Will Computers Ever Be as Good as Physicians at Diagnosing Patients?
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FM","link":"/"}},"futureofyou_256816":{"type":"posts","id":"futureofyou_256816","meta":{"index":"posts_1591205157","site":"futureofyou","id":"256816","score":null,"sort":[1485624645000]},"guestAuthors":[],"slug":"how-technology-ruined-the-radiology-profession","title":"Has Technology Ruined the Radiology Profession?","publishDate":1485624645,"format":"image","headTitle":"KQED Future of You | KQED Science","labelTerm":{},"content":"\u003cp>\u003cem>This is an edited excerpt from Robert Wachter's \"\u003cem>\u003ca href=\"https://www.amazon.com/Digital-Doctor-Hope-Medicines-Computer/dp/0071849467\" target=\"_blank\" rel=\"noopener\">The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine's Computer Age\u003c/a>\u003c/em>,\" reprinted with permission from McGraw-Hill. Copyright 2015.\u003c/em>\u003c/p>\n\u003cp>\u003cspan style=\"font-size: 4.6875em;float: left;line-height: 0.733em;padding: 0.05em 0.1em 0 0;font-family: times, serif, georgia\">W\u003c/span>hen I was a medical student in the 1980s, the beating heart of the Hospital of the University of Pennsylvania was not the mahogany-lined executive suite, nor the dazzling operating room of L. Henry Edmunds, Jr., HUP's most famed cardiac surgeon. No, it was in the decidedly unglamorous, dimly lit Chest Reading room, where all the X-rays were hung on a moving contraption called an alternator that resembled the one on which the clothes hang at your local dry cleaner. Controlled by a seated radiologist operating a foot pedal, the machine would cycle through panel after panel until it arrived at your films. The radiologist took his foot off the pedal, the machine ground to a halt, and the dark X-ray sheets were brought to life by intense backlighting.\u003c/p>\n\u003cp>At Penn in the 1980s, everybody — and I mean everybody, from the lowliest student to the loftiest transplant surgeon — brought films for deciphering to the late Wallace Miller, Sr., a crusty but endearing professor of radiology and one of the best teachers I've ever known. For students like me, time spent with him was at once exhilarating and terrifying. \"What's this opacity?\" he asked me once, the memory burned into my hippocampus by that cognitive curing process known as overwhelming anxiety. \"A ... a pneumonia?\" I stammered.\u003c/p>\n\u003cp>\"Mooiaaa,\" retorted The Oracle, an unforgettable signature sound uttered as Miller smartly turned his head away in mock disgust. I loved it. We all did.\u003c/p>\n\u003caside class=\"pullquote alignright\">'One day I tried to see if I could go the whole day without speaking to anyone. And that’s what happened—I didn’t speak to a single person.'\u003ccite>A radiologist\u003c/cite>\u003c/aside>\n\u003cp>Today, many of my internal medicine trainees barely know where the radiology department is. Just as your record player and LPs are now long gone, in your local hospital today, the films, the analog X-ray machines, and even those charming film conveyor belts have left the building.\u003c/p>\n\u003cp>[ad fullwidth]\u003c/p>\n\u003cp>Why? In 2000, only 8 percent of U.S. hospitals had some version of a game-changing computer technology called the Picture Archiving and Communications System, or PACS. By 2008, more than three out of four did.\u003c/p>\n\u003cp>Because radiology was the first medical specialty to computerize, what has happened to it — at once shocking and, in retrospect, entirely predictable — is our canary in the digital coal mine, its experience offering important lessons for patients, clinicians and health care systems.\u003c/p>\n\u003cp>\u003cstrong>The Beauty of PACS\u003c/strong>\u003c/p>\n\u003cp>While the main catalyst for PACS was economic, the quality of the images and the ability to manipulate them were also important. Unlike regular films, CT scans need to be viewed at various contrast levels: One setting is best to look at bones, another to look at lungs, and still another to look at soft tissue like muscle.\u003c/p>\n\u003cp>PACS allowed radiologists to toggle through these views, in the same way that Instagram lets you play with your photos. You can also use a nifty magnifying glass to zoom in on a part of the image. An unexpected benefit was “stacking”: rather than looking at 100 images arrayed in a 10 × 10 grid on a one-dimensional page, the images could be digitally stacked, one on top of another, allowing the radiologist to scroll through them swiftly by rolling a mouse ball. Moreover, computerization let the radiologist look at the images from home, enabling senior experts to weigh in on subtle findings that trainees might flub. And while the images were fuzzy at first, today they’re as crisp as high-definition television.\u003c/p>\n\u003cp>Perhaps most important, PACS obviated the need for maddening searches for prior X-rays. Twenty years ago, when a chest X-ray revealed a lung nodule, the first commandment on the radiologist’s report was to “obtain old films.” The rationale: If the nodule had been unchanged for many years, it could safely be ignored—such stability simply wasn’t consistent with a diagnosis of cancer. But searching for old films was often an exercise in frustration: They were lost, or locked up, or at another institution, or in a filing cabinet in the thoracic surgeon’s garage, behind the golf clubs. When my colleagues and I came up empty-handed, which was more often than not, the patient frequently paid the price in the form of an unnecessary biopsy. But PACS made finding old films a breeze (assuming that they were done at the same hospital or had been scanned into the system); they’re usually just a click away.\u003c/p>\n\u003cp>While PACS was widely anticipated and generally accepted by radiologists, some prescient observers worried that computerization might lead to unbidden effects on the field. In 1999, Stephen Baker, chair of the Department of Radiology at New Jersey Medical School, fretted that PACS might turn radiologists into “disembodied functionaries, more akin to servicing technicians than professional colleagues.” Paul Chang, professor of radiology at the University of Chicago and an early leader in digital radiology, describes the day his father, a retired radiologist, took him to task.\u003c/p>\n\u003cp>“Before PACS, we were the doctor’s doctor,” his father berated him. “Medicine and surgery rounds started in radiology. . . . Every morning the clinicians and the radiologists collaborated.”\u003c/p>\n\u003cp>His father’s less-than-endearing nickname for his famous son: “The Man Who Ruined Radiology.”\u003c/p>\n\u003cp>The advantages of PACS are so vast that few would want to turn back the clock. Yet the effects on those of us who order X-rays and the radiologists who read them have been profound, and they’re not all positive. The fact that we can now review our images without trekking down to radiology means that we rarely do make the trip.\u003c/p>\n\u003cp>https://www.youtube.com/watch?v=fHUzVqoDnts&ab_channel=DoctorKlioze\u003c/p>\n\u003cp>\u003cstrong>An Awkward Trip to Radiology\u003c/strong>\u003c/p>\n\u003cp>A few years ago, when I asked my interns and students to visit the radiology department to review the key films, they looked at me as if I had grown a second head. After my team humored me by accompanying me to the radiology department, I conducted a little sociology experiment. Standing outside my hospital’s chest reading room, I delivered a brief speech:\u003c/p>\n\u003caside class=\"pullquote alignright\">'Did you look at the official report?' he hissed. The unspoken message was clear: Get out of my space; I’m busy.\u003c/aside>\n\u003cp>\"Watch what happens when we enter. Does anybody turn around and welcome us, ask, 'How can I help you?' and seem genuinely enthusiastic? When they go over the X-ray, do they delve a layer deeper than what they said in the formal report? Do they make any teaching points? Does the radiologist suggest courses of action or ask provocative questions?\"\u003c/p>\n\u003cp>I did this because I am deeply concerned that mine is the last generation to have learned the habit of going to the radiology department. Nostalgic for my interactions with Wally Miller and his like, it saddens me that our current trainees will never know how much they can learn from a great radiology teacher, and how much their patients’ care can be improved by actually talking to a real live radiologist. Yet I know that even if I bring my young horses to water, whether they visit the radiology department after I am no longer their wrangler will be determined by the quality of their experience.\u003c/p>\n\u003cp>We entered the chest reading room and were greeted by a wall of radiologists’ backs, their faces trained like lasers on the computer screens in front of them. Not a single head—located atop the shoulders of about eight different radiologists—turned to greet us.\u003c/p>\n\u003cp>After a couple of awkward minutes of crescendo throat-clearing, one of the radiologists grudgingly swiveled around to face my team and me. “Oh, do you need something?” he asked.\u003c/p>\n\u003cp>“Sure; can you help us look at a few films?”\u003c/p>\n\u003cp>He did, kind of, but offered his help in a whisper animated mostly by passive aggressiveness.\u003c/p>\n\u003cp>I thought it couldn’t get any worse, but it did.\u003c/p>\n\u003cp>“What do you think of this area?” I asked him, pointing to a confusing patch of whiteness on one patient’s chest CT scan.\u003c/p>\n\u003cp>“Did you look at the official report?” he hissed. (In other words: \"Perhaps you don’t know how to turn on your computer?\")\u003c/p>\n\u003cp>The unspoken message was clear: Get out of my space; I’m busy.\u003c/p>\n\u003cp>Now, I understand that he might well be busy, and that it has to be annoying having clinicians interrupt you every few minutes to go over images, particularly after you’ve just reviewed them with a different set of specialists and dictated a report. But that is the radiologist’s job. Or at least it used to be.\u003c/p>\n\u003cp>Allison Tillack, a young radiologist and a medical anthropologist whose Ph.D. thesis involved observing the world of radiologists for a year at a prominent academic hospital, has explored how the computerization of radiology has transformed the worlds of radiologists and those who use their services.\u003c/p>\n\u003cp>\"The ability of PACS to alter the accessibility and tempo of medical imaging has resulted in visits to the reading room being viewed now by non-radiology clinicians as a ‘waste of time’ and by radiologists as an ‘interruption,’” she wrote.\u003c/p>\n\u003cp>\u003cstrong>In a Funk\u003c/strong>\u003c/p>\n\u003cp>While I was well aware of the changing perceptions of radiology by nonradiologists, I had not, until I met Tillack, appreciated the degree to which the field of radiology is itself in a PACS-fueled funk.\u003c/p>\n\u003cp>After all, the field remains extremely popular among medical students, as many perceive it as offering the perfect blend of “great lifestyle” (that is, banker’s hours and limited overnight call) and high income, which averaged $340,000 in 2013. In fact, it’s often said that today’s medical students are attracted to the “ROAD specialties”: Radiology, Ophthalmology, Anesthesiology, and Dermatology, all of which are lucrative and none of which involves a lot of contact with those pesky sick people. In her research, Tillack found that the vast majority of radiologists and radiology residents identified the lack of direct patient contact as one of the main attractions of the field.\u003c/p>\n\u003cp>Given all these pluses, many frontline clinicians think of radiologists as having “won the game.” Yet I should have gotten a hint of the field’s handwringing in 2005, when I saw the results of a survey of physicians regarding their satisfaction with their chosen specialty. The happiest doctors were radiation oncologists (the folks who deliver radiation therapy to cancer patients), who do satisfying work, earn a good income, and have predictable hours. The least happy were cardiac surgeons, who train forever and, in recent years, have seen much of their business eroded by stents and other nonsurgical approaches to heart disease.\u003c/p>\n\u003cp>Radiologists show up a bit below the mean on the satisfaction scale—just behind the perennially overwhelmed and undercompensated primary care doctors.\u003c/p>\n\u003cp>In a 2012 paper, Tillack and a colleague described “the loneliness of the long distance radiologist.” One radiologist told them, “Before, I knew the face, name, wife’s name, and kids’ names of all the clinicians, but now I don’t know who you are if you joined the medical staff after we got PACS. . . . Before, when a clinician showed up, I could ask them and find out what’s really going on with the patient.”\u003c/p>\n\u003cp>I hear similar stories from every radiologist I meet. Patrick Luetmer, a Mayo Clinic neuroradiologist, described what happened when his MRI suite was remodeled. The suite was originally configured with two MRI “donuts” (the huge magnets that are responsible for the image) on either side of a central workstation in which Luetmer sat. There, he could monitor the scans as they were being performed, and talk to both the patients and the radiology assistants. Clinicians sometimes wandered down to look at the scans with him.\u003c/p>\n\u003cp>A few years ago, as part of a big efficiency push, Mayo decided that a third MRI machine was a better use of that central area than the radiologist’s air traffic control desk. Luetmer’s workstation was moved to an office a few hundred feet away, where he could follow the scans on his computer monitor and communicate with the techs via a special text messaging system.\u003c/p>\n\u003cp>“One day I tried to see if I could go the whole day without speaking to anyone. And that’s what happened—I didn’t speak to a single person. It was incredibly isolating.”\u003c/p>\n\u003cfigure id=\"attachment_264998\" class=\"wp-caption aligncenter\" style=\"max-width: 640px\">\u003ca href=\"http://ww2.kqed.org/futureofyou/wp-content/uploads/sites/13/2019/10/radiology2.jpg\">\u003cimg class=\"size-large wp-image-264998\" src=\"http://ww2.kqed.org/futureofyou/wp-content/uploads/sites/13/2019/10/radiology2-1180x953.jpg\" alt=\"1950s-era radiology at the U.S. Naval Hospital, Charleston, South Carolina.\" width=\"640\" height=\"517\" srcset=\"https://ww2.kqed.org/app/uploads/sites/13/2019/10/radiology2-1180x953.jpg 1180w, https://ww2.kqed.org/app/uploads/sites/13/2019/10/radiology2-400x323.jpg 400w, https://ww2.kqed.org/app/uploads/sites/13/2019/10/radiology2-743x600.jpg 743w, https://ww2.kqed.org/app/uploads/sites/13/2019/10/radiology2-768x620.jpg 768w, https://ww2.kqed.org/app/uploads/sites/13/2019/10/radiology2-1920x1550.jpg 1920w, https://ww2.kqed.org/app/uploads/sites/13/2019/10/radiology2-960x775.jpg 960w\" sizes=\"(max-width: 640px) 100vw, 640px\">\u003c/a>\u003cfigcaption class=\"wp-caption-text\">1950s-era radiology at the U.S. Naval Hospital, Charleston, South Carolina. \u003ccite>(National Library of Medicine)\u003c/cite>\u003c/figcaption>\u003c/figure>\n\u003cp>The radiologists were lonely, sure, but their situation involves something far deeper. Hari Tsoukas, an expert in organizational studies at the University of Cyprus, highlights the distinction between “information” and “knowledge.” Information, he wrote, “consists of objectified, decontextualized, time-less, impersonal, value-free representations,” whereas knowledge is “context-dependent, personalized, time-bound, and infused with values.”