a wonderful scholar. Bayesian
\nstatistician who was started at the University of Texas at Austin
\nas an undergraduate. And I had him and I had the pleasure of having him into my classes at least
\ntwo to two classes, and as did Roger. And
\nand he has gone on to have a wonderful career at a Marshall Scholarship, went to England on a Marshall Scholarship.
\nHe has came back out of PHC at Duke and has been a professor here in the business school
\nand in the Department of Statistics and Data Science in the College of Natural
\nSciences. He’s won many awards, including international awards and
\nas well as an NSF career award, the Savage Award, which is a by the one
\ngiven a year by the International Society of Bayesian Analysis for his doctoral
\nwork and by Ari Award for Early Career Research Achievements in Bayesian
\nstatistics. He’s really accomplished. But what I like, among many things I like about him
\nis that he’s also accomplished in other areas, such as teaching. He’s won the UC System Regents
\nOutstanding Teaching Award. How much did you get for that? Got twenty five thousand dollars.
\nWas that not? Not quite. How much? How much? Something like that. Something like that. Yeah.
\nI just wanted him to share. But no. No. Do I hear anything? But. But, but I
\nwanted to say one one, not one less. You know, form one, which is
\none of the things I admire, among many things I admire about James, as is his.
\nHis strategy of taking advantage of things and then taking them to another
\nlevel. So while he was an undergraduate student here at u._t, he was in my among other classes,
\nmy number theory class. And for the only time in the history of my teaching at
\nU.T., he organized a collection of students in the class and he said,
\nwe’d like to learn more. No theory than you’re offering. Could we come in on Tuesday and Thursday
\nand have an other sessions? And so a group of have’nt led by him and three or four
\nothers came in every Tuesday and Thursday for the last half of the semester and learned things beyond the
\ncourse. There’s no credit. You know, just good. So it was a
\nand I think he’s done this throughout his his career. And it’s and
\nit’s great. The other thing that is great is that he is taking his work and taking it beyond the academy.
\nI think that our academia is guilty of inward
\nlooking. And and the fact that he has written for people outside
\nthe academy is really important. He’s written a new book called a-I. Q How People and Machines are Smarter,
\ntogether with coauthor Polson, which is coming out
\nnext month, next month by a Macmillan press. And I wanted to read a blurb
\nby Stephen D. Levitt, who is the coauthor of Freakonomics, you know, Freakonomics.
\nAnd he said of this coming book, There comes a time in the life of a subject when someone steps up
\nand writes the book about it. Q That’s the book that he just wrote, explores
\nthe fascinating history of the ideas that drive this technology of the future and demystify demystifies
\nthe core concepts behind it. The result is a positive and entertaining look at the great potential
\nunlocked by marrying human creativity with powerful machines. So I
\nthink we’re all looking forward to reading his work when it comes out next month. And so it’s my great pleasure
\nto introduce James Scott.
\nThank you so much, Roger, for inviting me here, it is always such a pleasure to be among friends and colleagues
\nback in British studies to see many familiar old faces. Thank you very much, Mike, for that kind introduction.
\nMike got out. I’ll say a little bit about the book before I kind of launch into the subject of today’s talk. I
\nasked my coauthor, Nick Paulson, and I thought of this book first as a way to answer all of the great questions
\nthat our students had about artificial intelligence. Things like how does a self-driving car work? How does an Amazon
\necho understand what I’m saying? That sort of thing. We noticed that there was a lot of writing about
\nA.I. out there that was very technical. A lot that was kind of fizzy pop, sociology
\nand then a lot that was borderline science fiction of the Elon Musk. The robots
\nare coming for you variety. But if you wanted the non-technical version of how this stuff actually works, then
\nyou were stuck. But then along the way we realized that the public narrative surrounding
\nartificial intelligence were broken. On the one hand, you have nothing but hype about A.I.
\ncoming from the business world. You know, if you believed all of those IBM Watson ads doing during the
\nSuper Bowl, you would come away believing that A.I. is going to fix the health care system and sell more
\nCheerios and make your toilet smell like roses and and so on. But then on the other side,
\nyou have folks claiming that A.I. is going to destroy everything we care about from our jobs toward democracy
\nto our privacy. With A.I., we’ve clearly reached the point where a non-expert can’t
\ntell the difference between hype and the reality. So as educators, Nick and I just came back to a simple bedrock
\nthat if you want to participate in the great debates of the 21st century, it’s really important. Three people have a sense of
\nwhat’s hot air and what’s genuine promise when it comes to A.I. And they can’t do that without understanding how
\nthe underlying technologies actually work. In particular, they can’t do that without understanding
\nthe role of data in A.I., which is why we wrote a i._q. I now tell you a quick story here that will
\nthat will give you some sense of how naive I was in dealing with a mainstream nonacademic press
\nfor the first time. When we talk with our American publishers about cover designs, we told
\nthem we wanted something simple and elegant. Academic. Right. And they came up with this, which I really
\nlike to write. So Nick and I thought it was great. We then asked a British publisher if they could do
\nsomething similar and they said, yeah, sure, sure. We’ll do something very much along the same lines. And then finally in
\nmid-March, I got an email from the British publisher saying that this was their idea of something
\nsimilar. Right. So so I told them that I hated it.
\nIn fact, when I saw the email, I was in an airport at the time. And my reaction to this design
\nwas to point out that I could find exactly one book in the airport bookstore that was had
\na cover that was in such a lurid shade of yellow. And I didn’t think it offered a very flattering comparison.
\nSo my wife was laughing me, by the way, she was in the airport, too, as all this happened. And she said, you’re color
\nblind, what do you care? And I said, I’m not that color blind. So, you know, in the
\nend, the publisher was pretty firm about the need for a design that pops or some nonsense like that.
