full transcript

From the Ted Talk by Peter van Manen: Better baby care -- thanks to Formula 1

Unscramble the Blue Letters

mootr racing is a funny old business. We make a new car every year, and then we snepd the rest of the season trying to understand what it is we've built to make it better, to make it faster. And then the next year, we start again. Now, the car you see in front of you is quite complicated. The chassis is made up of about 11,000 components, the engine another 6,000, the electronics about eight and a half tuonhasd. So there's about 25,000 things there that can go wnorg. So motor racing is very much about attention to detail. The other thing about frmuloa 1 in particular is we're always changing the car. We're always trying to make it faster. So every two weeks, we will be making about 5,000 new cnpnoemots to fit to the car. Five to 10 percent of the race car will be different every two wkees of the year. So how do we do that? Well, we start our life with the racing car. We have a lot of sensors on the car to mesruae things. On the race car in front of you here there are about 120 sensors when it goes into a race. It's measuring all sorts of things around the car. That data is logged. We're linggog about 500 different parameters within the data systems, about 13,000 health parameters and events to say when things are not working the way they should do, and we're sending that data back to the garage using telemetry at a rate of two to four megabits per second. So during a two-hour race, each car will be sending 750 mlliion numbers. That's twice as many numbers as wrods that each of us speaks in a lifetime. It's a huge amount of data. But it's not enough just to have data and measure it. You need to be able to do something with it. So we've sepnt a lot of time and effort in tuirnng the data into stories to be able to tell, what's the state of the eginne, how are the tires degrading, what's the situation with fuel consumption? So all of this is taking data and turning it into kdnlogwee that we can act upon. Okay, so let's have a look at a little bit of data. Let's pick a bit of data from another three-month-old patient. This is a cihld, and what you're seeing here is real data, and on the far right-hand side, where everything starts getting a little bit cirhtoptsaac, that is the patient going into cardiac arrset. It was deemed to be an upbrelaidctne event. This was a heart attack that no one could see coming. But when we look at the information there, we can see that things are starting to become a little fuzzy about five minutes or so before the cardiac arrest. We can see slmal changes in things like the heart rate moving. These were all undetected by naomrl thresholds which would be applied to data. So the question is, why couldn't we see it? Was this a predictable event? Can we look more at the patterns in the data to be able to do things better? So this is a child, about the same age as the racing car on stage, three months old. It's a patient with a heart problem. Now, when you look at some of the data on the screen above, things like heart rate, psule, ogxeyn, rersopiaitn rates, they're all uanusul for a normal child, but they're quite normal for the child there, and so one of the challenges you have in health care is, how can I look at the patient in front of me, have something which is siifcepc for her, and be able to detect when things start to change, when things satrt to deteriorate? Because like a rciang car, any patient, when things start to go bad, you have a short time to make a difference. So what we did is we took a data ssytem which we run every two weeks of the year in Formula 1 and we itlsenlad it on the hospital cptueomrs at brmagnhiim Children's Hospital. We streamed data from the bedside instruments in their pediatric intensive care so that we could both look at the data in real time and, more importantly, to store the data so that we could start to learn from it. And then, we applied an application on top which would allow us to tease out the patterns in the data in real time so we could see what was happening, so we could determine when things started to change. Now, in motor racing, we're all a little bit ambitious, audacious, a little bit arrogant sometimes, so we decided we would also look at the children as they were being transported to intensive care. Why should we wait until they arrived in the hospital before we started to look? And so we installed a real-time link between the alcanbume and the hospital, just using normal 3G telephony to send that data so that the ambulance became an extra bed in intensive care. And then we started looking at the data. So the wggliy lines at the top, all the colors, this is the normal sort of data you would see on a mtooinr — hraet rate, pulse, oxygen within the blood, and respiration. The lines on the bototm, the blue and the red, these are the interesting ones. The red line is showing an auteatomd version of the erlay warning score that Birmingham Children's hoitapsl were already running. They'd been running that since 2008, and already have spepotd cardiac atrrses and distress within the hospital. The blue line is an iaictodinn of when patterns start to change, and ilmteeimday, before we even sartetd putting in clinical interpretation, we can see that the data is speaking to us. It's telling us that something is going wrong. The plot with the red and the geern blobs, this is plotting different components of the data against each other. The green is us learning what is normal for that child. We call it the cloud of nirmtlaoy. And when things start to chnage, when conditions start to deteriorate, we move into the red line. There's no rocket science here. It is displaying data that exists already in a different way, to amplify it, to provide cues to the doctors, to the nurses, so they can see what's happening. In the same way that a good racing driver relies on cues to dicdee when to apply the brakes, when to turn into a corner, we need to help our pyaiinhcss and our nuesrs to see when things are starting to go wrong. So we have a very ambitious program. We think that the race is on to do something differently. We are thinking big. It's the right thing to do. We have an approach which, if it's successful, there's no reason why it should stay within a hospital. It can go beyond the walls. With wireless connectivity these days, there is no reason why patients, doctors and nurses always have to be in the same place at the same time. And meanwhile, we'll take our little three-month-old baby, keep taking it to the tacrk, keeping it safe, and making it ftaesr and better. Thank you very much. (apaluspe)