\u003c/p>\n\u003cp>Adds Tillack, “Hunches, hypotheses, frustrations with patients or their families, second guessing, judging of colleagues, and similar activities that mark how uncertainty is negotiated on a daily basis in medical practice are rarely reflected in the medical record . . . this knowledge can only circulate in private or semi-private contexts—by its very definition, this knowledge cannot be made a matter of public record.”\u003c/p>\n\u003cp>By purging the informal conversations during which such information was exchanged, the emergence of PACS left radiologists information-rich (Think of all those pixels! And old films just a click away!), but knowledge-poor.\u003c/p>\n\u003cp>\u003cstrong>'Great Case. Next Case.'\u003c/strong>\u003c/p>\n\u003cp>Radiologists’ alienation runs deeper than the lack of collegial exchange and the inability to find out what’s really going on with the patients. It’s also about power, status, and expertise. The fact that the traditional film lived only in the radiology reading room gave radiologists a monopoly over their entire ecosystem. PACS, observes Tillack, created a new normal in which “the ‘right’ to see [the image] is no longer mediated by radiologists, as it was in the reading room,” and has thus “eroded radiologists’ claims for authoritative knowledge over the interpretation of medical images.”\u003c/p>\n\u003cp>Once the radiology department no longer housed the films, the impact was immediate and dramatic. Without any changes in policy or very much forethought, the mid-1990s transition to filmless operations at the Baltimore VA hospital led to an 82 percent decrease in in-person consultation rates for general radiology studies. Today, many clinicians—particularly specialists like neurologists, pulmonologists, and surgeons—look at images themselves and act on their own interpretations; Many don’t even bother to read the radiologist’s formal report (which usually takes several hours, sometimes even a day, to reach the chart) unless they have unanswered questions or judge the study to be particularly challenging.\u003c/p>\n\u003cp>PACS was to increase efficiency, but that virtue has also become a curse, as radiologists increasingly feel like Lucy and Ethel on the assembly line of the chocolate factory. Among teleradiologists (radiologists reading x-rays from a distant site, often covering emergency departments at night while the hospital’s own radiologists are sleeping), there’s a well-known adage that captures the relentless objectification of their modern predicament: “Great case. Next case.\" As with so many other aspects of our modern digital lives, PACS sped up the clock, and did so without mercy.\u003c/p>\n\u003cp>That clock is constantly ticking. “Instead of waiting for films to be acquired, printed, sorted, and hung, radiologists now are always playing catch-up, looking at more ‘stuff’ in less time,” observed Tillack. That miraculous access to old films also creates an obligation for the radiologist to actually review them.\u003c/p>\n\u003cp>And it’s not just the old films that need to be examined; PACS makes vast amounts of information available with every study. In the early days of CT, the output of a scan might have been about 12 “slices,” each one representing a ¼-inch section through the thorax or abdomen, akin to a thick slice of deli-cut salami. But today’s ultra-fast CT scanners can produce images of more than 50 slices per inch of the human body, more like ultra-thin cuts of prosciutto. And PACS, with its massive memory bank and blazingly fast transmission speeds,can easily display every slice, which means that the radiologist has to scroll through hundreds of images in order to read a single CT study. This combination of more information in each scan, more old studies to compare, and more time pressure is unremitting.\u003c/p>\n\u003cp>The clock is ticking for other reasons as well. Since the image is available to the ordering clinician the moment it is created, radiologists feel obliged to perform their review quickly lest their reading seem like old news, like an afternoon newspaper in the Age of Twitter. Piling on, after recognizing the efficiency of PACS, insurance companies and Medicare slashed the reimbursement for each interpretation, pushing radiologists to read more films in less time in order to maintain their incomes. Said one radiologist, “With PACS, work is busier now. We have 70 percent more cases to read than 10 years ago. . . . At the end of the day . . . I’m fried.”\u003c/p>\n\u003cp>On top of this, there are even greater threats to radiologists’ livelihoods and happiness. One of them flows from the growing pressure on health care systems to slash their costs. Currently, virtually every X-ray performed at a U.S. hospital is sent for a formal reading by a radiologist, who is paid a fee by an insurance company. In today’s cost-cutting environment, it’s probably only a matter of time before some health care systems permit their frontline specialists to officially read certain films, reserving radiologist “overreads” for those images that the clinicians have questions about or the ones with super-high malpractice risk if they are misread. Radiologists can be counted on to fight such a move by frantically waving the banner of quality, but they will need to demonstrate that the value of having them review every film is worth the considerable expense.\u003c/p>\n\u003cp>Moreover, a major theme of Obama-era health reform is a shift from our historical fee-for-service, piecework payment model to one that dispenses a single payment to a hospital and doctors to manage all the care for a group of patients (“accountable care organizations,” ACOs for short) or a given episode of disease (“bundled payments”). Under such systems, the risk for the cost of care shifts from the insurer to the providers, and it’s up to the latter to decide how to divvy up the cash. Ron Arenson, chairman of the department of radiology at the University of California, San Francisco, sees this as the greatest threat to his field.\u003c/p>\n\u003cp>“If the world moves to bundled payments, we won’t do well,” he said. “We’re not very high in the pecking order.”\u003c/p>\n\u003cp>Some nonradiologists, particularly ER doctors working nights and weekends, have little sympathy for their colleagues’ new predicament. In fact, they have begun to wonder why radiologists should be compensated for next-day readings when they’ve already looked at the images themselves, acted on their interpretations, and assumed the risk of being sued if anything goes wrong. In a 2011 editorial entitled “The Life Cycle of a Parasitic Specialist,” ER physician William Mallon took off the gloves.\u003c/p>\n\u003cblockquote>\u003cp>[On Monday morning] these parasites will commence to feed on the financial juices of the lowly unfortunate emergency physicians, who had to work the entire weekend without radiologic support or backup. . . . The radiologist arrives well rested, café latte in hand, and promptly installs himself in a dark room to re-read and bill for all the films the emergency physicians read over the weekend. . . . Never has a specialty done so little for so many and been paid so much.\u003c/p>\u003c/blockquote>\n\u003cp>Ouch.\u003c/p>\n\u003cp>Another challenge to radiology made possible by the death of film has come in the form of teleradiology. Once X-rays went digital, it was no longer crucial for radiologists to be in the same building as the patient or the treating clinicians. As a result, many multihospital systems consolidated their reading rooms, particularly on weekends and nights, with centralized radiologists supporting multiple sites. Predictably, once the technical challenges of connectivity were solved, teleradiology companies emerged to fill this need. As is often the case with contented “legacy” providers (in health care and other industries), traditional radiologists were only too happy to have their colleagues read their films during off-hours. Who wouldn’t be?\u003c/p>\n\u003cp>The playing field soon expanded across national borders, as radiologists in Zurich, Israel, and Singapore began to read nighttime X-rays for American hospitals during their own local daytimes. Hundreds of hospitals now use these \"nighthawks,\" and everybody seems happy about it, including the domestic radiologists, who are sleeping soundly while the overnight images are read half a world away.\u003c/p>\n\u003cp>But one wonders whether this is the start of so-called disruptive innovation, the concept made famous by Harvard’s Clay Christensen. Disruption often begins with a fat and happy incumbent content to preserve its existing enviable position in a market. In industries ranging from commercial aviation to steel manufacturing, an upstart comes in and grabs an unattractive part of the market (in this case, nights and weekends). But once a low-cost company has squeezed through a crack to capture a slice of a previously locked franchise, it is rarely content to stay put. With the average U.S. radiologist earning about $350,000 per year and the average Indian radiologist earning less than one-tenth of that, one wonders whether the same World-is-Flat forces that have revolutionized other industries but mostly bypassed health care will be unleashed.\u003c/p>\n\u003cp>This is where radiologists’ loss of trust and collegiality with other clinicians may exact its heaviest toll. “Some people see teleradiology as a big threat, but I don’t,” UCSF’s Arenson told me. “I think that relationships with radiologists are important.” I do too, which is why I believe he may have his head in the sand: If physicians don’t get much out of visiting the radiology department or have even forgotten where it is located, we have little reason to fight to keep it in our buildings. Or, for that matter, our country.\u003c/p>\n\u003cp>Like all legacy providers faced with a technological or global workforce threat, radiologists can be counted on to argue that quality would take a huge hit if we outsourced their work to less expensive providers, domestic or foreign. The degree to which the field has accepted nighttime readings from non-U.S. radiologists will, of course, undermine this argument. It’s hard to make the claim that a Bangalore-based teleradiologist is sufficiently competent to read an image for your hospital at 3 a.m., but not at 3 p.m.\u003c/p>\n\u003cp>\u003cstrong>The Ultimate Threat\u003c/strong>\u003c/p>\n\u003cp>Finally, there is the ultimate threat: replacement by the machine. Of course, this issue is marbled throughout health care as we enter the digital age. To date, most claims that “this technology will replace doctors” (in areas ranging from diagnostic reasoning to robotic surgery) have proven to be hype.\u003c/p>\n\u003cp>However, in fields that are primarily about visual pattern recognition, the promise (or, if you’re a radiologist, the threat) is much more real. Studies have shown that computers can detect significant numbers of breast cancers and pulmonary emboli missed by radiologists, although nobody has yet taken the bold step of having the computers completely supplant the humans, partly because there are armadas of malpractice attorneys waiting to pounce, and partly because, at least for now, the combination of human and machine seems to perform better than either alone.\u003c/p>\n\u003cp>But over the long haul, I wouldn’t bet on the humans here, particularly since one of the hottest areas in artificial intelligence research is “deep learning”—research that has created computers that are reasonably skilled at “reading,” “hearing,” and, yes, “seeing.” The same kind of software that now allows Facebook to guess that a certain collection of pixels is a picture of you, or that alerts the casino’s security guards to keep an eye on that guy, is likely to eventually crack the code in radiology, and in similar areas such as dermatology and pathology.\u003c/p>\n\u003cp>Slowly, radiologists are waking up to their peril. Rather than isolating themselves from clinical care, some are now relocating their reading stations in clinical areas, such as the ER and the ICU, to be in the line of sight of their clinician colleagues. Others are resurrecting interdisciplinary conferences and training their staff in customer service. Technological solutions that allow radiologists and frontline clinicians to communicate through PACS and the electronic health record are springing up (through programs that create a mash-up of a Skype-like communication tool and a John Madden–style telestrator).\u003c/p>\n\u003cp>Said Paul Chang, the University of Chicago radiologist whose advocacy of PACS so upset his father, “We have to go beyond isolating ourselves and concentrating on messages in a bottle, where we just write a report and are done with it, but instead fostering collaboration.”\u003c/p>\n\u003cp>Since radiology represents such a large segment of health care expenditures (and also a source of harm through the risk of radiation), some health care systems are even putting radiologists in the position of being gatekeepers to their own technology. It’s a role they might not have accepted in the past, but, faced with an existential threat, many now welcome it. Said David Levin, a radiologist at Thomas Jefferson University, “We have to act more like consulting physicians . . . to look at the appropriateness of the requests for advanced imaging studies . . . rather than just going ahead and doing the study.”\u003c/p>\n\u003cp>Radiology’s experience over the past 15 years offers a crystal ball for the rest of the health care system. The speed with which computerization unleashed a series of forces that completely transformed an established field would be all too familiar to travel agents, journalists, and others who have been run over by the digital bulldozer, but it has shocked many health care observers, even astute ones.\u003c/p>\n\u003cp>[ad floatright]\u003c/p>\n\u003cp>Will the computerization of the rest of medicine similarly upend the lives of other kinds of doctors, as well as their patients? The early returns are in, and the answer is yes.\u003c/p>\n\n","blocks":[],"excerpt":"Because radiology was the first medical specialty to computerize, what has happened to it -- at once shocking and, in retrospect, entirely predictable -- is our canary in the digital coal mine.","status":"publish","parent":0,"modified":1514581391,"stats":{"hasAudio":false,"hasVideo":true,"hasChartOrMap":false,"iframeSrcs":[],"hasGoogleForm":false,"hasGallery":false,"hasHearkenModule":false,"hasPolis":false,"paragraphCount":71,"wordCount":4601},"headData":{"title":"Has Technology Ruined the Radiology Profession? | KQED","description":"Because radiology was the first medical specialty to computerize, what has happened to it -- at once shocking and, in retrospect, entirely predictable -- is our canary in the digital coal mine.","ogTitle":"","ogDescription":"","ogImgId":"","twTitle":"","twDescription":"","twImgId":"","schema":{"@context":"http://schema.org","@type":"Article","headline":"Has Technology Ruined the Radiology Profession?","datePublished":"2017-01-28T17:30:45.000Z","dateModified":"2017-12-29T21:03:11.000Z","image":"https://cdn.kqed.org/wp-content/uploads/2020/02/KQED-OG-Image@1x.png"}},"disqusIdentifier":"256816 http://ww2.kqed.org/futureofyou/?p=256816","disqusUrl":"https://ww2.kqed.org/futureofyou/2017/01/28/how-technology-ruined-the-radiology-profession/","disqusTitle":"Has Technology Ruined the Radiology Profession?","source":"Future of You","customPermalink":"2016/10/25/technology-radiology/","nprByline":"Bob Wachter","path":"/futureofyou/256816/how-technology-ruined-the-radiology-profession","audioTrackLength":null,"parsedContent":[{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003cp>\u003cem>This is an edited excerpt from Robert Wachter's \"\u003cem>\u003ca href=\"https://www.amazon.com/Digital-Doctor-Hope-Medicines-Computer/dp/0071849467\" target=\"_blank\" rel=\"noopener\">The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine's Computer Age\u003c/a>\u003c/em>,\" reprinted with permission from McGraw-Hill. Copyright 2015.