\nSo if you see a i._q in America, you’ll get the simple, elegant cover. And if you see it in a Commonwealth country somewhere,
\nit will be despite my protests in that very fluorescent shade of yellow. OK, so I
\nwill I will stop complaining about yellow now, because I did come here to talk about a serious topic,
\nwhich is health care. Now, if you read the stories about health care these days, you will encounter
\ntwo very different narratives. First, there’s the bad news, which is that health care systems
\nacross the rich world are an awful shape. Obesity and heart disease are up. Costs are spiraling
\nout of control. In 2016, two thirds of all British NHS trusts ran a deficit.
\nAmericans, meanwhile, spend far more of their GDP on health care than anyone else and aren’t
\nany healthier to show for it. Doctors tend to spend their days, at least in America, sweating lawsuits,
\nfighting insurance companies and typing data into an electronic health records system. Compared
\nto non-doctors, they are 40 percent more likely to abuse alcohol or drugs and twice as likely
\nto commit suicide. But then, perhaps as an antidote to all of these depressing stories,
\nwe are also told that artificial intelligence is set to transform health care. a-I evangelist’s
\ndescribe a futuristic world where your surgeon is assisted by a laser guided robot, just like the Google
\ncar, where your vital signs are algorithmically monitored for anomalies, just like your
\ncredit card and where your treatments are personalized. Jeff, just like your Netflix account,
\nit’s a world where your smartwatch can tell you whether you’re going into labor and where you can snap a picture of
\na skin lesion with your smartphone and get an instant diagnosis. In this world of the future,
\ndoctors no longer spend a third of their time doing manual data entry. Instead, they
\ntell everything to a sort of Amazon echo on steroids, which immediately updates your medical record.
\nIt is a future where a technology update accessible through smartphone brings
\nbetter health care to underserved communities. Probably first here in. Rich world. And then eventually in the developing
\nworld, it is a future where childbirth becomes safer, where diseases
\nare caught earlier and where oceans of human potential reach full tide.
\nSo here’s the question I’d like to address today. Why aren’t we there already? After
\nall, each of the technologies I just listed already exists in some form or another.
\nAnd it is dead obvious what’s needed in order to prompt their widespread adoption. We need better data.
\nWe need deeper collaboration between health care professionals and data scientists. And we need smarter laws
\nthat can foster innovation and yet still safeguard patients and their privacy. But as I’ll argue
\ntoday. Just because something good can be done with data doesn’t mean it will be done.
\nIf you look across the spectrum of human activity, I claim that health care is the one area where artificial
\nintelligence could probably do more good than anywhere else. And yet the grim reality today, at least,
\nis that we are still likely years away from seeing our most advanced A.I. technologies used to help real
\npatients in substantial numbers. And I’m not talking about speculative future technologies. I’m talking
\nabout stuff that exists right now. For example, here is a smartphone
\napp designed by researchers at Stanford. You can snap a picture of a skin lesion and it will using
\nsomething called a deep neural network, classify it into one of over two thousand different types
\nof skin lesions. And we’ll do so. Moreover, with accuracy comparable to a panel of 23 board certified
\ndermatologists. I’m talking about something developed here at the University of Texas
\nby a chemistry professor named Livia Evelin that made the news. This is the BBC. They call it the mass spec
\npen. It’s a pen that you can insert into a tissue during cancer surgery. And within 10 seconds
\nit will run mass spectrometry and tell you whether that tissue is cancerous or healthy, which can really help
\ntell you which parts of the tissue to resect during cancer surgery. I’m talking about epidermal electronics.
\nIt’s a little tattoo, no thicker than the width of human skin. You can see for scale right here.
\nHere’s a person’s wrist. Here’s the little epidermal electronic with EKG and EEG sensors,
\nwith temperature and hydration sensors with wireless technology that you could hook it up to your cell phone
\nprogram with algorithms that can monitor your vital signs for anomalies. This is stuff that exists right
\nnow. And the reasons why we aren’t seeing widespread adoption of these technologies that have
\nnothing to do with science or computing power, statistics and everything to do with culture incentives and bureaucracy.
\nHealthcare systems in America, Europe and Asia different important ways, but they all share some similarities
\nin terms of how I could help and why it isn’t helping already.
\nAs I like to put it, cancer and kidney disease have no nationality. But there is a word for bureaucracy
\nin every language. Now I know that historians tend to be suspicious of analogies, but
\nI am not in the story and I’m a data scientist and I spend my days using health care data
\nto help doctors and patients in that role. It really helps me at least to seek an historical
\nexample of someone who faced a similar problem and overcame it. Someone who possess the knowledge,
\nthe stature and the moral authority to stand up to the powerful people who run health care systems
\nand say basically, get your act together. In my example for that is Florence NIGHTINGALE.
\nYou all surely know NIGHTINGALE as the most famous nurse of all time. The lady with the lamp
\nwho tended the wounded British soldiers of the crimeand war. But when she wasn’t caring for soldiers,
\nNIGHTINGALE was also a skilled data scientist who successfully convinced hospitals
\nthat they could improve health care using statistics. In fact, there’s no other data science scientist
\nin history who can claim to have saved as many lives as NIGHTINGALE. And as a result of her achievements in 1859,
\nshe became the first woman ever inducted into the u.k.’s Royal Statistical Society. Nightingale’s
\npath to unlocking the power of health care data offers some really good lessons for today in her
\nquest to bring data analysis to health care in the 1850s. She fought entrenched interests
\nthat defended the status quo against reforms that could help patients. And the fight to do the same thing today
\nis playing out in a shockingly similar way. Now, I know that some of you in the room are Victorian ists
\nand even some of you who aren’t. We’ll still know a lot more about Florence Nightingale’s biography than I do.
\nBut for those of you who are not experts in this area like me, I want to give a brief bit of background
\non Nightingale’s life here. And I hope I won’t give you any calls to tell me I’ve gotten something wrong. So
\nKnightdale became famous primarily as a result of her experience as a nurse during the crimeand war.