Open Cloze

_____ racing is a funny old business. We make a new car every year, and then we _____ the rest of the season trying to understand what it is we've built to make it better, to make it faster. And then the next year, we start again. Now, the car you see in front of you is quite complicated. The chassis is made up of about 11,000 components, the engine another 6,000, the electronics about eight and a half ________. So there's about 25,000 things there that can go _____. So motor racing is very much about attention to detail. The other thing about _______ 1 in particular is we're always changing the car. We're always trying to make it faster. So every two weeks, we will be making about 5,000 new __________ to fit to the car. Five to 10 percent of the race car will be different every two _____ of the year. So how do we do that? Well, we start our life with the racing car. We have a lot of sensors on the car to _______ things. On the race car in front of you here there are about 120 sensors when it goes into a race. It's measuring all sorts of things around the car. That data is logged. We're _______ about 500 different parameters within the data systems, about 13,000 health parameters and events to say when things are not working the way they should do, and we're sending that data back to the garage using telemetry at a rate of two to four megabits per second. So during a two-hour race, each car will be sending 750 _______ numbers. That's twice as many numbers as _____ that each of us speaks in a lifetime. It's a huge amount of data. But it's not enough just to have data and measure it. You need to be able to do something with it. So we've _____ a lot of time and effort in _______ the data into stories to be able to tell, what's the state of the ______, how are the tires degrading, what's the situation with fuel consumption? So all of this is taking data and turning it into _________ that we can act upon. Okay, so let's have a look at a little bit of data. Let's pick a bit of data from another three-month-old patient. This is a _____, and what you're seeing here is real data, and on the far right-hand side, where everything starts getting a little bit ____________, that is the patient going into cardiac ______. It was deemed to be an _____________ event. This was a heart attack that no one could see coming. But when we look at the information there, we can see that things are starting to become a little fuzzy about five minutes or so before the cardiac arrest. We can see _____ changes in things like the heart rate moving. These were all undetected by ______ thresholds which would be applied to data. So the question is, why couldn't we see it? Was this a predictable event? Can we look more at the patterns in the data to be able to do things better? So this is a child, about the same age as the racing car on stage, three months old. It's a patient with a heart problem. Now, when you look at some of the data on the screen above, things like heart rate, _____, ______, ___________ rates, they're all _______ for a normal child, but they're quite normal for the child there, and so one of the challenges you have in health care is, how can I look at the patient in front of me, have something which is ________ for her, and be able to detect when things start to change, when things _____ to deteriorate? Because like a ______ car, any patient, when things start to go bad, you have a short time to make a difference. So what we did is we took a data ______ which we run every two weeks of the year in Formula 1 and we _________ it on the hospital _________ at __________ Children's Hospital. We streamed data from the bedside instruments in their pediatric intensive care so that we could both look at the data in real time and, more importantly, to store the data so that we could start to learn from it. And then, we applied an application on top which would allow us to tease out the patterns in the data in real time so we could see what was happening, so we could determine when things started to change. Now, in motor racing, we're all a little bit ambitious, audacious, a little bit arrogant sometimes, so we decided we would also look at the children as they were being transported to intensive care. Why should we wait until they arrived in the hospital before we started to look? And so we installed a real-time link between the _________ and the hospital, just using normal 3G telephony to send that data so that the ambulance became an extra bed in intensive care. And then we started looking at the data. So the ______ lines at the top, all the colors, this is the normal sort of data you would see on a _______ — _____ rate, pulse, oxygen within the blood, and respiration. The lines on the ______, the blue and the red, these are the interesting ones. The red line is showing an _________ version of the _____ warning score that Birmingham Children's ________ were already running. They'd been running that since 2008, and already have _______ cardiac _______ and distress within the hospital. The blue line is an __________ of when patterns start to change, and ___________, before we even _______ putting in clinical interpretation, we can see that the data is speaking to us. It's telling us that something is going wrong. The plot with the red and the _____ blobs, this is plotting different components of the data against each other. The green is us learning what is normal for that child. We call it the cloud of _________. And when things start to ______, when conditions start to deteriorate, we move into the red line. There's no rocket science here. It is displaying data that exists already in a different way, to amplify it, to provide cues to the doctors, to the nurses, so they can see what's happening. In the same way that a good racing driver relies on cues to ______ when to apply the brakes, when to turn into a corner, we need to help our __________ and our ______ to see when things are starting to go wrong. So we have a very ambitious program. We think that the race is on to do something differently. We are thinking big. It's the right thing to do. We have an approach which, if it's successful, there's no reason why it should stay within a hospital. It can go beyond the walls. With wireless connectivity these days, there is no reason why patients, doctors and nurses always have to be in the same place at the same time. And meanwhile, we'll take our little three-month-old baby, keep taking it to the _____, keeping it safe, and making it ______ and better. Thank you very much. (________)