\u003c/em>\u003c/p>\n\u003cp>\u003cspan style=\"font-size: 4.6875em;float: left;line-height: 0.733em;padding: 0.05em 0.1em 0 0;font-family: times, serif, georgia\">W\u003c/span>hen I was a medical student in the 1980s, the beating heart of the Hospital of the University of Pennsylvania was not the mahogany-lined executive suite, nor the dazzling operating room of L. Henry Edmunds, Jr., HUP's most famed cardiac surgeon. No, it was in the decidedly unglamorous, dimly lit Chest Reading room, where all the X-rays were hung on a moving contraption called an alternator that resembled the one on which the clothes hang at your local dry cleaner. Controlled by a seated radiologist operating a foot pedal, the machine would cycle through panel after panel until it arrived at your films. The radiologist took his foot off the pedal, the machine ground to a halt, and the dark X-ray sheets were brought to life by intense backlighting.\u003c/p>\n\u003cp>At Penn in the 1980s, everybody — and I mean everybody, from the lowliest student to the loftiest transplant surgeon — brought films for deciphering to the late Wallace Miller, Sr., a crusty but endearing professor of radiology and one of the best teachers I've ever known. For students like me, time spent with him was at once exhilarating and terrifying. \"What's this opacity?\" he asked me once, the memory burned into my hippocampus by that cognitive curing process known as overwhelming anxiety. \"A ... a pneumonia?\" I stammered.\u003c/p>\n\u003cp>\"Mooiaaa,\" retorted The Oracle, an unforgettable signature sound uttered as Miller smartly turned his head away in mock disgust. I loved it. We all did.\u003c/p>\n\u003caside class=\"pullquote alignright\">'One day I tried to see if I could go the whole day without speaking to anyone. And that’s what happened—I didn’t speak to a single person.'\u003ccite>A radiologist\u003c/cite>\u003c/aside>\n\u003cp>Today, many of my internal medicine trainees barely know where the radiology department is. Just as your record player and LPs are now long gone, in your local hospital today, the films, the analog X-ray machines, and even those charming film conveyor belts have left the building.\u003c/p>\n\u003cp>\u003c/p>\u003c/div>","attributes":{"named":{},"numeric":[]}},{"type":"component","content":"","name":"ad","attributes":{"named":{"label":"fullwidth"},"numeric":["fullwidth"]}},{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>Why? In 2000, only 8 percent of U.S. hospitals had some version of a game-changing computer technology called the Picture Archiving and Communications System, or PACS. By 2008, more than three out of four did.\u003c/p>\n\u003cp>Because radiology was the first medical specialty to computerize, what has happened to it — at once shocking and, in retrospect, entirely predictable — is our canary in the digital coal mine, its experience offering important lessons for patients, clinicians and health care systems.\u003c/p>\n\u003cp>\u003cstrong>The Beauty of PACS\u003c/strong>\u003c/p>\n\u003cp>While the main catalyst for PACS was economic, the quality of the images and the ability to manipulate them were also important. Unlike regular films, CT scans need to be viewed at various contrast levels: One setting is best to look at bones, another to look at lungs, and still another to look at soft tissue like muscle.\u003c/p>\n\u003cp>PACS allowed radiologists to toggle through these views, in the same way that Instagram lets you play with your photos. You can also use a nifty magnifying glass to zoom in on a part of the image. An unexpected benefit was “stacking”: rather than looking at 100 images arrayed in a 10 × 10 grid on a one-dimensional page, the images could be digitally stacked, one on top of another, allowing the radiologist to scroll through them swiftly by rolling a mouse ball. Moreover, computerization let the radiologist look at the images from home, enabling senior experts to weigh in on subtle findings that trainees might flub. And while the images were fuzzy at first, today they’re as crisp as high-definition television.\u003c/p>\n\u003cp>Perhaps most important, PACS obviated the need for maddening searches for prior X-rays. Twenty years ago, when a chest X-ray revealed a lung nodule, the first commandment on the radiologist’s report was to “obtain old films.” The rationale: If the nodule had been unchanged for many years, it could safely be ignored—such stability simply wasn’t consistent with a diagnosis of cancer. But searching for old films was often an exercise in frustration: They were lost, or locked up, or at another institution, or in a filing cabinet in the thoracic surgeon’s garage, behind the golf clubs. When my colleagues and I came up empty-handed, which was more often than not, the patient frequently paid the price in the form of an unnecessary biopsy. But PACS made finding old films a breeze (assuming that they were done at the same hospital or had been scanned into the system); they’re usually just a click away.\u003c/p>\n\u003cp>While PACS was widely anticipated and generally accepted by radiologists, some prescient observers worried that computerization might lead to unbidden effects on the field. In 1999, Stephen Baker, chair of the Department of Radiology at New Jersey Medical School, fretted that PACS might turn radiologists into “disembodied functionaries, more akin to servicing technicians than professional colleagues.” Paul Chang, professor of radiology at the University of Chicago and an early leader in digital radiology, describes the day his father, a retired radiologist, took him to task.\u003c/p>\n\u003cp>“Before PACS, we were the doctor’s doctor,” his father berated him. “Medicine and surgery rounds started in radiology. . . . Every morning the clinicians and the radiologists collaborated.”\u003c/p>\n\u003cp>His father’s less-than-endearing nickname for his famous son: “The Man Who Ruined Radiology.”\u003c/p>\n\u003cp>The advantages of PACS are so vast that few would want to turn back the clock. Yet the effects on those of us who order X-rays and the radiologists who read them have been profound, and they’re not all positive. The fact that we can now review our images without trekking down to radiology means that we rarely do make the trip.\u003c/p>\u003c/p>\u003cp>\u003cspan class='utils-parseShortcode-shortcodes-__youtubeShortcode__embedYoutube'>\n \u003cspan class='utils-parseShortcode-shortcodes-__youtubeShortcode__embedYoutubeInside'>\n \u003ciframe\n loading='lazy'\n class='utils-parseShortcode-shortcodes-__youtubeShortcode__youtubePlayer'\n type='text/html'\n src='//www.youtube.com/embed/fHUzVqoDnts'\n title='//www.youtube.com/embed/fHUzVqoDnts'\n allowfullscreen='true'\n style='border:0;'>\u003c/iframe>\n \u003c/span>\n \u003c/span>\u003c/p>\u003cp>\u003cp>\u003cstrong>An Awkward Trip to Radiology\u003c/strong>\u003c/p>\n\u003cp>A few years ago, when I asked my interns and students to visit the radiology department to review the key films, they looked at me as if I had grown a second head. After my team humored me by accompanying me to the radiology department, I conducted a little sociology experiment. Standing outside my hospital’s chest reading room, I delivered a brief speech:\u003c/p>\n\u003caside class=\"pullquote alignright\">'Did you look at the official report?' he hissed. The unspoken message was clear: Get out of my space; I’m busy.\u003c/aside>\n\u003cp>\"Watch what happens when we enter. Does anybody turn around and welcome us, ask, 'How can I help you?' and seem genuinely enthusiastic? When they go over the X-ray, do they delve a layer deeper than what they said in the formal report? Do they make any teaching points? Does the radiologist suggest courses of action or ask provocative questions?\"\u003c/p>\n\u003cp>I did this because I am deeply concerned that mine is the last generation to have learned the habit of going to the radiology department. Nostalgic for my interactions with Wally Miller and his like, it saddens me that our current trainees will never know how much they can learn from a great radiology teacher, and how much their patients’ care can be improved by actually talking to a real live radiologist. Yet I know that even if I bring my young horses to water, whether they visit the radiology department after I am no longer their wrangler will be determined by the quality of their experience.\u003c/p>\n\u003cp>We entered the chest reading room and were greeted by a wall of radiologists’ backs, their faces trained like lasers on the computer screens in front of them. Not a single head—located atop the shoulders of about eight different radiologists—turned to greet us.\u003c/p>\n\u003cp>After a couple of awkward minutes of crescendo throat-clearing, one of the radiologists grudgingly swiveled around to face my team and me. “Oh, do you need something?” he asked.\u003c/p>\n\u003cp>“Sure; can you help us look at a few films?”\u003c/p>\n\u003cp>He did, kind of, but offered his help in a whisper animated mostly by passive aggressiveness.\u003c/p>\n\u003cp>I thought it couldn’t get any worse, but it did.\u003c/p>\n\u003cp>“What do you think of this area?” I asked him, pointing to a confusing patch of whiteness on one patient’s chest CT scan.\u003c/p>\n\u003cp>“Did you look at the official report?” he hissed. (In other words: \"Perhaps you don’t know how to turn on your computer?\")\u003c/p>\n\u003cp>The unspoken message was clear: Get out of my space; I’m busy.\u003c/p>\n\u003cp>Now, I understand that he might well be busy, and that it has to be annoying having clinicians interrupt you every few minutes to go over images, particularly after you’ve just reviewed them with a different set of specialists and dictated a report. But that is the radiologist’s job. Or at least it used to be.\u003c/p>\n\u003cp>Allison Tillack, a young radiologist and a medical anthropologist whose Ph.D. thesis involved observing the world of radiologists for a year at a prominent academic hospital, has explored how the computerization of radiology has transformed the worlds of radiologists and those who use their services.\u003c/p>\n\u003cp>\"The ability of PACS to alter the accessibility and tempo of medical imaging has resulted in visits to the reading room being viewed now by non-radiology clinicians as a ‘waste of time’ and by radiologists as an ‘interruption,’” she wrote.\u003c/p>\n\u003cp>\u003cstrong>In a Funk\u003c/strong>\u003c/p>\n\u003cp>While I was well aware of the changing perceptions of radiology by nonradiologists, I had not, until I met Tillack, appreciated the degree to which the field of radiology is itself in a PACS-fueled funk.\u003c/p>\n\u003cp>After all, the field remains extremely popular among medical students, as many perceive it as offering the perfect blend of “great lifestyle” (that is, banker’s hours and limited overnight call) and high income, which averaged $340,000 in 2013. In fact, it’s often said that today’s medical students are attracted to the “ROAD specialties”: Radiology, Ophthalmology, Anesthesiology, and Dermatology, all of which are lucrative and none of which involves a lot of contact with those pesky sick people. In her research, Tillack found that the vast majority of radiologists and radiology residents identified the lack of direct patient contact as one of the main attractions of the field.\u003c/p>\n\u003cp>Given all these pluses, many frontline clinicians think of radiologists as having “won the game.” Yet I should have gotten a hint of the field’s handwringing in 2005, when I saw the results of a survey of physicians regarding their satisfaction with their chosen specialty. The happiest doctors were radiation oncologists (the folks who deliver radiation therapy to cancer patients), who do satisfying work, earn a good income, and have predictable hours. The least happy were cardiac surgeons, who train forever and, in recent years, have seen much of their business eroded by stents and other nonsurgical approaches to heart disease.\u003c/p>\n\u003cp>Radiologists show up a bit below the mean on the satisfaction scale—just behind the perennially overwhelmed and undercompensated primary care doctors.\u003c/p>\n\u003cp>In a 2012 paper, Tillack and a colleague described “the loneliness of the long distance radiologist.” One radiologist told them, “Before, I knew the face, name, wife’s name, and kids’ names of all the clinicians, but now I don’t know who you are if you joined the medical staff after we got PACS. . . . Before, when a clinician showed up, I could ask them and find out what’s really going on with the patient.”\u003c/p>\n\u003cp>I hear similar stories from every radiologist I meet. Patrick Luetmer, a Mayo Clinic neuroradiologist, described what happened when his MRI suite was remodeled. The suite was originally configured with two MRI “donuts” (the huge magnets that are responsible for the image) on either side of a central workstation in which Luetmer sat. There, he could monitor the scans as they were being performed, and talk to both the patients and the radiology assistants. Clinicians sometimes wandered down to look at the scans with him.\u003c/p>\n\u003cp>A few years ago, as part of a big efficiency push, Mayo decided that a third MRI machine was a better use of that central area than the radiologist’s air traffic control desk. Luetmer’s workstation was moved to an office a few hundred feet away, where he could follow the scans on his computer monitor and communicate with the techs via a special text messaging system.\u003c/p>\n\u003cp>“One day I tried to see if I could go the whole day without speaking to anyone. And that’s what happened—I didn’t speak to a single person. It was incredibly isolating.”\u003c/p>\n\u003cfigure id=\"attachment_264998\" class=\"wp-caption aligncenter\" style=\"max-width: 640px\">\u003ca href=\"http://ww2.kqed.org/futureofyou/wp-content/uploads/sites/13/2019/10/radiology2.jpg\">\u003cimg class=\"size-large wp-image-264998\" src=\"http://ww2.kqed.org/futureofyou/wp-content/uploads/sites/13/2019/10/radiology2-1180x953.jpg\" alt=\"1950s-era radiology at the U.S. Naval Hospital, Charleston, South Carolina.\" width=\"640\" height=\"517\" srcset=\"https://ww2.kqed.org/app/uploads/sites/13/2019/10/radiology2-1180x953.jpg 1180w, https://ww2.kqed.org/app/uploads/sites/13/2019/10/radiology2-400x323.jpg 400w, https://ww2.kqed.org/app/uploads/sites/13/2019/10/radiology2-743x600.jpg 743w, https://ww2.kqed.org/app/uploads/sites/13/2019/10/radiology2-768x620.jpg 768w, https://ww2.kqed.org/app/uploads/sites/13/2019/10/radiology2-1920x1550.jpg 1920w, https://ww2.kqed.org/app/uploads/sites/13/2019/10/radiology2-960x775.jpg 960w\" sizes=\"(max-width: 640px) 100vw, 640px\">\u003c/a>\u003cfigcaption class=\"wp-caption-text\">1950s-era radiology at the U.S. Naval Hospital, Charleston, South Carolina. \u003ccite>(National Library of Medicine)\u003c/cite>\u003c/figcaption>\u003c/figure>\n\u003cp>The radiologists were lonely, sure, but their situation involves something far deeper. Hari Tsoukas, an expert in organizational studies at the University of Cyprus, highlights the distinction between “information” and “knowledge.” Information, he wrote, “consists of objectified, decontextualized, time-less, impersonal, value-free representations,” whereas knowledge is “context-dependent, personalized, time-bound, and infused with values.”