\nBritain first sent troops to Crimea in the spring of 1854 to lay siege to Sebastopol, the main harbor
\nfor Russia’s Black Sea fleet. And people back in London had assumed that the war would be over quickly,
\nwhich sounds awfully familiar, but there would be no quick victory. And it soon became clear
\nthat the British Army, which was a generation removed from its last major war against Napoleon
\nin 1815, was not at all prepared to face the Russians. And nowhere
\nwas this more obvious than in the Army’s decaying medical system, where. Basic matters
\nof supply chains and sanitation were thought to be beneath the dignity of the medical man in charge.
\nAnd the result of all this poor planning was predictably a logistical and humanitarian catastrophe.
\nA soldier wounded in the Crimea would find himself packed onto a grimy ship. Here’s the Crimea.
\nHere’s the field of battle packed onto a grimy ship and sent 300 miles away to the Barrack
\nHospital at Scutari, which was on the Anatolian side of the Bosphorus, opposite Constantinople.
\nAnd there the soldier might wait as long as three days on the ship to be taken ashore, where he would be
\nloaded on a stretcher or maybe strapped to a mule for this kind of jarring climb up a steep
\nhill to the hospital right there in the hospital’s filthy and I mean filthy.
\nRats crawled over the injured soldiers who lay sprawled on thin mats. Cholera
\nand dysentery were rampant. The sewers were clogged. The toilets leaked excrement into the main courtyard,
\nand a water main was blocked by a decomposing carcass of a dead horse. Who knows how it got
\nin there? The hospital was badly short of medical supplies, clean clothes, healthy
\nfood, you name it. Many amputations were even done without chloroform. By the autumn of
\nThe September 30th editorial in the Times channeled the public’s growing outrage. It went as
\nfollows. Not only are the men left to expire in agony, unheeded and shaken off the
\ncatchin desperately at the surgeon whenever he makes his rounds through the fetid ship. But now, when they
\nare placed in the hospital where we were led to believe that everything was ready, which could ease their pain or facilitate
\ntheir recovery. It was found that the communist appliances of a workhouse psych ward are wanting.
\nWell, as you can imagine, Sidney Herbert, who is secretary of war at the time, came under enormous public
\npressure. He was a family friend of the Nightingale’s, and he had seen her rapid rise in the field
\nof nursing. And so he asked her to lead a government sponsored group of nurses to Scutari to assist
\nthe doctors and to tend to the suffering soldiers. Florence agreed, and she steeled herself
\nfor the worst. But really, nothing could have prepared her for the condition she found upon her arrival
\nfor miles of corridors with the conditions I described to you men in sleeping 18
\ninches apart and their lives made miserable by what NIGHTINGALE in her diary called foul air
\nand preventable mischiefs, the hospital supply chain had broken down completely.
\nNIGHTINGALE could find no linen to make bandages. She couldn’t find fresh shirts to replace
\nthose soaked with blood. There are plenty of gangrene, lice, bugs and fleas. Yet, as NIGHTINGALE
\nwrote to a friend back home, no mops, no plates, no wooden trays, no slippers, no knives
\nand forks, no scissors for cutting the men’s hair, which is literally alive, no basin’s,
\nno toweling, no chloride of lime. She soon learned that her requests for provisions
\nhad to pass through no less than eight different government departments back in London. And when those requests
\nwere finally processed, sometimes the wrong supplies were sent or the right supplies were sent to the wrong place
\nat Squitieri itself. NIGHTINGALE encountered nothing but dawdling and obstruction from the chief purveyor.
\nMatters got so bad that she asked the times to entrust her with the donations it had collected for Soldiers Fund.
\nThat way, she could bypass the chief purveyor and go shopping for necessities herself and the Grand Bazaar of Constantinople.
\nAnd after that, she effectively became the shadow purveyor at school thirty. as the conduit for the
\nenormous variety of goods that civilians sent to Scutari. Things like food,
\ncash, slippers, linens, a drying cupboard even. I noted in reading about this raspberry
\npreserves and ginger biscuits from one Mrs Gallop, Buckinghamshire. God bless her.
\nShe soon found herself charged with reorganizing virtually every non-medical function at the
\nhospital. She described her role as cook, housekeeper, scavenger washer, woman, general
\ndealers’ storekeeper, the effort tireder to the bone. She worked 20 hour days. She took meals
\non her feet. She was exhausted by, as she put it in a letter home. The quantity of writing, the quantity
\nof talking, the dealing with the selfish, the mean. I feel like Prometheus bound to the
\nrock of ignorance and incompetency. Yet all the while, she was making a difference.
\nOnly two months after her arrival, the hospital chaplain noted in a letter, a surprising
\nair of comfort and enjoyment. There were Stow’s on every ward. There were tin baths in every corner.
\nEvery man had a bed, a clean mattress and a change of shirt twice a week. And mortality was dropping,
\nhaving peaked at a shocking 52 percent of admissions in the winter of 1855.
\nIt had fallen to 20 percent by March and thereafter continued downward to the following winter.
\nBy which point it had reached the level of mortality at no higher than the rate among civilians in a major
\ncity. Now, Florence NIGHTINGALE could hardly take all of the credit for this herself, and she never tried
\nto. Still, for more than a year, the hospital at school three had been a ship,
\nbarely surviving the gale. And in the words of an army colonel who’d. First things firsthand,
\nMiss NIGHTINGALE was its only anchor in their letters home, her colleagues noted
\nher energy, her example, her way of cutting through red tape with a machete. They recall the darkest
\ndays of winter when wounded troops arrived by the hundreds. And when winter, as one fellow noted nurse put
\nit, the officials lost their heads, crying out to flow for this and that. And they also
\nrecalled the chaos that reigned during Nightingale’s brief absences, like the one day in 1854
\nwhen she took a brief rest from her duties as shadow purveyor. And when the man of sea corridor therefore all ended
\nup drunk because they had guzzled their wine straight from the bottle, as no one had given them any cups from
\nthe store cupboard. Back in Britain, the famous journalist of the Times
\nconveyed the image of NIGHTINGALE that would endure forever. And it was this when all the medical men
\nhe wrote have retired for the night, and silence and darkness have settled upon those miles of prostrate
\nsick. She may be observed alone with a little lamp in her hands, making her solitary
\nrounds and with time. Of course, the NIGHTINGALE legend only grew up. Poems and sentimental
\nsongs were written about her soldiers, private diaries of the day recorded daydreams
\nof leaping to her aid in the face of some imaginary danger. Ships, race horses, babies
\nof every social class were named in her honor. But NIGHTINGALE herself called this reputation,
\nand I quote, nothing but a false popularity based on ignorance. She actually
\nbelieved that her work back in London far after the war was over ultimately made a much bigger difference.