Solution

  1. ambulance
  2. faster
  3. green
  4. motor
  5. early
  6. million
  7. monitor
  8. logging
  9. small
  10. birmingham
  11. child
  12. decide
  13. knowledge
  14. unusual
  15. change
  16. hospital
  17. system
  18. heart
  19. normality
  20. track
  21. wrong
  22. formula
  23. respiration
  24. automated
  25. words
  26. indication
  27. physicians
  28. engine
  29. started
  30. stopped
  31. bottom
  32. applause
  33. unpredictable
  34. computers
  35. spend
  36. specific
  37. oxygen
  38. thousand
  39. immediately
  40. nurses
  41. normal
  42. components
  43. measure
  44. turning
  45. installed
  46. arrests
  47. catastrophic
  48. spent
  49. racing
  50. weeks
  51. pulse
  52. wiggly
  53. arrest
  54. start

Original Text

Motor racing is a funny old business. We make a new car every year, and then we spend the rest of the season trying to understand what it is we've built to make it better, to make it faster. And then the next year, we start again. Now, the car you see in front of you is quite complicated. The chassis is made up of about 11,000 components, the engine another 6,000, the electronics about eight and a half thousand. So there's about 25,000 things there that can go wrong. So motor racing is very much about attention to detail. The other thing about Formula 1 in particular is we're always changing the car. We're always trying to make it faster. So every two weeks, we will be making about 5,000 new components to fit to the car. Five to 10 percent of the race car will be different every two weeks of the year. So how do we do that? Well, we start our life with the racing car. We have a lot of sensors on the car to measure things. On the race car in front of you here there are about 120 sensors when it goes into a race. It's measuring all sorts of things around the car. That data is logged. We're logging about 500 different parameters within the data systems, about 13,000 health parameters and events to say when things are not working the way they should do, and we're sending that data back to the garage using telemetry at a rate of two to four megabits per second. So during a two-hour race, each car will be sending 750 million numbers. That's twice as many numbers as words that each of us speaks in a lifetime. It's a huge amount of data. But it's not enough just to have data and measure it. You need to be able to do something with it. So we've spent a lot of time and effort in turning the data into stories to be able to tell, what's the state of the engine, how are the tires degrading, what's the situation with fuel consumption? So all of this is taking data and turning it into knowledge that we can act upon. Okay, so let's have a look at a little bit of data. Let's pick a bit of data from another three-month-old patient. This is a child, and what you're seeing here is real data, and on the far right-hand side, where everything starts getting a little bit catastrophic, that is the patient going into cardiac arrest. It was deemed to be an unpredictable event. This was a heart attack that no one could see coming. But when we look at the information there, we can see that things are starting to become a little fuzzy about five minutes or so before the cardiac arrest. We can see small changes in things like the heart rate moving. These were all undetected by normal thresholds which would be applied to data. So the question is, why couldn't we see it? Was this a predictable event? Can we look more at the patterns in the data to be able to do things better? So this is a child, about the same age as the racing car on stage, three months old. It's a patient with a