\u003c/p>\n\u003cp>Adds Tillack, “Hunches, hypotheses, frustrations with patients or their families, second guessing, judging of colleagues, and similar activities that mark how uncertainty is negotiated on a daily basis in medical practice are rarely reflected in the medical record . . . this knowledge can only circulate in private or semi-private contexts—by its very definition, this knowledge cannot be made a matter of public record.”\u003c/p>\n\u003cp>By purging the informal conversations during which such information was exchanged, the emergence of PACS left radiologists information-rich (Think of all those pixels! And old films just a click away!), but knowledge-poor.\u003c/p>\n\u003cp>\u003cstrong>'Great Case. Next Case.'\u003c/strong>\u003c/p>\n\u003cp>Radiologists’ alienation runs deeper than the lack of collegial exchange and the inability to find out what’s really going on with the patients. It’s also about power, status, and expertise. The fact that the traditional film lived only in the radiology reading room gave radiologists a monopoly over their entire ecosystem. PACS, observes Tillack, created a new normal in which “the ‘right’ to see [the image] is no longer mediated by radiologists, as it was in the reading room,” and has thus “eroded radiologists’ claims for authoritative knowledge over the interpretation of medical images.”\u003c/p>\n\u003cp>Once the radiology department no longer housed the films, the impact was immediate and dramatic. Without any changes in policy or very much forethought, the mid-1990s transition to filmless operations at the Baltimore VA hospital led to an 82 percent decrease in in-person consultation rates for general radiology studies. Today, many clinicians—particularly specialists like neurologists, pulmonologists, and surgeons—look at images themselves and act on their own interpretations; Many don’t even bother to read the radiologist’s formal report (which usually takes several hours, sometimes even a day, to reach the chart) unless they have unanswered questions or judge the study to be particularly challenging.\u003c/p>\n\u003cp>PACS was to increase efficiency, but that virtue has also become a curse, as radiologists increasingly feel like Lucy and Ethel on the assembly line of the chocolate factory. Among teleradiologists (radiologists reading x-rays from a distant site, often covering emergency departments at night while the hospital’s own radiologists are sleeping), there’s a well-known adage that captures the relentless objectification of their modern predicament: “Great case. Next case.\" As with so many other aspects of our modern digital lives, PACS sped up the clock, and did so without mercy.\u003c/p>\n\u003cp>That clock is constantly ticking. “Instead of waiting for films to be acquired, printed, sorted, and hung, radiologists now are always playing catch-up, looking at more ‘stuff’ in less time,” observed Tillack. That miraculous access to old films also creates an obligation for the radiologist to actually review them.\u003c/p>\n\u003cp>And it’s not just the old films that need to be examined; PACS makes vast amounts of information available with every study. In the early days of CT, the output of a scan might have been about 12 “slices,” each one representing a ¼-inch section through the thorax or abdomen, akin to a thick slice of deli-cut salami. But today’s ultra-fast CT scanners can produce images of more than 50 slices per inch of the human body, more like ultra-thin cuts of prosciutto. And PACS, with its massive memory bank and blazingly fast transmission speeds,can easily display every slice, which means that the radiologist has to scroll through hundreds of images in order to read a single CT study. This combination of more information in each scan, more old studies to compare, and more time pressure is unremitting.\u003c/p>\n\u003cp>The clock is ticking for other reasons as well. Since the image is available to the ordering clinician the moment it is created, radiologists feel obliged to perform their review quickly lest their reading seem like old news, like an afternoon newspaper in the Age of Twitter. Piling on, after recognizing the efficiency of PACS, insurance companies and Medicare slashed the reimbursement for each interpretation, pushing radiologists to read more films in less time in order to maintain their incomes. Said one radiologist, “With PACS, work is busier now. We have 70 percent more cases to read than 10 years ago. . . . At the end of the day . . . I’m fried.”\u003c/p>\n\u003cp>On top of this, there are even greater threats to radiologists’ livelihoods and happiness. One of them flows from the growing pressure on health care systems to slash their costs. Currently, virtually every X-ray performed at a U.S. hospital is sent for a formal reading by a radiologist, who is paid a fee by an insurance company. In today’s cost-cutting environment, it’s probably only a matter of time before some health care systems permit their frontline specialists to officially read certain films, reserving radiologist “overreads” for those images that the clinicians have questions about or the ones with super-high malpractice risk if they are misread. Radiologists can be counted on to fight such a move by frantically waving the banner of quality, but they will need to demonstrate that the value of having them review every film is worth the considerable expense.\u003c/p>\n\u003cp>Moreover, a major theme of Obama-era health reform is a shift from our historical fee-for-service, piecework payment model to one that dispenses a single payment to a hospital and doctors to manage all the care for a group of patients (“accountable care organizations,” ACOs for short) or a given episode of disease (“bundled payments”). Under such systems, the risk for the cost of care shifts from the insurer to the providers, and it’s up to the latter to decide how to divvy up the cash. Ron Arenson, chairman of the department of radiology at the University of California, San Francisco, sees this as the greatest threat to his field.\u003c/p>\n\u003cp>“If the world moves to bundled payments, we won’t do well,” he said. “We’re not very high in the pecking order.”\u003c/p>\n\u003cp>Some nonradiologists, particularly ER doctors working nights and weekends, have little sympathy for their colleagues’ new predicament. In fact, they have begun to wonder why radiologists should be compensated for next-day readings when they’ve already looked at the images themselves, acted on their interpretations, and assumed the risk of being sued if anything goes wrong. In a 2011 editorial entitled “The Life Cycle of a Parasitic Specialist,” ER physician William Mallon took off the gloves.\u003c/p>\n\u003cblockquote>\u003cp>[On Monday morning] these parasites will commence to feed on the financial juices of the lowly unfortunate emergency physicians, who had to work the entire weekend without radiologic support or backup. . . . The radiologist arrives well rested, café latte in hand, and promptly installs himself in a dark room to re-read and bill for all the films the emergency physicians read over the weekend. . . . Never has a specialty done so little for so many and been paid so much.\u003c/p>\u003c/blockquote>\n\u003cp>Ouch.\u003c/p>\n\u003cp>Another challenge to radiology made possible by the death of film has come in the form of teleradiology. Once X-rays went digital, it was no longer crucial for radiologists to be in the same building as the patient or the treating clinicians. As a result, many multihospital systems consolidated their reading rooms, particularly on weekends and nights, with centralized radiologists supporting multiple sites. Predictably, once the technical challenges of connectivity were solved, teleradiology companies emerged to fill this need. As is often the case with contented “legacy” providers (in health care and other industries), traditional radiologists were only too happy to have their colleagues read their films during off-hours. Who wouldn’t be?\u003c/p>\n\u003cp>The playing field soon expanded across national borders, as radiologists in Zurich, Israel, and Singapore began to read nighttime X-rays for American hospitals during their own local daytimes. Hundreds of hospitals now use these \"nighthawks,\" and everybody seems happy about it, including the domestic radiologists, who are sleeping soundly while the overnight images are read half a world away.\u003c/p>\n\u003cp>But one wonders whether this is the start of so-called disruptive innovation, the concept made famous by Harvard’s Clay Christensen. Disruption often begins with a fat and happy incumbent content to preserve its existing enviable position in a market. In industries ranging from commercial aviation to steel manufacturing, an upstart comes in and grabs an unattractive part of the market (in this case, nights and weekends). But once a low-cost company has squeezed through a crack to capture a slice of a previously locked franchise, it is rarely content to stay put. With the average U.S. radiologist earning about $350,000 per year and the average Indian radiologist earning less than one-tenth of that, one wonders whether the same World-is-Flat forces that have revolutionized other industries but mostly bypassed health care will be unleashed.\u003c/p>\n\u003cp>This is where radiologists’ loss of trust and collegiality with other clinicians may exact its heaviest toll. “Some people see teleradiology as a big threat, but I don’t,” UCSF’s Arenson told me. “I think that relationships with radiologists are important.” I do too, which is why I believe he may have his head in the sand: If physicians don’t get much out of visiting the radiology department or have even forgotten where it is located, we have little reason to fight to keep it in our buildings. Or, for that matter, our country.\u003c/p>\n\u003cp>Like all legacy providers faced with a technological or global workforce threat, radiologists can be counted on to argue that quality would take a huge hit if we outsourced their work to less expensive providers, domestic or foreign. The degree to which the field has accepted nighttime readings from non-U.S. radiologists will, of course, undermine this argument. It’s hard to make the claim that a Bangalore-based teleradiologist is sufficiently competent to read an image for your hospital at 3 a.m., but not at 3 p.m.\u003c/p>\n\u003cp>\u003cstrong>The Ultimate Threat\u003c/strong>\u003c/p>\n\u003cp>Finally, there is the ultimate threat: replacement by the machine. Of course, this issue is marbled throughout health care as we enter the digital age. To date, most claims that “this technology will replace doctors” (in areas ranging from diagnostic reasoning to robotic surgery) have proven to be hype.\u003c/p>\n\u003cp>However, in fields that are primarily about visual pattern recognition, the promise (or, if you’re a radiologist, the threat) is much more real. Studies have shown that computers can detect significant numbers of breast cancers and pulmonary emboli missed by radiologists, although nobody has yet taken the bold step of having the computers completely supplant the humans, partly because there are armadas of malpractice attorneys waiting to pounce, and partly because, at least for now, the combination of human and machine seems to perform better than either alone.\u003c/p>\n\u003cp>But over the long haul, I wouldn’t bet on the humans here, particularly since one of the hottest areas in artificial intelligence research is “deep learning”—research that has created computers that are reasonably skilled at “reading,” “hearing,” and, yes, “seeing.” The same kind of software that now allows Facebook to guess that a certain collection of pixels is a picture of you, or that alerts the casino’s security guards to keep an eye on that guy, is likely to eventually crack the code in radiology, and in similar areas such as dermatology and pathology.\u003c/p>\n\u003cp>Slowly, radiologists are waking up to their peril. Rather than isolating themselves from clinical care, some are now relocating their reading stations in clinical areas, such as the ER and the ICU, to be in the line of sight of their clinician colleagues. Others are resurrecting interdisciplinary conferences and training their staff in customer service. Technological solutions that allow radiologists and frontline clinicians to communicate through PACS and the electronic health record are springing up (through programs that create a mash-up of a Skype-like communication tool and a John Madden–style telestrator).\u003c/p>\n\u003cp>Said Paul Chang, the University of Chicago radiologist whose advocacy of PACS so upset his father, “We have to go beyond isolating ourselves and concentrating on messages in a bottle, where we just write a report and are done with it, but instead fostering collaboration.”\u003c/p>\n\u003cp>Since radiology represents such a large segment of health care expenditures (and also a source of harm through the risk of radiation), some health care systems are even putting radiologists in the position of being gatekeepers to their own technology. It’s a role they might not have accepted in the past, but, faced with an existential threat, many now welcome it. Said David Levin, a radiologist at Thomas Jefferson University, “We have to act more like consulting physicians . . . to look at the appropriateness of the requests for advanced imaging studies . . . rather than just going ahead and doing the study.”\u003c/p>\n\u003cp>Radiology’s experience over the past 15 years offers a crystal ball for the rest of the health care system. The speed with which computerization unleashed a series of forces that completely transformed an established field would be all too familiar to travel agents, journalists, and others who have been run over by the digital bulldozer, but it has shocked many health care observers, even astute ones.\u003c/p>\n\u003cp>\u003c/p>\u003c/div>","attributes":{"named":{},"numeric":[]}},{"type":"component","content":"","name":"ad","attributes":{"named":{"label":"floatright"},"numeric":["floatright"]}},{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>Will the computerization of the rest of medicine similarly upend the lives of other kinds of doctors, as well as their patients? The early returns are in, and the answer is yes.\u003c/p>\n\n\u003c/div>\u003c/p>","attributes":{"named":{},"numeric":[]}}],"link":"/futureofyou/256816/how-technology-ruined-the-radiology-profession","authors":["byline_futureofyou_256816"],"categories":["futureofyou_452","futureofyou_1","futureofyou_73"],"tags":["futureofyou_1105","futureofyou_1439","futureofyou_1104","futureofyou_1106"],"featImg":"futureofyou_264973","label":"source_futureofyou_256816"},"futureofyou_274449":{"type":"posts","id":"futureofyou_274449","meta":{"index":"posts_1591205157","site":"futureofyou","id":"274449","score":null,"sort":[1478540165000]},"guestAuthors":[],"slug":"will-computers-ever-be-able-to-make-diagnoses-as-well-as-physicians","title":"Will Computers Ever Be as Good as Physicians at Diagnosing Patients?","publishDate":1478540165,"format":"image","headTitle":"KQED Future of You | KQED Science","labelTerm":{},"content":"\u003cp>\u003cem>This is an edited excerpt from Robert Wachter’s “\u003ca href=\"https://www.amazon.com/Digital-Doctor-Hope-Medicines-Computer/dp/0071849467\" target=\"_blank\">The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine’s Computer Age\u003c/a>,” reprinted with permission from McGraw-Hill. Copyright 2015.