\nAnd modern historians largely agree with her.
\nNow, much of the historical work on NIGHTINGALE concerns her legacy in the field of nursing, specifically her
\nrole in the decades long period of reform in the training and certification of nurses that took place
\nthe middle of the 19th century. But here I want to focus on a different part of Nightingale’s legacy, which is her legacy
\nas a data scientist, not as a nurse. A big part of that is her
\npersonal analysis of medical statistics from the crimeand war. NIGHTINGALE It’s fair to say
\nit was really into math and statistics. A lot has been written about how she aspired to be
\na nurse from a very young age, about how she treated injured dogs, about how she nursed a cow
\nwith a bad cough in the field next to her house, how she visited the sick and the dying of the village
\nnearly every evening as a teenager, but also from a very young age. She was precociously
\ntalented at math. As a child, she played mathy word games. I took breath
\nand I made forty words, she wrote in her diary. At age seven, her parents letters talked
\nof how and how NIGHTINGALE positively threw herself into her math book as a child solving
\nworld problems from a vanished age. I’ll give you an example of one of Florence’s Victorian era word problems.
\nIf there are six hundred millions of heathens in the world, how many missionaries are needed to supply one to every
\ntwenty thousand. But as a teenager,
\nshe she learned geometry by reading Euclid herself. She learned logarithms from her cousin
\nHenry, who studied mathematics at Trinity College, Cambridge, and she once begged her parents to give a visit to
\nher uncle Octavius for the simple reason that he had a fantastic math library. Moreover, all
\nof this mathematical ability was married to an incredibly strong power of will. Will,
\nin fact, that her sister pathetic be called the most resolute and iron thing I ever knew.
\nAs a young adult, Florence would awake as early as 3:00 a.m. to read anything statistical she could find
\non social welfare. Minutes from Parliament. Data from the census. A report on the sanitary
\nconditions of the laboring classes of Great Britain. So, as you might imagine,
\nFlorence came home from the scandal of Scutari, full of righteous indignation.
\nIn her diary, she wrote I stand at the altar of the murdered men, and while I live, I fight
\ntheir cause. And it absolutely was a fight against those in the army and
\nthe medical establishment who stood in the way of change and defended the status quo, like Army Doctor
\nJohn Hall, for example, who dismissed NIGHTINGALE as and I quote, a petticoat
\nin pure use. And NIGHTINGALE brought all of her weapons to bear in that
\nfight, her intellect, her network of friends, her acid pen. I’ll give you some examples of that.
\nBut above all, math and statistics, which she clearly viewed as the mightiest arrows
\nin her quiver. Nightingale’s first biographer called it?i Cook, nicknamed her the passionate
\nstatistician, which really didn’t stick in the public’s imagination the way the lady with the lamp did for obvious
\nreasons, but did provide a far better description of how she changed the world for the better.
\nNIGHTINGALE was especially adept at using graphical representations of data data visualization
\nin modern parlance to draw the nation’s attention to the conditions that had prevailed in military hospitals.
\nAs one of her colleagues put it, Nightingale’s pictures of data could affect through the eyes what we may fail
\nto convey to the brains of the public through their word proof. She even invented
\na new kind of statistical figure, which I’ll show you here. The Polar Area or coxcomb diagram,
\nwhich here shows changes in mortality over time using a series of colored wedges. So it begins
\nhere in March of 1850, rather April of 1854. And as you go
\nclockwise around the the the rows here, the size of each colored wedge
\nrepresents deaths due to a particular cause. And by far the largest pie slice
\nhere is deaths due to preventable disease. And
\nas you can see, it peaks in the winter of eighteen fifty five down here in January 1855
\ninto February, and then falls starting here in the spring of eighteen fifty five
\nand all the way around until eight feet to fifty six until the deaths due to disease are no different than
\nin a major city. So her analysis and these figures revealed that in the first seven
\nmonths of the crimeand campaign, British soldiers suffered a 60 percent mortality rate
\nfrom disease alone that was higher than Londoners had experienced during the great plague of sixteen sixty
\nfive. And it was higher even than the rate of death among a population of civilians who have cholera.
\nIt was literally safer to have cholera at home than to take your chances in the Crimea. And that was before you faced
\na single enemy bullet. A nightingale referred to this as the finest experiment modern history
\nhas seen. As to what given number may be put to death, it will by the sole agency of bad food
\nand bad air, an experiment she reckoned, that had sent sixteen thousand men to death. And
\nNIGHTINGALE did more than anything else in the wake of the crime war to bring these facts to the public’s attention.
\nA second very important data science legacy of NIGHTINGALE was her contribution to evidence. Based hospital
\ndesign, together with English statistician William Farr, she analyzed data from army hospitals
\nduring peacetime, and she discovered that because of poor sanitation, the army’s rate of mortality
\nat home was twice that of a comparable civilian population. She called this situation criminal.
\nRemember her acid pan. I referred to earlier? No different than to take eleven hundred men out upon Salisbury
\nPlain and shoot them on account of her report. The Army Sanitary Committee visit
\nevery army, barracks and hospital in England between 1858 and 1861, and they recommended concrete
\nsteps that were taken to retrofit barracks and redesign hospitals, which produced an immediate drop
\nin disease related mortality across the British Armed Services. Her recommendation soon caught on in
\nthe civilian world as well. Hospitals with long corridors and stuffy rooms came to be seen as
\ninfection incubators. Her preferred model of hospital construction became the norm, which was
\nthe pavilion style hospital, which had wings of light and abundant ventilation. These nightingale
\nwards, my sister in law, who’s a doctor in England tells me, are still popular to the extent that
\nNHS hospitals built in the 40s and 50s are almost all Knightdale words.