heart problem. Now, when you look at some of the data on the screen above, things like heart rate, pulse, oxygen, respiration rates, they're all unusual for a normal child, but they're quite normal for the child there, and so one of the challenges you have in health care is, how can I look at the patient in front of me, have something which is specific for her, and be able to detect when things start to change, when things start to deteriorate? Because like a racing car, any patient, when things start to go bad, you have a short time to make a difference. So what we did is we took a data system which we run every two weeks of the year in Formula 1 and we installed it on the hospital computers at Birmingham Children's Hospital. We streamed data from the bedside instruments in their pediatric intensive care so that we could both look at the data in real time and, more importantly, to store the data so that we could start to learn from it. And then, we applied an application on top which would allow us to tease out the patterns in the data in real time so we could see what was happening, so we could determine when things started to change. Now, in motor racing, we're all a little bit ambitious, audacious, a little bit arrogant sometimes, so we decided we would also look at the children as they were being transported to intensive care. Why should we wait until they arrived in the hospital before we started to look? And so we installed a real-time link between the ambulance and the hospital, just using normal 3G telephony to send that data so that the ambulance became an extra bed in intensive care. And then we started looking at the data. So the wiggly lines at the top, all the colors, this is the normal sort of data you would see on a monitor — heart rate, pulse, oxygen within the blood, and respiration. The lines on the bottom, the blue and the red, these are the interesting ones. The red line is showing an automated version of the early warning score that Birmingham Children's Hospital were already running. They'd been running that since 2008, and already have stopped cardiac arrests and distress within the hospital. The blue line is an indication of when patterns start to change, and immediately, before we even started putting in clinical interpretation, we can see that the data is speaking to us. It's telling us that something is going wrong. The plot with the red and the green blobs, this is plotting different components of the data against each other. The green is us learning what is normal for that child. We call it the cloud of normality. And when things start to change, when conditions start to deteriorate, we move into the red line. There's no rocket science here. It is displaying data that exists already in a different way, to amplify it, to provide cues to the doctors, to the nurses, so they can see what's happening. In the same way that a good racing driver relies on cues to decide when to apply the brakes, when to turn into a corner, we need to help our physicians and our nurses to see when things are starting to go wrong. So we have a very ambitious program. We think that the race is on to do something differently. We are thinking big. It's the right thing to do. We have an approach which, if it's successful, there's no reason why it should stay within a hospital. It can go beyond the walls. With wireless connectivity these days, there is no reason why patients, doctors and nurses always have to be in the same place at the same time. And meanwhile, we'll take our little three-month-old baby, keep taking it to the track, keeping it safe, and making it faster and better. Thank you very much. (Applause)