\u003c/em>\u003c/p>\n\u003cp>Since 2012, Vinod Khosla, a co-founder of Sun Microsystems, has been predicting that most of what physicians currently do can, will, and should be done by computers. “By 2025,” he has written, “more data-driven, automated health care will displace up to 80 percent of physicians’ diagnostic and prescription work.”\u003c/p>\n\u003caside class=\"pullquote alignright\">A computer can’t read a patient’s tone of voice or anxious look. These clues—like one patient saying, “I have chest pain,” and another, “I HAVE CHEST PAIN!!!”—can make all the difference in diagnosis.\u003c/aside>\n\u003cp>Though Khosla’s comments have irked many a physician, I’m not willing to dismiss him as a kooky provocateur or a utopian techno-evangelist. First of all, his investment track record has made him a Silicon Valley rock star. More important, as recently as a decade ago, some very smart and savvy computer engineers and economists believed that another seemingly intractable problem, building a driverless car, was beyond the reach of modern technology. As of April 2014, the Google car had clocked nearly 700,000 miles and been involved in just two accidents.\u003c/p>\n\u003cp>If the driverless car weren’t enough of a challenge to human superiority, who could have watched IBM’s Watson supercomputer defeat the Jeopardy Hall of Famers in 2011 and not fretted about the future of physicians, or any highly skilled workers, for that matter?\u003c/p>\n\u003cp>\"Just as factory jobs were eliminated in the twentieth century by new assembly-line robots,” wrote all-time (human) Jeopardy champion Ken Jennings soon after the lopsided match ended, “Brad [Rutter, the other defeated champ] and I were the first knowledge-industry workers put out of work by the new generation of ‘thinking’ machines. ‘Quiz show contestant’ may be the first job made redundant by Watson, but I’m sure it won’t be the last.”\u003c/p>\n\u003cp>[ad fullwidth]\u003c/p>\n\u003cp>Soon after the well-publicized trouncing, IBM announced that one of its first “use cases” for Watson would be medicine. Sean Hogan, vice president for IBM Healthcare, told me that “health care jumped out as an area whose complexity and nuances would be\u003ca href=\"https://contextly.com/redirect/?id=K8AsBQdj7f:274449:4068:13:::sidebar:5820be532053f2-30720465\" target=\"_blank\"> receptive to what Watson was representing\u003c/a>.”\u003c/p>\n\u003cp>\u003cstrong>Sticking Up for Team Human\u003c/strong>\u003c/p>\n\u003cp>Andy McAfee, coauthor with Erik Brynjolfsson of the terrific book \"The Second Machine Age,\" agrees with Khosla that computers will ultimately take over much of what physicians do, including diagnosis. “I can’t see how that doesn’t happen,” McAfee, a self-described “technology optimist,” told me when we met for lunch near his MIT office. McAfee and Brynjolfsson argue that the confluence of staggering growth in computing power, zetabytes of fully networked information available on the Web, and the “combinatorial power” of innovation mean that areas that seemed like dead ends, such as artificial intelligence in medicine, are now within reach. They liken the speed with which old digital barriers are falling to Hemingway’s observation about how a person goes broke: “gradually, then suddenly.\"\u003c/p>\n\u003cp>In speaking with both McAfee and Khosla, I felt a strange obligation to stick up for my teams: humans and the subset of humans called doctors. I told McAfee that while I was in awe of the driverless car and IBM’s victories in chess (over world champion Garry Kasparov in 1997) and Jeopardy, he just didn’t understand how hard medicine is. Answering questions posed by Alex Trebek like, “While Maltese borrows many words from Italian, it developed from a dialect of this Semitic language” (the correct response is “What is Arabic?”—Watson answered it, and 65 of the 74 other questions it rang in for, correctly) is tricky, sure, but, at the end of the day, one is simply culling a series of databases to find a fact—a single right answer.\u003c/p>\n\u003caside class=\"pullquote alignright\">‘Quiz show contestant’ may be the first job made redundant by Watson, but I’m sure it won’t be the last.'\u003ccite>(Human) 'Jeopardy' champ Ken Jennings\u003c/cite>\u003c/aside>\n\u003cp>Medical diagnosis isn’t like that. For one thing, uncertainty is endemic, so that the “correct” answer is often a surprisingly probabilistic notion. For another, many diagnoses reveal themselves over time. The patient may present with, say, a headache, but not a worrisome one, and so the primary treatment is reassurance, Tylenol, and time. If the headache worsens over the next two weeks—particularly if it is now accompanied by additional symptoms such as weakness or nausea—that’s an entirely different story.\u003c/p>\n\u003cp>McAfee listened sympathetically—he’s obviously heard scores of versions of the \"You just don’t understand; my work is different\" argument—and then said, “I imagine there are a bunch of really smart geeks at IBM taking notes as guys like you describe this situation. In their heads, they’re asking, ‘How do I model that?’”\u003c/p>\n\u003cp>Undaunted, I tried another tack on Khosla when we met in his office in Menlo Park. “Vinod,” I said, “in medicine we have something we call the ‘eyeball test.’ That means I can see two patients whose numbers look the same”—things like temperature, heart rate, and blood counts—“and my training allows me to say, ‘That guy is sick [I pointed to an imaginary person across the imposing conference table] and the other is okay.’” And good doctors are usually right, I told him, as we possess a kind of sixth sense that we acquire from our training, our role models, and a thousand cases of trial and error.\u003c/p>\n\u003cp>Before Khosla could dismiss this as the usual whining from a dinosaur on the edge of extinction, I tossed him an example from his own world. “I’ll bet you have CEOs of start-ups constantly coming through this office pitching their companies,” I said. “I can imagine two companies that look the same on paper: both CEOs have Stanford MBAs; the proposals have similar financials. Your skill is to be able to point to one and say, ‘Winner’ and to the other, ‘Loser,’ and I’m guessing you’re right more often than not. You’re using information that isn’t measurable. Right?”\u003c/p>\n\u003cp>Nice try. He didn’t budge. “The question is, ‘Is it not measurable or is it not being measured?’” he responded. “And, when does your instinct work and when does it mislead? I think if you did a rigorous study, you’d find that your ‘eyeball test’ is far less effective than you think.”\u003c/p>\n\u003cp>https://www.youtube.com/watch?v=P18EdAKuC1U\u003c/p>\n\u003cp>\u003cstrong>Secrets of the Great Diagnosticians\u003c/strong>\u003c/p>\n\u003cp>There is a rich 50-year history of efforts to build artificial intelligence (AI) systems in health care, and it’s not a particularly uplifting story. Even technophiles admit that the quest to replace doctors with computers—or even the more modest ambition of providing them with useful guidance at the point of care—has been overhyped and unproductive. But times have changed. The growing prevalence of electronic health records offers grist for the AI and big data mills, grist that wasn’t available when the records were on paper. And in this, the Age of Watson, we have new techniques, like natural language processing and machine learning, at our disposal. Perhaps this is our “gradually, then suddenly” moment.\u003c/p>\n\u003caside class=\"pullquote alignright\">Early attempts to use computers for diagnosis were like tackling Saturday’s crossword puzzle in the New York Times before first mastering the one in USA Today.\u003c/aside>\n\u003cp>The public worships dynamic, innovative surgeons like Michael DeBakey; passionate, insightful researchers like Jonas Salk; and telegenic show horses like Mehmet Oz. But we seldom hear about those doctors whom other physicians tend to hold in the highest esteem: the great medical diagnosticians. These sages, like the legendary Johns Hopkins professors William Osler and A. McGehee Harvey, had the uncanny ability to deduce the truth from what others found to be a jumble of symptoms, signs, and lab results. In fact, Sir Arthur Conan Doyle, a physician by training, modeled Sherlock Holmes on one of his old professors, Joseph Bell, a renowned diagnostician at Edinburgh’s medical school.\u003c/p>\n\u003cp>For most doctors, diagnosis forms the essence of their practice (and of their professional souls), which may help explain why we find it so painful to believe that this particular skill could be replaced by silicon wafers.\u003c/p>\n\u003cp>In the 1970s, a Tufts kidney specialist named Jerome Kassirer (who later became editor of the New England Journal of Medicine) decided to try to unlock the cognitive secrets of the great diagnosticians. If he succeeded, the rewards could be great. The insights, problem-solving strategies, and reasoning patterns of these medical geniuses might be teachable to other physicians, perhaps even programmed into computers.\u003c/p>\n\u003cp>Kassirer focused first on the differential diagnosis, the method that doctors have long used to inventory and sort through their patients’ problems. The differential diagnosis is to a physician what the building of hypotheses is to a basic scientist: the core work of the professional mind. Let’s say a female patient complains of right lower abdominal pain and fever. We automatically begin to generate “a differential,” including appendicitis, pelvic inflammatory disease, kidney infection, and a host of less common disorders—some of them quite serious. Our job is to weigh the facts at hand in an effort to ultimately “rule in” one diagnosis on the list and “rule out” the others. Sometimes, the information we gather from the history and physical examination is sufficient.\u003c/p>\n\u003cp>More often, particularly when patients are truly ill, we require additional laboratory or radiographic studies to push one of the diagnoses over the “rule in” line. There is considerable skill, and no small amount of art, involved in this process. For one thing, we need to figure out whether the patient’s symptoms are part of a single disease or are manifestations of two or more distinct illnesses. The principle known as\u003ca href=\"http://www.medicinenet.com/script/main/art.asp?articlekey=26739\" target=\"_blank\"> Occam’s Razor\u003c/a> bids us to try to find a unifying diagnosis for all of a patient’s symptoms. But as soon as medical students memorize this so-called Law of Clinical Parsimony, we whipsaw them with \u003ca href=\"http://www.emergencymedicalparamedic.com/what-is-hickams-dictum/\" target=\"_blank\">Hickam’s Dictum\u003c/a>, which counters, irreverently, that “patients can have as many diseases as they damn well please.”\u003c/p>\n\u003cp>[contextly_sidebar id=\"fXsrKyRyGekfunOt3dsmZPdJAbzwHrbd\"]\u003c/p>\n\u003cp>Setting the “rule in” threshold is yet another challenge, since it’s wholly dependent on the context. For diseases with relatively benign treatments and prognoses—let’s say, stomach discomfort with no alarming features—I might make the diagnosis of “nonulcer dyspepsia” if I’m 75 percent certain that this is what’s going on. Why? Dyspepsia is a not-too-serious illness, the other illnesses that might present with the same symptoms aren’t likely to be acutely life-threatening either, and dyspepsia has a safe, inexpensive, and fairly effective treatment. All of this makes a 75 percent threshold high enough for me to try an acid-blocker and see what happens.\u003c/p>\n\u003cp>Now let’s turn to a patient who presents with acute shortness of breath and pleuritic chest pain. In this patient, I’m considering the diagnosis of pulmonary embolism (a blood clot to the lungs), a more serious disorder whose treatment (blood thinners) is riskier. Now, I’d want to be at least 95 percent sure before attaching that diagnostic label. And I won’t rule in a diagnosis of cancer—with its psychological freight, prognostic implications, and toxic treatments—unless I’m close to 100 percent certain, even if it takes a surgical biopsy to achieve this level of confidence.\u003c/p>\n\u003cp>Kassirer and his colleagues observed the diagnostic reasoning of scores of clinicians. They found that the good ones employed robust strategies to answer these knotty questions, even if they couldn’t always articulate what they were doing and why. The researchers ultimately came to appreciate that the physicians were engaging in a process called “iterative hypothesis testing” to transform the differential diagnosis (or, more accurately, diagnoses, since sick patients often have a variety of abnormalities to be explained) into something actionable. After hearing the initial portion of a case, the doctors began drawing possible scenarios to explain it, modifying their opinions as they went along and more information became available.\u003c/p>\n\u003cp>For example, when a physician confronts a case that begins with, “This 57-year-old man has three days of chest pain, shortness of breath, and lightheadedness,” she responds by thinking, “The worst thing this could be is a heart attack or a pulmonary embolism. I need to ask if the chest pain bores through to the back, which would make me worry about aortic dissection [a rip in the aorta]. I’ll also inquire about typical cardiac symptoms, such as sweating and nausea, and see if the pain is squeezing or radiates to the left arm or jaw. But even if it doesn’t, I’ll certainly get an EKG to rule out a heart attack or pericarditis [inflammation of the sac that surrounds the heart]. If he also reports a fever or a cough, I might begin to suspect pneumonia or pleurisy. The chest X-ray should help sort that out.”\u003c/p>\n\u003cp>Every answer the patient gives, and each positive or negative finding on the physical examination (yes, there is a heart murmur; no, the liver is not enlarged) triggers an automatic, almost intuitive recalibration of the most likely alternatives. When I see a master clinician at work—my favorite is my UCSF colleague Gurpreet Dhaliwal, who was profiled in a 2012\u003ca href=\"http://www.nytimes.com/2012/12/04/health/quest-to-eliminate-diagnostic-lapses.html\" target=\"_blank\"> New York Times article\u003c/a>—I know that these synapses are firing as he asks a patient a series of questions that may seem unrelated to the patient’s presenting complaint but are directed toward “narrowing the differential.” It turns out that there’s an even more impressive piece of cognitive magic going on. The master clinician embraces certain pieces of data (the patient’s trip to rural Thailand last year) while discarding others (an episode of belly pain and bloating three weeks ago). This is the part of diagnostic reasoning that beginners find most vexing, since they lack the foundational knowledge to understand why their teacher focused so intently on one nugget of information and all but ignored others that, to the novice, seemed equally crucial. How do the great diagnosticians make such choices?