\nFinally, perhaps the least known of Nightingale’s data science contributions was her role in creating
\na new standard of professionalism in the collection and analysis of medical data. Now,
\nI’m sure, sure, you’ve heard it said of generals that they are always fighting the last war, but a doctor
\ntrying to learn from the experience of the crimeand war couldn’t have even done that. The medical
\nstaff at School Three had collected no statistics. They had preserved very few case histories.
\nThey had done almost no postmortem examinations in many cases. Sick men were loaded on
\none side of the boat in the Crimea, shipped 300 miles and dumped off the other side of the boat dead. When they reach
\nScuderi. NIGHTINGALE despaired at the fate of the soldiers. But she also found it
\ndeeply discouraging that this scientific treasury had been lost to mismanagement. And upon returning to England
\nafter the war, she also found that these failings were mirrored in the civilian system, that the country had no system
\nfor the collection of even the most basic medical statistics. Recoveries, lengths of stay at a hospital.
\nDeaths due to different disease and so on. And even if there had been such a system, there would have been no way to compare
\nthese statistics across hospitals because every hospital used its own idiosyncratic classification system
\nfor disease. NIGHTINGALE saw this lack of attention to good health care data as
\nan emergency. She saw how the new discipline of statistics was transforming other fields
\nlike astronomy and earth science. She also noted how Continental statisticians, most notably
\nthe eminent Belgian adult Lay, who is one of her idols. We’re using these new statistical
\ntools to look at complex social science questions and crime demographic change. NIGHTINGALE
\nsaw incredible potential in these new mathematical and statistical tools, but in her view, that required
\nmuch better health care data. And so to that end, she drew up a standard set of medical forms.
\nShe obtained the endorsement of many of the world’s leading statisticians, and she urged the big hospitals in London
\nto begin using these standardised forms. She also lobby the government to begin collecting data on illness and
\nhousing quality as part of the census. From top to bottom, her work on evidence based health care
\nclearly foreshadowed the coming a hundred and sixty years. Her ideas formed a clear model for the international
\nsystem of disease classification use today, which really is the bedrock for all of modern epidemiology
\nand medical data science. So finally, we come back to the
\nmodern day and the questions about today’s health care system that I raised in the beginning.
\nI think the Nightingale’s three data science legacy’s all have very clear parallels today. And for me at
\nleast, they also raise some very sharp questions. NIGHTINGALE spoke of the foul air and preventable mischiefs.
\nAnd while the air in hospitals today may be a lot less foul, there are I sure you mischiefs aplenty.
\nOne big question left for me at least is this how should we staff and train a modern health care
\nteam? After NIGHTINGALE, no hospital could function without nurses. And my question
\nis, when will the same be true of data scientists and experts in artificial intelligence who today
\nplay almost zero day to day role in the practice of health care? A second question
\nis how evidence based hospital design should work today. NIGHTINGALE helped to establish new
\nsanitary standards hospitals re-engineered from the ground up in response to her findings.
\nBut when will hospitals undergo another era of redesign to accomplish what is now possible using data science
\nand A.I.? When, in other words, will data hygiene be taken just as seriously as
\npatient hygiene? Finally, the most important question of all is how medical
\nstatistics should be collected, shared, analyzed and used. We’ve improved a lot
\nin that department in the last hundred sixty years, thanks in no small part to Nightingale’s efforts, but as
\nI’ll argue here, we’ve improved only in certain ways, and we could be doing so very much
\nmore in light of what’s happening outside health care. This is starting to look like a moral embarrassment.
\nWe live in an age when Formula One race cars are monitored in real time by algorithms
\nand teams of engineers. When you’re movie watching, preferences are the subject of multi-billion
\ndollar A.I. operations like Netflix, and when your propensity to click on an ad for dog food
\nis analyzed on supercomputers using millions of variables and billions of data points.
\nYet for the most part, we still rely on numbers that Florence NIGHTINGALE could have crunched with pen and paper to quantify
\nthe risk that your kidneys will fail. And in some ways we haven’t improved at all. A 2017 paper
\nin the Journal of the Royal Statistical Society referred to Nightingale’s 1860 protocol for hospital
\ndata collection as conceptually more complete than many systems today. Which leaves
\nme, and I think it should leave us all wondering when will medical data science move into the 21st
\ncentury? So I want to be clear upfront that this is not the fault of individual doctors
\nand nurses. It is the fault of the whole health care system. The plain fact is that in health care today,
\nmost data simply goes to waste. It’s no different than those soldiers on the boat in Scutari.
\nIt comes on one side of the boat is used to send you a bill and then basically gets dumped over the other
\nside of the boat dead. Now, to illustrate this point, I want to tell you a story. It is a story
\nof a man from somewhere on the East Coast, the United States. We’ll call him Joads, a real patient, but Joe’s not his real name,
\nwho died at age 62 with chronic kidney disease. Joe’s story tells you a lot
\nabout how the contemporary approach to medical data science is failing patients and why a combination of better
\ndata curation and artificial intelligence could prevent so very much suffering.
\nNow, by his mid-forties, our patient Joe was already suffering from Type 2 diabetes and congestive
\nheart failure. Maybe his job was stressful. Maybe his diet and exercise habits were poor. We don’t know.