Frequently Occurring Word Combinations

ngrams of length 2

collocation frequency
intensive care 3
motor racing 2
race car 2
racing car 2
cardiac arrest 2
real time 2
red line 2

Important Words

  1. act
  2. age
  3. ambitious
  4. ambulance
  5. amount
  6. amplify
  7. applause
  8. application
  9. applied
  10. apply
  11. approach
  12. arrest
  13. arrests
  14. arrived
  15. arrogant
  16. attack
  17. attention
  18. audacious
  19. automated
  20. baby
  21. bad
  22. bed
  23. bedside
  24. big
  25. birmingham
  26. bit
  27. blobs
  28. blood
  29. blue
  30. bottom
  31. brakes
  32. built
  33. business
  34. call
  35. car
  36. cardiac
  37. care
  38. catastrophic
  39. challenges
  40. change
  41. changing
  42. chassis
  43. child
  44. children
  45. clinical
  46. cloud
  47. colors
  48. coming
  49. complicated
  50. components
  51. computers
  52. conditions
  53. connectivity
  54. consumption
  55. corner
  56. cues
  57. data
  58. days
  59. decide
  60. decided
  61. deemed
  62. degrading
  63. detail
  64. detect
  65. deteriorate
  66. determine
  67. difference
  68. differently
  69. displaying
  70. distress
  71. doctors
  72. driver
  73. early
  74. effort
  75. electronics
  76. engine
  77. event
  78. events
  79. exists
  80. extra
  81. faster
  82. fit
  83. formula
  84. front
  85. fuel
  86. funny
  87. fuzzy
  88. garage
  89. good
  90. green
  91. happening
  92. health
  93. heart
  94. hospital
  95. huge
  96. immediately
  97. importantly
  98. indication
  99. information
  100. installed
  101. instruments
  102. intensive
  103. interesting
  104. interpretation
  105. keeping
  106. knowledge
  107. learn
  108. learning
  109. life
  110. lifetime
  111. line
  112. lines
  113. link
  114. logged
  115. logging
  116. lot
  117. making
  118. measure
  119. measuring
  120. megabits
  121. million
  122. minutes
  123. monitor
  124. months
  125. motor
  126. move
  127. moving
  128. normal
  129. normality
  130. numbers
  131. nurses
  132. oxygen
  133. parameters
  134. patient
  135. patients
  136. patterns
  137. pediatric
  138. percent
  139. physicians
  140. pick
  141. place
  142. plot
  143. plotting
  144. predictable
  145. problem
  146. program
  147. provide
  148. pulse
  149. putting
  150. question
  151. race
  152. racing
  153. rate
  154. rates
  155. real
  156. reason
  157. red
  158. relies
  159. respiration
  160. rest
  161. rocket
  162. run
  163. running
  164. safe
  165. science
  166. score
  167. screen
  168. season
  169. send
  170. sending
  171. sensors
  172. short
  173. showing
  174. side
  175. situation
  176. small
  177. sort
  178. sorts
  179. speaking
  180. speaks
  181. specific
  182. spend
  183. spent
  184. stage
  185. start
  186. started
  187. starting
  188. starts
  189. state
  190. stay
  191. stopped
  192. store
  193. stories
  194. streamed
  195. successful
  196. system
  197. systems
  198. tease
  199. telemetry
  200. telephony
  201. telling
  202. thinking
  203. thousand
  204. thresholds
  205. time
  206. tires
  207. top
  208. track
  209. transported
  210. turn
  211. turning
  212. understand
  213. undetected
  214. unpredictable
  215. unusual
  216. version
  217. wait
  218. walls
  219. warning
  220. weeks
  221. wiggly
  222. wireless
  223. words
  224. working
  225. wrong
  226. year