\u003c/p>\n\u003cp>We now recognize this as a relatively intuitive version of \u003ca href=\"http://www.medicinenet.com/script/main/art.asp?articlekey=10301\" target=\"_blank\">Bayes’ theorem\u003c/a>. Developed by the eighteenth-century British theologian-turned-mathematician Thomas Bayes, this theorem (often ignored by students because it is taught to them with the dryness of a Passover matzo) is the linchpin of clinical reasoning. In essence, Bayes’ theorem says that any medical test must be interpreted from two perspectives. The first: How accurate is the test—that is, how often does it give right or wrong answers? The second: How likely is it that this patient has the disease the test is looking for?\u003c/p>\n\u003cp>These deceptively simple questions explain why, in the early days of the AIDS epidemic (when HIV testing was far less accurate than it is today), it was silly to test heterosexual couples applying for a marriage license, since the vast majority of positive tests in this very low-risk group would be wrong. Similarly, they show why it is foolish to screen healthy 36-year-old executives with a cardiac treadmill test or a heart scan, since positive results will mostly be false positives, serving only to scare the bejesus out of the patients and run up bills for unnecessary follow-up tests. Conversely, in a 68-year-old smoker with diabetes and high cholesterol who develops squeezing chest pain while jogging, there is a 95 percent chance that those pains are from coronary artery disease. In this case, a negative treadmill test only lowers this probability to about 80 percent, so the clinician who reassures the patient that his negative test means that his heart is fine—“take some antacids; it’s OK to keep jogging”—is making a terrible, and potentially fatal, mistake.\u003c/p>\n\u003cp>\u003cstrong>The AI Challenge\u003c/strong>\u003c/p>\n\u003cp>As if this weren’t complicated enough for the poor IBM engineer gearing up to retool Watson from answering questions about “Potent Potables” to diagnosing sick patients, there’s more. While the EHR at least offers a fighting chance for computerized diagnosis (older medical AI programs, built in the pen-and-paper era, required busy physicians to write their notes and then reenter all the key data), parsing an electronic medical record is far from straightforward. Natural language processing is getting much better, but it still has real problems with negation (“the patient has no history of chest pain or cough”) and with family history (“there is a history of arthritis in the patient’s sister, but his mother is well”), to name just a couple of issues. Certain terms have multiple meanings: when written by a psychiatrist, the term depression is likely to refer to a mood disorder, while when it appears in a cardiologist’s note (“there was no evidence of ST-depression”) it probably refers to a dip in the EKG tracing that is often a clue to coronary disease. Ditto abbreviations: Does the patient with “MS” have multiple sclerosis or mitral stenosis, a sticky heart valve? Finally, the computer can’t read a patient’s tone of voice or the anxious look on her face, although engineers are working on this. These clues—like one patient saying, “I have chest pain,” and another, “I HAVE CHEST PAIN!!!”—can make all the difference in the world diagnostically.\u003c/p>\n\u003cp>Perhaps the trickiest problem of all is that—at least today—the very collection of the facts needed to feed an AI system is itself a cognitively complex process. Let’s return to the example of aortic dissection, a rip in the aorta that is often fatal if it is not treated promptly. If the initial history raises the slightest concern about dissection, I’m going to ask questions about whether the pain bores through to the back and check carefully for the quiet murmur of aortic insufficiency as well as for asymmetric blood pressure readings in the two arms, all clues to dissection. If I don’t harbor a suspicion of this scary (and unusual) disease, I’m not going to look for these things—they’re not part of a routine exam.\u003c/p>\n\u003cp>Decades ago, MIT’s Peter Szolovits, an AI expert who worked with Kassirer and his colleagues in the early days, gave up thinking about diagnosis as a simple matter of question answering. This was mostly because he came to appreciate the importance of timing—a nonissue in Jeopardy but a pivotal one in medicine. “A heart attack that happened five years ago has different implications from one that happened five minutes ago,” he explained, and a computer can’t “know” this unless it is programmed to do so. (It turns out that such issues of foundational knowledge are fundamental in AI—computers have no way of “knowing” some of the basic assumptions that allow us to get through our days, things like water is wet, love is good, and death is permanent.)\u003c/p>\n\u003cp>Moreover, much of medical reasoning relies on feedback loops: observing how events unfold and using that information to refine the diagnostic possibilities.We think a patient has bacterial pneumonia, and so we treat the “pneumonia” with antibiotics, but the patient’s fever doesn’t break after three days. So now we consider the possibility of tuberculosis or lupus. This is the cognitive work of the practicing clinician—focused a bit less on “What is the diagnosis?” and more on “How do I best manage this situation?”—and an AI program that doesn’t account for this will be of limited value.\u003c/p>\n\u003cp>\u003cstrong>Early Attempts\u003c/strong>\u003c/p>\n\u003cp>Now that you appreciate the nature of the problem, it’s easy (in retrospect, at least) to see why the choice by early health care computer experts to focus on diagnosis was risky, perhaps even wrongheaded. It’s like tackling Saturday’s crossword puzzle in the New York Times before first mastering the one in USA Today.\u003c/p>\n\u003cp>Larry Fagan, an early Stanford computing pioneer, told me, “We were not naive about the complexity. It’s just that it was the most exciting question.” Diagnosis is not just exciting, it’s at the heart of safe medical care. Diagnostic errors are common, and they can be fatal. A number of autopsy studies conducted over the past 40 years have shown that major diagnoses were overlooked in nearly one in five patients. With the advent of CT scans and MRIs, the number has gone down a bit, but it still hovers around one in ten. Diagnostic errors contribute to 40,000 to 80,000 deaths per year in the United States. And reviews of malpractice cases have demonstrated that diagnostic errors are the most common source of mistakes leading to successful lawsuits.\u003c/p>\n\u003cp>Medical IT experts jumped into the fray in the 1970s, designing a series of computer programs that they believed could help physicians be better diagnosticians, or perhaps even replace them entirely. That decade’s literature was replete with enthusiastic articles about how microprocessors, programmed to think like experts, would soon replace the brains of harried doctors. The attitude was captured by one early computing pioneer in a 1971 paean to his computer: “It is immune from fatigue and carelessness; and it works day and night, weekends and holidays, without coffee breaks, overtime, fringe benefits or human courtesy.”\u003c/p>\n\u003cp>By the mid-1980s, disappointment had set in. The tools that had seemed so promising a decade earlier were, by and large, unable to manage the complexity of clinical medicine, and they garnered few clinician advocates and miniscule commercial adoption. The medical AI movement skidded to a halt, marking the start of a 20-year period that insiders still refer to as the “AI winter.” Ted Shortliffe, one of the field’s longstanding leaders, has said that the early experience with programs like INTERNIST, DXplain, and MYCIN reminded him of this cartoon:\u003c/p>\n\u003cp>\u003ca href=\"http://ww2.kqed.org/futureofyou/wp-content/uploads/sites/13/2016/11/cartoon.jpg\">\u003cimg class=\"aligncenter size-full wp-image-276235\" src=\"http://ww2.kqed.org/futureofyou/wp-content/uploads/sites/13/2016/11/cartoon.jpg\" alt=\"cartoon\" width=\"300\" height=\"373\" srcset=\"https://ww2.kqed.org/app/uploads/sites/13/2016/11/cartoon.jpg 300w, https://ww2.kqed.org/app/uploads/sites/13/2016/11/cartoon-160x199.jpg 160w, https://ww2.kqed.org/app/uploads/sites/13/2016/11/cartoon-240x298.jpg 240w\" sizes=\"(max-width: 300px) 100vw, 300px\">\u003c/a>\u003c/p>\n\u003cp>\u003cstrong>'Version 0'\u003c/strong>\u003c/p>\n\u003cp>Vinod Khosla is prepared for this. He knows that even today’s generation of medical AI programs will produce some crazy output, akin to when Watson famously mistook Toronto for an American city during its Jeopardy triumph. (It was worse in rehearsal, when Watson referred to civil rights leader Malcolm X as “Malcolm Ten.”) Khosla points out that the enormous cellphones of the late 1980s would seem equally ridiculous when placed alongside our iPhone 6.0s. He calls today’s medical AI programs “Version 0,” and cautions that people should “expect these early systems and tools to be the butt of jokes from many a writer and physician.”\u003c/p>\n\u003cp>These cases illustrate a perennial debate in AI, one that pits two camps against each other: the “neats” and the “scruffies.” The neats seek solutions that are elegant and provable; they try to model the way experts think and work, and then code that into AI tools. The scruffies are the pragmatists, the hackers, the crazy ones; they believe that problems should be attacked through whatever means work, and that modeling the behavior of experts or the scientific truth of a situation isn’t all that important. IBM’s breakthrough was to figure out that a combination of neat and scruffy—programming in some of the core rules of the game, but then folding in the fruits of machine learning and natural language processing—could solve truly complicated problems.\u003c/p>\n\u003cp>When he was asked about the difference between human thinking and Watson’s method, Eric Brown, who runs IBM’s Watson Technologies group, gave a careful answer (note the shout-out to the humans, the bit players who made it all possible):\u003c/p>\n\u003cblockquote>\u003cp>A lot of the way that Watson works is motivated by the way that humans analyze problems and go about trying to find solutions, especially when it comes to dealing with complex problems where there are a number of intermediate steps toget you to the final answer. So it certainly is inspired by that process. . . . But a lot of it is different from the ways humans work; it tends to leverage the powers and advantages of a computer system, and its ability to rapidly analyze huge amounts of data and text that humans just can’t keep track of.\u003c/p>\u003c/blockquote>\n\u003cp>However Watson works, we find ourselves today in a world with new tools, new mental models, and a new sense of optimism that computers can do pretty much anything. But have we finally reached the age when computers can master the art of clinical reasoning?\u003c/p>\n\u003cp>I asked Eric Brown, who worked on the \"Jeopardy\" project and is now helping to lead Watson’s efforts in medicine, what the equivalent event might be in health care, the moment when his team could finally congratulate itself on its successes. I wondered if it would be the creation of some kind of holographic physician—like “\u003ca href=\"http://memory-alpha.wikia.com/wiki/Emergency_Medical_Holographic_program\" target=\"_blank\">The Doctor\u003c/a>” on Star Trek Voyager—with Watson serving as the cognitive engine. His answer, though, reflected the deep respect he and his colleagues have for the magnitude of the challenge:\u003c/p>\n\u003cp>[ad floatright]\u003c/p>\n\u003cp>“It will be when we have a technology that physicians suddenly can’t live without.”\u003c/p>\n\n","blocks":[],"excerpt":"When it comes to the art of medical diagnosis, has Team Human finally triumphed over AI? Or is it only a matter of time before computers supplant the physician's brain? ","status":"publish","parent":0,"modified":1517000026,"stats":{"hasAudio":false,"hasVideo":true,"hasChartOrMap":false,"iframeSrcs":[],"hasGoogleForm":false,"hasGallery":false,"hasHearkenModule":false,"hasPolis":false,"paragraphCount":50,"wordCount":4460},"headData":{"title":"Will Computers Ever Be as Good as Physicians at Diagnosing Patients? | KQED","description":"When it comes to the art of medical diagnosis, has Team Human finally triumphed over AI? Or is it only a matter of time before computers supplant the physician's brain? ","ogTitle":"","ogDescription":"","ogImgId":"","twTitle":"","twDescription":"","twImgId":"","schema":{"@context":"http://schema.org","@type":"Article","headline":"Will Computers Ever Be as Good as Physicians at Diagnosing Patients?","datePublished":"2016-11-07T17:36:05.000Z","dateModified":"2018-01-26T20:53:46.000Z","image":"https://cdn.kqed.org/wp-content/uploads/2020/02/KQED-OG-Image@1x.png"}},"disqusIdentifier":"274449 http://ww2.kqed.org/futureofyou/?p=274449","disqusUrl":"https://ww2.kqed.org/futureofyou/2016/11/07/will-computers-ever-be-able-to-make-diagnoses-as-well-as-physicians/","disqusTitle":"Will Computers Ever Be as Good as Physicians at Diagnosing Patients?","source":"Future of You","customPermalink":"2016/11/07/AI-computers-diagnosis-watson/","nprByline":"Bob Wachter","path":"/futureofyou/274449/will-computers-ever-be-able-to-make-diagnoses-as-well-as-physicians","audioTrackLength":null,"parsedContent":[{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003cp>\u003cem>This is an edited excerpt from Robert Wachter’s “\u003ca href=\"https://www.amazon.com/Digital-Doctor-Hope-Medicines-Computer/dp/0071849467\" target=\"_blank\">The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine’s Computer Age\u003c/a>,” reprinted with permission from McGraw-Hill. Copyright 2015.\u003c/em>\u003c/p>\n\u003cp>Since 2012, Vinod Khosla, a co-founder of Sun Microsystems, has been predicting that most of what physicians currently do can, will, and should be done by computers. “By 2025,” he has written, “more data-driven, automated health care will displace up to 80 percent of physicians’ diagnostic and prescription work.”\u003c/p>\n\u003caside class=\"pullquote alignright\">A computer can’t read a patient’s tone of voice or anxious look. These clues—like one patient saying, “I have chest pain,” and another, “I HAVE CHEST PAIN!!!”—can make all the difference in diagnosis.\u003c/aside>\n\u003cp>Though Khosla’s comments have irked many a physician, I’m not willing to dismiss him as a kooky provocateur or a utopian techno-evangelist. First of all, his investment track record has made him a Silicon Valley rock star. More important, as recently as a decade ago, some very smart and savvy computer engineers and economists believed that another seemingly intractable problem, building a driverless car, was beyond the reach of modern technology. As of April 2014, the Google car had clocked nearly 700,000 miles and been involved in just two accidents.\u003c/p>\n\u003cp>If the driverless car weren’t enough of a challenge to human superiority, who could have watched IBM’s Watson supercomputer defeat the Jeopardy Hall of Famers in 2011 and not fretted about the future of physicians, or any highly skilled workers, for that matter?