\nBut whatever the mix of causes, they finally caught up with him. And a few weeks shy of his forty seventh birthday,
\nJoe had a stroke and was rushed to the emergency room. I just survived the stroke, and although
\nhis blood pressure and diabetes at that point marked him as having a higher risk for kidney disease at some point the
\nfuture. For now, his kidneys tested fine. The standard measure of kidney function is something
\ncalled the Jaffar on, as you can pronounce this right. It is the glue Malula filtration rate
\nto the Jaffar. Joh’s Jaffar was estimated at ninety nine, which is well above the danger
\nzone. For some reference here at GFI 60 or below indicates mild to moderate loss
\nof kidney function and GFI 30 or below means severe loss. Joe is at ninety
\nnine. Over the next year, he made nine more trips to the emergency room for problems
\nof one kind or another. None of them explicitly related to his kidneys. On two of those occasions he was admitted
\nto the hospital, and in his kidney function was measured. His Jaffar was first ninety six and then ninety
\nfive. A few months later, this decline was a little bit steeper than the expected 1 to 2
\npercent decline per year in a healthy patient. But still, each individual reading was way above the
\nthreshold that doctors used to think about danger here. About a year after a stroke,
\nJoe started making regular trips to an outpatient clinic eight visits over 14 months.
\nEach time, the doctor ordered a routine series of tests and the lab techs entered the data on his kidney function
\ninto a electronic health records database. The same database, I should add that his hospital
\nused his GFA our numbers Yo-Yo to bet between 60 and seventy five,
\nwhich was still above the threshold of 64 moderate loss of kidney function, but still quite a bit down
\nfrom the prior year reading of ninety nine. And on an unmistakable downward trend
\nat age forty nine. Two years after his first stroke, Joe was readmitted to the hospital, and his Jaffar was
\nmeasured at 54. Over the next several months, he made 10 more visits to the E.R.,
\nas well as a dozen more visits to the outpatient clinic. Joe was very sick at
\nthis point, as you might imagine. A month before his 50th birthday, his GFI was measured at 40. Well
\ninto the danger zone. Yet he received no treatment that might have prevented his slide toward kidney failure.
\nWe can only speculate why, but one reason might be that test results sometimes take a couple of days to
\ncome back from the lab, and by which point the patient might be already home, checked out of the emergency room, no longer
\nunder the direct care of the doctor who ordered the test in the first place. Who knows? Bottom line is,
\nover the next three years, Joe had 20 further encounters with the doctor and his kidney function was dropping
\nat a scary rate below 30 by age 50, one below 20 by age 52. At
\nwhich point Joe was finally referred to a kidney specialist more than a year after his kidney function had fallen
\nbelow the level of 30. That typically triggers that kind of referral. But for Joe. Kidney function,
\nkidney failure, I should say it was by now inevitable. Three months after his first appointment
\nwith the specialist his kidneys gave out, he was rushed to the emergency room. His 25th
\nsuch visits since his first stroke, his GFA was measured at twelve. His kidney
\nfunction had declined 34 percent per year each of the last five years from his initial reading
\nof ninety nine after the stroke. The doctors in the E.R. put him on emergency dialysis, which is
\none of the most traumatic and expensive procedures on the books in medicine for the next
\ndecade. Joe became what the insurance industry refers to as a super analyzer, which is just
\nmanagement speak for an appallingly sick human being. One of the 5 percent of patients who account
\nfor 50 percent of all health care spending in United States. And in Joe’s case, that meant severe
\ndiabetes, stage five kidney disease, angina, vascular disease, inflammatory connective tissue disease,
\nalong with a series of several heart attacks. His kidneys were tested one hundred and twenty four times over
\nthat decade long period, which included twenty six more visits to the E.R. and nine to a kidney
\nspecialist. His Jaffar bounced around, but it never again rose above 20. And
\nJoe died a week shy of the 60 third birthday, roughly 10 years after beginning dialysis.
\nSo my question is, what did Jo die of? And in one sense, the answer is clear. His
\nkidneys failed. But in order for that to happen, something else had to fail first in a manner so complete
\nthat to me at least, it beggars belief. For if you take all of Joe’s GFI readings the eight years
\nafter his stroke and you plot them over time and a scatterplot, that’s the trend right there
\nin those three years of steep decline between ages forty seven to fifty. When he falls
\nbelow that level four specialist referral, not a single one of Joe’s
\nhealth care providers had looked at a simple scatterplot of his GFI readings over time. The issue was
\nquite literally one of failing to connect the dots. Doing so would have yield a simple
\nand I think to all of us obvious prediction. This guy’s kidney function is declining
\nso rapidly that it is almost surely going to keep declining. And if it does, the result is going to be very painful and
\nvery expensive. So my conclusion is that, yeah, Joe certainly died for wanting a kidney. But more
\nfundamentally, he died for want of a scatterplot. My question is, how could this have happened?
\nI actually had a discussion with about this topic with a friend and colleague of mine named Dr. Katherine
\nHeller, who is a professor of statistics and machine learning at Duke University. It was Katherine who brought Joe’s
\nattention to a case to my attention in the first place. And I will
\nquote for you what she told me. In retrospect, she said that steep decline
\nbetween ages 47 and 50 represents such an obvious missed opportunity.
\nAll you have to do is draw a straight line through the cloud of data points and you can see where things are going.
\nSo why did no one, whether human or machine, draw that straight line?
\nThis is the essential question in modern health care. To understand the answer, we have to revisit
\ntwo earlier questions that Florence NIGHTINGALE asked 160 years ago when she pondered how the new mathematical
\ntools of the 1850s might be used in hospitals of that age. First, how does the health
\ncare system use data today? And second, in light of new data analysis technologies, what
\ncould it be doing instead? So today, the main way the health care system uses data is
\nto create checklists. These checklists and code the standards of care of medical bodies, things
\nlike the American Medical Association, the u.k.’s General Medical Council. And these standards of care
\nin turn are driven by data from published research findings about what warning signs to look for,
\nabout what treatments actually work, about what diagnostic protocols help the most people. That kind of thing.
\nNow, look, I think medical checklists are great. To me, the way they’re created and updated represents a triumph
\nof data over anecdote, which is something that a Florence NIGHTINGALE were around today. She could take immense pride in
\nchecklists, save lives by helping doctors catch subtle clues when making complex decisions.