\u003c/p>\n\u003cp>\"Just as factory jobs were eliminated in the twentieth century by new assembly-line robots,” wrote all-time (human) Jeopardy champion Ken Jennings soon after the lopsided match ended, “Brad [Rutter, the other defeated champ] and I were the first knowledge-industry workers put out of work by the new generation of ‘thinking’ machines. ‘Quiz show contestant’ may be the first job made redundant by Watson, but I’m sure it won’t be the last.”\u003c/p>\n\u003cp>\u003c/p>\u003c/div>","attributes":{"named":{},"numeric":[]}},{"type":"component","content":"","name":"ad","attributes":{"named":{"label":"fullwidth"},"numeric":["fullwidth"]}},{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>Soon after the well-publicized trouncing, IBM announced that one of its first “use cases” for Watson would be medicine. Sean Hogan, vice president for IBM Healthcare, told me that “health care jumped out as an area whose complexity and nuances would be\u003ca href=\"https://contextly.com/redirect/?id=K8AsBQdj7f:274449:4068:13:::sidebar:5820be532053f2-30720465\" target=\"_blank\"> receptive to what Watson was representing\u003c/a>.”\u003c/p>\n\u003cp>\u003cstrong>Sticking Up for Team Human\u003c/strong>\u003c/p>\n\u003cp>Andy McAfee, coauthor with Erik Brynjolfsson of the terrific book \"The Second Machine Age,\" agrees with Khosla that computers will ultimately take over much of what physicians do, including diagnosis. “I can’t see how that doesn’t happen,” McAfee, a self-described “technology optimist,” told me when we met for lunch near his MIT office. McAfee and Brynjolfsson argue that the confluence of staggering growth in computing power, zetabytes of fully networked information available on the Web, and the “combinatorial power” of innovation mean that areas that seemed like dead ends, such as artificial intelligence in medicine, are now within reach. They liken the speed with which old digital barriers are falling to Hemingway’s observation about how a person goes broke: “gradually, then suddenly.\"\u003c/p>\n\u003cp>In speaking with both McAfee and Khosla, I felt a strange obligation to stick up for my teams: humans and the subset of humans called doctors. I told McAfee that while I was in awe of the driverless car and IBM’s victories in chess (over world champion Garry Kasparov in 1997) and Jeopardy, he just didn’t understand how hard medicine is. Answering questions posed by Alex Trebek like, “While Maltese borrows many words from Italian, it developed from a dialect of this Semitic language” (the correct response is “What is Arabic?”—Watson answered it, and 65 of the 74 other questions it rang in for, correctly) is tricky, sure, but, at the end of the day, one is simply culling a series of databases to find a fact—a single right answer.\u003c/p>\n\u003caside class=\"pullquote alignright\">‘Quiz show contestant’ may be the first job made redundant by Watson, but I’m sure it won’t be the last.'\u003ccite>(Human) 'Jeopardy' champ Ken Jennings\u003c/cite>\u003c/aside>\n\u003cp>Medical diagnosis isn’t like that. For one thing, uncertainty is endemic, so that the “correct” answer is often a surprisingly probabilistic notion. For another, many diagnoses reveal themselves over time. The patient may present with, say, a headache, but not a worrisome one, and so the primary treatment is reassurance, Tylenol, and time. If the headache worsens over the next two weeks—particularly if it is now accompanied by additional symptoms such as weakness or nausea—that’s an entirely different story.\u003c/p>\n\u003cp>McAfee listened sympathetically—he’s obviously heard scores of versions of the \"You just don’t understand; my work is different\" argument—and then said, “I imagine there are a bunch of really smart geeks at IBM taking notes as guys like you describe this situation. In their heads, they’re asking, ‘How do I model that?’”\u003c/p>\n\u003cp>Undaunted, I tried another tack on Khosla when we met in his office in Menlo Park. “Vinod,” I said, “in medicine we have something we call the ‘eyeball test.’ That means I can see two patients whose numbers look the same”—things like temperature, heart rate, and blood counts—“and my training allows me to say, ‘That guy is sick [I pointed to an imaginary person across the imposing conference table] and the other is okay.’” And good doctors are usually right, I told him, as we possess a kind of sixth sense that we acquire from our training, our role models, and a thousand cases of trial and error.\u003c/p>\n\u003cp>Before Khosla could dismiss this as the usual whining from a dinosaur on the edge of extinction, I tossed him an example from his own world. “I’ll bet you have CEOs of start-ups constantly coming through this office pitching their companies,” I said. “I can imagine two companies that look the same on paper: both CEOs have Stanford MBAs; the proposals have similar financials. Your skill is to be able to point to one and say, ‘Winner’ and to the other, ‘Loser,’ and I’m guessing you’re right more often than not. You’re using information that isn’t measurable. Right?”\u003c/p>\n\u003cp>Nice try. He didn’t budge. “The question is, ‘Is it not measurable or is it not being measured?’” he responded. “And, when does your instinct work and when does it mislead? I think if you did a rigorous study, you’d find that your ‘eyeball test’ is far less effective than you think.”\u003c/p>\u003c/p>\u003cp>\u003cspan class='utils-parseShortcode-shortcodes-__youtubeShortcode__embedYoutube'>\n \u003cspan class='utils-parseShortcode-shortcodes-__youtubeShortcode__embedYoutubeInside'>\n \u003ciframe\n loading='lazy'\n class='utils-parseShortcode-shortcodes-__youtubeShortcode__youtubePlayer'\n type='text/html'\n src='//www.youtube.com/embed/P18EdAKuC1U'\n title='//www.youtube.com/embed/P18EdAKuC1U'\n allowfullscreen='true'\n style='border:0;'>\u003c/iframe>\n \u003c/span>\n \u003c/span>\u003c/p>\u003cp>\u003cp>\u003cstrong>Secrets of the Great Diagnosticians\u003c/strong>\u003c/p>\n\u003cp>There is a rich 50-year history of efforts to build artificial intelligence (AI) systems in health care, and it’s not a particularly uplifting story. Even technophiles admit that the quest to replace doctors with computers—or even the more modest ambition of providing them with useful guidance at the point of care—has been overhyped and unproductive. But times have changed. The growing prevalence of electronic health records offers grist for the AI and big data mills, grist that wasn’t available when the records were on paper. And in this, the Age of Watson, we have new techniques, like natural language processing and machine learning, at our disposal. Perhaps this is our “gradually, then suddenly” moment.\u003c/p>\n\u003caside class=\"pullquote alignright\">Early attempts to use computers for diagnosis were like tackling Saturday’s crossword puzzle in the New York Times before first mastering the one in USA Today.\u003c/aside>\n\u003cp>The public worships dynamic, innovative surgeons like Michael DeBakey; passionate, insightful researchers like Jonas Salk; and telegenic show horses like Mehmet Oz. But we seldom hear about those doctors whom other physicians tend to hold in the highest esteem: the great medical diagnosticians. These sages, like the legendary Johns Hopkins professors William Osler and A. McGehee Harvey, had the uncanny ability to deduce the truth from what others found to be a jumble of symptoms, signs, and lab results. In fact, Sir Arthur Conan Doyle, a physician by training, modeled Sherlock Holmes on one of his old professors, Joseph Bell, a renowned diagnostician at Edinburgh’s medical school.\u003c/p>\n\u003cp>For most doctors, diagnosis forms the essence of their practice (and of their professional souls), which may help explain why we find it so painful to believe that this particular skill could be replaced by silicon wafers.\u003c/p>\n\u003cp>In the 1970s, a Tufts kidney specialist named Jerome Kassirer (who later became editor of the New England Journal of Medicine) decided to try to unlock the cognitive secrets of the great diagnosticians. If he succeeded, the rewards could be great. The insights, problem-solving strategies, and reasoning patterns of these medical geniuses might be teachable to other physicians, perhaps even programmed into computers.\u003c/p>\n\u003cp>Kassirer focused first on the differential diagnosis, the method that doctors have long used to inventory and sort through their patients’ problems. The differential diagnosis is to a physician what the building of hypotheses is to a basic scientist: the core work of the professional mind. Let’s say a female patient complains of right lower abdominal pain and fever. We automatically begin to generate “a differential,” including appendicitis, pelvic inflammatory disease, kidney infection, and a host of less common disorders—some of them quite serious. Our job is to weigh the facts at hand in an effort to ultimately “rule in” one diagnosis on the list and “rule out” the others. Sometimes, the information we gather from the history and physical examination is sufficient.\u003c/p>\n\u003cp>More often, particularly when patients are truly ill, we require additional laboratory or radiographic studies to push one of the diagnoses over the “rule in” line. There is considerable skill, and no small amount of art, involved in this process. For one thing, we need to figure out whether the patient’s symptoms are part of a single disease or are manifestations of two or more distinct illnesses. The principle known as\u003ca href=\"http://www.medicinenet.com/script/main/art.asp?articlekey=26739\" target=\"_blank\"> Occam’s Razor\u003c/a> bids us to try to find a unifying diagnosis for all of a patient’s symptoms. But as soon as medical students memorize this so-called Law of Clinical Parsimony, we whipsaw them with \u003ca href=\"http://www.emergencymedicalparamedic.com/what-is-hickams-dictum/\" target=\"_blank\">Hickam’s Dictum\u003c/a>, which counters, irreverently, that “patients can have as many diseases as they damn well please.”\u003c/p>\n\u003cp>\u003c/p>\u003cp>\u003c/p>\u003cp>\u003c/p>\n\u003cp>Setting the “rule in” threshold is yet another challenge, since it’s wholly dependent on the context. For diseases with relatively benign treatments and prognoses—let’s say, stomach discomfort with no alarming features—I might make the diagnosis of “nonulcer dyspepsia” if I’m 75 percent certain that this is what’s going on. Why? Dyspepsia is a not-too-serious illness, the other illnesses that might present with the same symptoms aren’t likely to be acutely life-threatening either, and dyspepsia has a safe, inexpensive, and fairly effective treatment. All of this makes a 75 percent threshold high enough for me to try an acid-blocker and see what happens.\u003c/p>\n\u003cp>Now let’s turn to a patient who presents with acute shortness of breath and pleuritic chest pain. In this patient, I’m considering the diagnosis of pulmonary embolism (a blood clot to the lungs), a more serious disorder whose treatment (blood thinners) is riskier. Now, I’d want to be at least 95 percent sure before attaching that diagnostic label. And I won’t rule in a diagnosis of cancer—with its psychological freight, prognostic implications, and toxic treatments—unless I’m close to 100 percent certain, even if it takes a surgical biopsy to achieve this level of confidence.\u003c/p>\n\u003cp>Kassirer and his colleagues observed the diagnostic reasoning of scores of clinicians. They found that the good ones employed robust strategies to answer these knotty questions, even if they couldn’t always articulate what they were doing and why. The researchers ultimately came to appreciate that the physicians were engaging in a process called “iterative hypothesis testing” to transform the differential diagnosis (or, more accurately, diagnoses, since sick patients often have a variety of abnormalities to be explained) into something actionable. After hearing the initial portion of a case, the doctors began drawing possible scenarios to explain it, modifying their opinions as they went along and more information became available.\u003c/p>\n\u003cp>For example, when a physician confronts a case that begins with, “This 57-year-old man has three days of chest pain, shortness of breath, and lightheadedness,” she responds by thinking, “The worst thing this could be is a heart attack or a pulmonary embolism. I need to ask if the chest pain bores through to the back, which would make me worry about aortic dissection [a rip in the aorta]. I’ll also inquire about typical cardiac symptoms, such as sweating and nausea, and see if the pain is squeezing or radiates to the left arm or jaw. But even if it doesn’t, I’ll certainly get an EKG to rule out a heart attack or pericarditis [inflammation of the sac that surrounds the heart]. If he also reports a fever or a cough, I might begin to suspect pneumonia or pleurisy. The chest X-ray should help sort that out.”\u003c/p>\n\u003cp>Every answer the patient gives, and each positive or negative finding on the physical examination (yes, there is a heart murmur; no, the liver is not enlarged) triggers an automatic, almost intuitive recalibration of the most likely alternatives. When I see a master clinician at work—my favorite is my UCSF colleague Gurpreet Dhaliwal, who was profiled in a 2012\u003ca href=\"http://www.nytimes.com/2012/12/04/health/quest-to-eliminate-diagnostic-lapses.html\" target=\"_blank\"> New York Times article\u003c/a>—I know that these synapses are firing as he asks a patient a series of questions that may seem unrelated to the patient’s presenting complaint but are directed toward “narrowing the differential.” It turns out that there’s an even more impressive piece of cognitive magic going on. The master clinician embraces certain pieces of data (the patient’s trip to rural Thailand last year) while discarding others (an episode of belly pain and bloating three weeks ago). This is the part of diagnostic reasoning that beginners find most vexing, since they lack the foundational knowledge to understand why their teacher focused so intently on one nugget of information and all but ignored others that, to the novice, seemed equally crucial. How do the great diagnosticians make such choices?\u003c/p>\n\u003cp>We now recognize this as a relatively intuitive version of \u003ca href=\"http://www.medicinenet.com/script/main/art.asp?articlekey=10301\" target=\"_blank\">Bayes’ theorem\u003c/a>. Developed by the eighteenth-century British theologian-turned-mathematician Thomas Bayes, this theorem (often ignored by students because it is taught to them with the dryness of a Passover matzo) is the linchpin of clinical reasoning. In essence, Bayes’ theorem says that any medical test must be interpreted from two perspectives. The first: How accurate is the test—that is, how often does it give right or wrong answers? The second: How likely is it that this patient has the disease the test is looking for?\u003c/p>\n\u003cp>These deceptively simple questions explain why, in the early days of the AIDS epidemic (when HIV testing was far less accurate than it is today), it was silly to test heterosexual couples applying for a marriage license, since the vast majority of positive tests in this very low-risk group would be wrong. Similarly, they show why it is foolish to screen healthy 36-year-old executives with a cardiac treadmill test or a heart scan, since positive results will mostly be false positives, serving only to scare the bejesus out of the patients and run up bills for unnecessary follow-up tests. Conversely, in a 68-year-old smoker with diabetes and high cholesterol who develops squeezing chest pain while jogging, there is a 95 percent chance that those pains are from coronary artery disease. In this case, a negative treadmill test only lowers this probability to about 80 percent, so the clinician who reassures the patient that his negative test means that his heart is fine—“take some antacids; it’s OK to keep jogging”—is making a terrible, and potentially fatal, mistake.