\nSome of you may have even read the checklist manifesto by the surgeon to go on day about how checklists can
\nhelp make complex decisions everywhere, not just in medicine. And I could recommend the book go on. They makes
\na fantastic case, but I will also point out that checklists can fail, especially when
\nthey rely on what Katherine Heller in our conversation about Joe’s case called threshold
\nthinking. So to see that, let’s return to the trend that’s really obvious from the scatterplot of Joe’s
\nkidney readings. So Heller to me in our conversation surmise that every doctor along
\nthis tragic trail of dots right here was thinking about Joe’s case in
\nterms of a binary threshold on a checklist. Is the patient’s GM far above 30?
\nCheck our blood levels of potassium above five point five million miles per liter check.
\nDoes he have normal levels of albumin in his urine check? Now, all of those checks can tell you something
\nabout Joe’s kidney function on that one isolated visit. And they’re really important for delivering good
\nhealth care. But those checks don’t tell you anything about the long term trend.
\nSo even though Joe had been hurtling towards that terrible threshold for years,
\nhe hadn’t yet crossed it. And nobody raised an alarm until it was basically too late.
\nSo in retrospect, to me at least, this shouldn’t be surprising. Checklists,
\nas useful as they are supposed to help doctors understand and respond to what’s happening right now.
\nWhat what’s likely to happen in the future? That’s an inherent design feature of checklists. They focus
\nthe doctor’s mind on the details of the present. But in a world where the biggest and most expensive medical conditions
\nare chronic diseases that unfold over a timescale of years or decades, that feature
\nof checklists is starting to look like a bug. You might ask why not just fix the bug
\nwith a longer checklist by adding an item that encourages doctors to look at the long term trend? So
\nwhat did it have even been possible for a doctor at Joe’s bedside to call up a scatterplot
\nlike this of his historical electronic health records and plotted over time to look for a trend?
\nThe short answer is no. Maybe you could do it if you were a database experts.
\nBut it certainly would not be a simple and obvious way for a doctor to use a modern electronic health records
\nsystem to see the trend in Joe’s GFI readings. You really would have needed to go back through the
\nelectronic health records. One. Meeting at a time. And ironically, this almost surely would have been easier back
\nin the days of paper charts. Just recall the power of Nightingale’s figures
\nand how clearly the underlying trend jumped out of a figure like this. And I reflect
\non the fact that in the subsequent hundred and sixty years, we haven’t managed to bring those figures to patients bedsides.
\nTo me, that’s a tragedy. Of course, this is also where we begin to see the power of
\nartificial intelligence, because in modern medicine, it’s not just one set of readings to look at
\neither in time or over. Over time, it’s hundreds or even thousands
\nof readings, blood tests, urine tests, EEG, EKG, heart rate, blood pressure,
\nclinical symptoms, social factors, and soon real time data on a patient’s genetics or epigenetic profile.
\nThere is just so much data. You’ve never seen this much data in your life. It is hard
\nfor a human to comprehend it all. Even as a single snapshot, much less is a story that unfolds
\nover time. And then finally, there’s the issue of how this kind of hypothetical look for trends.
\nItem on a checklist would fit into a doctor’s usual workflow. When you show up to the emergency
\nroom, your doctor’s main concern is how bad is your case right now? Should you be treated and sent home? Or are you sick
\nenough to be admitted to the hospital? Doctors face high stakes and enormous pressure in making those decisions.
\nAnd even outside of an emergency room and back in a normal clinic setting, they have to make them fast because
\nthere are dozens of other patients in the waiting room need their help, too. How reasonable is it to expect those doctors
\nto stop what they’re doing, fire up a statistical software package, download some data from a database and
\nmind through a vast collection of electronic health records? All define the one or two historical
\ntrends out of thousands or millions of possible trends that might predict something bad
\nmonths or years in the future. Doctors might do that kind of thing on a TV show like House, but I can assure you that
\nthey don’t do it in real hospitals. In researching our book, I actually chatted with the medical data
\nscience expert and a physician named Mark sendek who thinks about data science in hospitals for
\nliving specifically these workflow issues. Here’s what he told me. Physicians always say they want
\nthe data. Sendak said what the problem is that there’s no workflow for them to access or use
\nthe data. The way that the records are structured, it takes time and skill. You have to write a query. You have to download the
\ndata into a spreadsheet and then you actually have to do things with it. But physicians are under so much stress already.
\nThey have 15 minute clinic visits. When exactly are they going to be playing with the data for their clinic
\nand figuring out what it tells them that they need to do with their patients? So to me, that brings us to
\nthe deepest issue of all, which is the fact that the entire system of medical data science was designed
\nonly to address questions at the level of a population, not at the level of an individual patient.
\nFor example, how many lives could we save if we used Jeff our threshold a rather than GFI threshold
\nbe for detecting kidney disease? There must be hundreds of research articles that bear on that question
\nor any such question you could find in medicine. But medical data science is nearly silent
\nat the level of basic statistical questions at the level of an individual patient. How are
\nJoh’s individual GFI readings changing over the long term? Where are they likely to go from here? What does that
\npredict about his health next month or next year? These questions would have been straightforward for either a human
\nor an algorithm to answer using Joh’s historical medical record. Yet all of those data points were never given
\na chance to speak. There was no routine in place to sift his health record for signs of an underlying chronic
\ncondition. There was no team of data scientists who algorithm no doctor with interdisciplinary training and statistics
\nand with some exceptions here and there. The same is true at most modern hospitals and clinics today. In speaking
\nwith friends and colleagues about this question, I’ve noticed that many people seem to have this impression that there’s like
\nsome kind of medical robot car behind the scenes of a modern hospital, like some fancy suite of
\nalgorithms that’s analyzing individual patient data and helping doctors make personalized
\ntreatment and diagnostic decisions. Maybe they get that impression from seeing how A.I. has transformed
\nother industries. Maybe they get it from seeing just how much damn data entry doctors do with their own appointments.
\nBut whatever the reason, they’re usually shocked when I tell them the reality, which is not only is there no rub. Robot car
\nbehind the scenes when it comes to individual patient level data analysis. There is literally nobody at the
\nsteering wheel. When I spoke with Catherine Heller about this topic, her frustration
\nwas clear, as she put it. It turns out that it’s not enough to just collect all that data.
\nYou actually have to do something with it. And here she unknowingly was channeling NIGHTINGALE,
\nwho wrote of St. Thomas’s Hospital in London in 1859 that it appears to keep
\nits statistics more for the sake of checking obstreperous patients, which is an object, certainly,
\nbut not a scientific one. So the story of Joe. It turns out, is much more
\nthan the story of a man with kidney disease. It is the story of the vast canyon between what data
\ncould be doing for us and what the health care system lets it do.
\nSo if it if it seems to you that health care professionals are drowning in data and could really use a life preserver,
\nthat a combination of human and machine intelligence could radically improve healthcare. You’re not
\nalone in that thought. Companies and researchers are hard at work already on a new generation of A.I.
\nbased technologies that stand waiting in the wings, ready to help doctors and nurses do their jobs more effectively.
\nKathryn Heller The reason she was looking at this data on kidney disease is because she has invented an app that
\nwill do this right, look at historical readings on kidney disease and predict their lung function. It’s the kind of
\nthing that a doctor could call up on an iPad at the bedside of patients. So they really could bring that hypothetical
\nlook for trends item into their usual workflow. But for me, I’ll close here with just a few
\nthoughts about the barriers, both cultural and legal. One big issue is incentives. I
\nmean, look, American hospitals buy into something like this. The question that every hospital will be asking
\nitself is what does it mean for buying my bottom line? If you can better predict kidney disease,
\nyou do not have to be much of a cynic to observe that health care systems make money on advancing chronic disease.
\nThe legal system is another set of incentives or disincentives in this case. Imagine being in Katherine Heller’s position
\nand pondering the wisdom of selling or even just giving away an app that could predict the chronic kidney disease.
\nImagine the legal peril that would await such an app designer when you think of the first inevitable
\ncase of missed kidney disease. For the simple reason that policymakers and
\nlawmakers have not gotten off their backsides to address a simple question who ultimately is responsible
\nfor an algorithm’s medical advice? How can we answer that question in a way that fosters innovation
\nwhile simultaneously protects patients and their privacy? Another big issue is whether data science
\nteams will get access to the data they need to improve existing systems and build new ones.
\nThe key thing that makes A.I. work is data at scale. That’s why Google has great
\non A.I. That’s why Amazon has great A.I. And the more I ponder this topic,
\nthe more I started to believe that this problem will not be solved until Google and Amazon own
\nall of the hospitals because thousands of patient records at one
\nhospital are almost useless for this. You need millions or hundreds of millions of patient
\nrecords, and there is no reason in principle why that cannot be done. But the
\npractical and legal challenges of making that happen. Well, it’s just back to Nightingale’s
\nconcerns about data standardization in the 1860s all over again
\nand the sources of social and financial value that go unrealized from failing
\nto pool and failing to analyze the trends in those data sets to me are mind boggling.
\nNow, even if there were a common data standard, let’s say you could solve that problem. Hospitals, I found, are very
\nhesitant to team up with data scientists, even under terms that guarantee patient privacy. In fact, I found
\nto be downright paranoid about it and nobody will really tell you why. I’ve read other
\nresearchers, by the way, say the same thing. This isn’t just me. Nobody will tell you why. But I’ve
\nalways thought it’s for a pretty craven reason that hospitals in America don’t want their competitors to
\nreverse engineer their business team pricing models. And so their default position is as a corporation
\nis just lock up all the hard drives. Whatever the reason, all those electronic health records are
\nused to generate very detailed bills, but almost never to help people require fewer hospital services
\nin the first place. So I find this mind boggling and I am not alone.
\nCan you imagine if we allowed hospitals to treat your kidneys in the same way that they treated the data
\nabout your kidneys? I mean, shouldn’t be there a form that you could sign to overrule them donating your data
\nto save someone else’s life? For me, if hospitals are the only ones with this information
\nand you were paying them to take care of you. How are they not using it? So
\nas as I close up here, as you now appreciate, the barriers to adoption of A.I.
\nin the health care system have nothing to do with technology or science. But there are enormous
\nbarriers of culture, law and incentives. Some of these barriers are specific to America,
\nbut many others affect health care systems of the rich world. And the upshot is that the next data science,
\nrevolution and health care won’t just take one person like Florence NIGHTINGALE. It will take people who
\nkeep working on cool projects that keep convincing their colleagues that this stuff actually works and
\nkeep generating good evidence for policymakers and other stakeholders. It’ll take doctors, nurses,
\nsoftware engineers, lawyers, database managers, privacy experts, venture capitalists, insurers,
\nhospital administrators and patients, all of whom must come together to make this thing work.
\nIt is a daunting task. And so I’ll leave you here today with a simple prayer. May Florence Nightingale’s
\npower of Will. What her sister pathetically called that most resolute and iron thing live on
\nin us all. Thank you.<\/p>\n"},"episode_featured_image":false,"episode_player_image":"https:\/\/podcasts.la.utexas.edu\/british-studies-lecture-series\/wp-content\/uploads\/sites\/4\/2017\/09\/british-studies.png","download_link":"https:\/\/podcasts.la.utexas.edu\/british-studies-lecture-series\/podcast-download\/51\/florence-nightingale-artificial-intelligence-and-the-future-of-health-care-james-scott-statistics-and-data-sciences.mp3","player_link":"https:\/\/podcasts.la.utexas.edu\/british-studies-lecture-series\/podcast-player\/51\/florence-nightingale-artificial-intelligence-and-the-future-of-health-care-james-scott-statistics-and-data-sciences.mp3","audio_player":"