\u003c/p>\n\u003cp>\u003cstrong>The AI Challenge\u003c/strong>\u003c/p>\n\u003cp>As if this weren’t complicated enough for the poor IBM engineer gearing up to retool Watson from answering questions about “Potent Potables” to diagnosing sick patients, there’s more. While the EHR at least offers a fighting chance for computerized diagnosis (older medical AI programs, built in the pen-and-paper era, required busy physicians to write their notes and then reenter all the key data), parsing an electronic medical record is far from straightforward. Natural language processing is getting much better, but it still has real problems with negation (“the patient has no history of chest pain or cough”) and with family history (“there is a history of arthritis in the patient’s sister, but his mother is well”), to name just a couple of issues. Certain terms have multiple meanings: when written by a psychiatrist, the term depression is likely to refer to a mood disorder, while when it appears in a cardiologist’s note (“there was no evidence of ST-depression”) it probably refers to a dip in the EKG tracing that is often a clue to coronary disease. Ditto abbreviations: Does the patient with “MS” have multiple sclerosis or mitral stenosis, a sticky heart valve? Finally, the computer can’t read a patient’s tone of voice or the anxious look on her face, although engineers are working on this. These clues—like one patient saying, “I have chest pain,” and another, “I HAVE CHEST PAIN!!!”—can make all the difference in the world diagnostically.\u003c/p>\n\u003cp>Perhaps the trickiest problem of all is that—at least today—the very collection of the facts needed to feed an AI system is itself a cognitively complex process. Let’s return to the example of aortic dissection, a rip in the aorta that is often fatal if it is not treated promptly. If the initial history raises the slightest concern about dissection, I’m going to ask questions about whether the pain bores through to the back and check carefully for the quiet murmur of aortic insufficiency as well as for asymmetric blood pressure readings in the two arms, all clues to dissection. If I don’t harbor a suspicion of this scary (and unusual) disease, I’m not going to look for these things—they’re not part of a routine exam.\u003c/p>\n\u003cp>Decades ago, MIT’s Peter Szolovits, an AI expert who worked with Kassirer and his colleagues in the early days, gave up thinking about diagnosis as a simple matter of question answering. This was mostly because he came to appreciate the importance of timing—a nonissue in Jeopardy but a pivotal one in medicine. “A heart attack that happened five years ago has different implications from one that happened five minutes ago,” he explained, and a computer can’t “know” this unless it is programmed to do so. (It turns out that such issues of foundational knowledge are fundamental in AI—computers have no way of “knowing” some of the basic assumptions that allow us to get through our days, things like water is wet, love is good, and death is permanent.)\u003c/p>\n\u003cp>Moreover, much of medical reasoning relies on feedback loops: observing how events unfold and using that information to refine the diagnostic possibilities.We think a patient has bacterial pneumonia, and so we treat the “pneumonia” with antibiotics, but the patient’s fever doesn’t break after three days. So now we consider the possibility of tuberculosis or lupus. This is the cognitive work of the practicing clinician—focused a bit less on “What is the diagnosis?” and more on “How do I best manage this situation?”—and an AI program that doesn’t account for this will be of limited value.\u003c/p>\n\u003cp>\u003cstrong>Early Attempts\u003c/strong>\u003c/p>\n\u003cp>Now that you appreciate the nature of the problem, it’s easy (in retrospect, at least) to see why the choice by early health care computer experts to focus on diagnosis was risky, perhaps even wrongheaded. It’s like tackling Saturday’s crossword puzzle in the New York Times before first mastering the one in USA Today.\u003c/p>\n\u003cp>Larry Fagan, an early Stanford computing pioneer, told me, “We were not naive about the complexity. It’s just that it was the most exciting question.” Diagnosis is not just exciting, it’s at the heart of safe medical care. Diagnostic errors are common, and they can be fatal. A number of autopsy studies conducted over the past 40 years have shown that major diagnoses were overlooked in nearly one in five patients. With the advent of CT scans and MRIs, the number has gone down a bit, but it still hovers around one in ten. Diagnostic errors contribute to 40,000 to 80,000 deaths per year in the United States. And reviews of malpractice cases have demonstrated that diagnostic errors are the most common source of mistakes leading to successful lawsuits.\u003c/p>\n\u003cp>Medical IT experts jumped into the fray in the 1970s, designing a series of computer programs that they believed could help physicians be better diagnosticians, or perhaps even replace them entirely. That decade’s literature was replete with enthusiastic articles about how microprocessors, programmed to think like experts, would soon replace the brains of harried doctors. The attitude was captured by one early computing pioneer in a 1971 paean to his computer: “It is immune from fatigue and carelessness; and it works day and night, weekends and holidays, without coffee breaks, overtime, fringe benefits or human courtesy.”\u003c/p>\n\u003cp>By the mid-1980s, disappointment had set in. The tools that had seemed so promising a decade earlier were, by and large, unable to manage the complexity of clinical medicine, and they garnered few clinician advocates and miniscule commercial adoption. The medical AI movement skidded to a halt, marking the start of a 20-year period that insiders still refer to as the “AI winter.” Ted Shortliffe, one of the field’s longstanding leaders, has said that the early experience with programs like INTERNIST, DXplain, and MYCIN reminded him of this cartoon:\u003c/p>\n\u003cp>\u003ca href=\"http://ww2.kqed.org/futureofyou/wp-content/uploads/sites/13/2016/11/cartoon.jpg\">\u003cimg class=\"aligncenter size-full wp-image-276235\" src=\"http://ww2.kqed.org/futureofyou/wp-content/uploads/sites/13/2016/11/cartoon.jpg\" alt=\"cartoon\" width=\"300\" height=\"373\" srcset=\"https://ww2.kqed.org/app/uploads/sites/13/2016/11/cartoon.jpg 300w, https://ww2.kqed.org/app/uploads/sites/13/2016/11/cartoon-160x199.jpg 160w, https://ww2.kqed.org/app/uploads/sites/13/2016/11/cartoon-240x298.jpg 240w\" sizes=\"(max-width: 300px) 100vw, 300px\">\u003c/a>\u003c/p>\n\u003cp>\u003cstrong>'Version 0'\u003c/strong>\u003c/p>\n\u003cp>Vinod Khosla is prepared for this. He knows that even today’s generation of medical AI programs will produce some crazy output, akin to when Watson famously mistook Toronto for an American city during its Jeopardy triumph. (It was worse in rehearsal, when Watson referred to civil rights leader Malcolm X as “Malcolm Ten.”) Khosla points out that the enormous cellphones of the late 1980s would seem equally ridiculous when placed alongside our iPhone 6.0s. He calls today’s medical AI programs “Version 0,” and cautions that people should “expect these early systems and tools to be the butt of jokes from many a writer and physician.”\u003c/p>\n\u003cp>These cases illustrate a perennial debate in AI, one that pits two camps against each other: the “neats” and the “scruffies.” The neats seek solutions that are elegant and provable; they try to model the way experts think and work, and then code that into AI tools. The scruffies are the pragmatists, the hackers, the crazy ones; they believe that problems should be attacked through whatever means work, and that modeling the behavior of experts or the scientific truth of a situation isn’t all that important. IBM’s breakthrough was to figure out that a combination of neat and scruffy—programming in some of the core rules of the game, but then folding in the fruits of machine learning and natural language processing—could solve truly complicated problems.\u003c/p>\n\u003cp>When he was asked about the difference between human thinking and Watson’s method, Eric Brown, who runs IBM’s Watson Technologies group, gave a careful answer (note the shout-out to the humans, the bit players who made it all possible):\u003c/p>\n\u003cblockquote>\u003cp>A lot of the way that Watson works is motivated by the way that humans analyze problems and go about trying to find solutions, especially when it comes to dealing with complex problems where there are a number of intermediate steps toget you to the final answer. So it certainly is inspired by that process. . . . But a lot of it is different from the ways humans work; it tends to leverage the powers and advantages of a computer system, and its ability to rapidly analyze huge amounts of data and text that humans just can’t keep track of.\u003c/p>\u003c/blockquote>\n\u003cp>However Watson works, we find ourselves today in a world with new tools, new mental models, and a new sense of optimism that computers can do pretty much anything. But have we finally reached the age when computers can master the art of clinical reasoning?\u003c/p>\n\u003cp>I asked Eric Brown, who worked on the \"Jeopardy\" project and is now helping to lead Watson’s efforts in medicine, what the equivalent event might be in health care, the moment when his team could finally congratulate itself on its successes. I wondered if it would be the creation of some kind of holographic physician—like “\u003ca href=\"http://memory-alpha.wikia.com/wiki/Emergency_Medical_Holographic_program\" target=\"_blank\">The Doctor\u003c/a>” on Star Trek Voyager—with Watson serving as the cognitive engine. His answer, though, reflected the deep respect he and his colleagues have for the magnitude of the challenge:\u003c/p>\n\u003cp>\u003c/p>\u003c/div>","attributes":{"named":{},"numeric":[]}},{"type":"component","content":"","name":"ad","attributes":{"named":{"label":"floatright"},"numeric":["floatright"]}},{"type":"contentString","content":"\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>“It will be when we have a technology that physicians suddenly can’t live without.”\u003c/p>\n\n\u003c/div>\u003c/p>","attributes":{"named":{},"numeric":[]}}],"link":"/futureofyou/274449/will-computers-ever-be-able-to-make-diagnoses-as-well-as-physicians","authors":["byline_futureofyou_274449"],"categories":["futureofyou_452","futureofyou_1","futureofyou_73"],"tags":["futureofyou_849","futureofyou_1105","futureofyou_1439","futureofyou_915","futureofyou_190","futureofyou_1014","futureofyou_80","futureofyou_1106"],"featImg":"futureofyou_274615","label":"source_futureofyou_274449"}},"programsReducer":{"possible":{"id":"possible","title":"Possible","info":"Possible is hosted by entrepreneur Reid Hoffman and writer Aria Finger. 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But is this once sleepy suburb ready for them?","imageSrc":"https://cdn.kqed.org/wp-content/uploads/2024/04/American-Suburb-Podcast-Tile-703x703-1.jpg","officialWebsiteLink":"/news/series/american-suburb-podcast","meta":{"site":"news","source":"kqed","order":"13"},"link":"/news/series/american-suburb-podcast/","subscribe":{"npr":"https://rpb3r.app.goo.gl/RBrW","apple":"https://itunes.apple.com/WebObjects/MZStore.woa/wa/viewPodcast?mt=2&id=1287748328","tuneIn":"https://tunein.com/radio/American-Suburb-p1086805/","rss":"https://ww2.kqed.org/news/series/american-suburb-podcast/feed/podcast","google":"https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5tZWdhcGhvbmUuZm0vS1FJTkMzMDExODgxNjA5"}},"baycurious":{"id":"baycurious","title":"Bay Curious","tagline":"Exploring the Bay Area, one question at a time","info":"KQED’s new podcast, Bay Curious, gets to the bottom of the mysteries — both profound and peculiar — that give the Bay Area its unique identity. And we’ll do it with your help! You ask the questions. You decide what Bay Curious investigates. And you join us on the journey to find the answers.","imageSrc":"https://cdn.kqed.org/wp-content/uploads/2024/04/Bay-Curious-Podcast-Tile-703x703-1.jpg","imageAlt":"\"KQED Bay Curious","officialWebsiteLink":"/news/series/baycurious","meta":{"site":"news","source":"kqed","order":"4"},"link":"/podcasts/baycurious","subscribe":{"apple":"https://podcasts.apple.com/us/podcast/bay-curious/id1172473406","npr":"https://www.npr.org/podcasts/500557090/bay-curious","rss":"https://ww2.kqed.org/news/category/bay-curious-podcast/feed/podcast","google":"https://podcasts.google.com/feed/aHR0cHM6Ly93dzIua3FlZC5vcmcvbmV3cy9jYXRlZ29yeS9iYXktY3VyaW91cy1wb2RjYXN0L2ZlZWQvcG9kY2FzdA","stitcher":"https://www.stitcher.com/podcast/kqed/bay-curious","spotify":"https://open.spotify.com/show/6O76IdmhixfijmhTZLIJ8k"}},"bbc-world-service":{"id":"bbc-world-service","title":"BBC World Service","info":"The day's top stories from BBC News compiled twice daily in the week, once at weekends.","airtime":"MON-FRI 9pm-10pm, TUE-FRI 1am-2am","imageSrc":"https://cdn.kqed.org/wp-content/uploads/2024/04/BBC-World-Service-Podcast-Tile-360x360-1.jpg","officialWebsiteLink":"https://www.bbc.co.uk/sounds/play/live:bbc_world_service","meta":{"site":"news","source":"BBC World Service"},"link":"/radio/program/bbc-world-service","subscribe":{"apple":"https://itunes.apple.com/us/podcast/global-news-podcast/id135067274?mt=2","tuneIn":"https://tunein.com/radio/BBC-World-Service-p455581/","rss":"https://podcasts.files.bbci.co.uk/p02nq0gn.rss"}},"code-switch-life-kit":{"id":"code-switch-life-kit","title":"Code Switch / Life Kit","info":"\u003cem>Code Switch\u003c/em>, which listeners will hear in the first part of the hour, has fearless and much-needed conversations about race. Hosted by journalists of color, the show tackles the subject of race head-on, exploring how it impacts every part of society — from politics and pop culture to history, sports and more.\u003cbr />\u003cbr />\u003cem>Life Kit\u003c/em>, which will be in the second part of the hour, guides you through spaces and feelings no one prepares you for — from finances to mental health, from workplace microaggressions to imposter syndrome, from relationships to parenting. The show features experts with real world experience and shares their knowledge. Because everyone needs a little help being human.\u003cbr />\u003cbr />\u003ca href=\"https://www.npr.org/podcasts/510312/codeswitch\">\u003cem>Code Switch\u003c/em> offical site and podcast\u003c/a>\u003cbr />\u003ca href=\"https://www.npr.org/lifekit\">\u003cem>Life Kit\u003c/em> offical site and podcast\u003c/a>\u003cbr />","airtime":"SUN 9pm-10pm","imageSrc":"https://cdn.kqed.org/wp-content/uploads/2024/04/Code-Switch-Life-Kit-Podcast-Tile-360x360-1.jpg","meta":{"site":"radio","source":"npr"},"link":"/radio/program/code-switch-life-kit","subscribe":{"apple":"https://podcasts.apple.com/podcast/1112190608?mt=2&at=11l79Y&ct=nprdirectory","google":"https://podcasts.google.com/feed/aHR0cHM6Ly93d3cubnByLm9yZy9yc3MvcG9kY2FzdC5waHA_aWQ9NTEwMzEy","spotify":"https://open.spotify.com/show/3bExJ9JQpkwNhoHvaIIuyV","rss":"https://feeds.npr.org/510312/podcast.xml"}},"commonwealth-club":{"id":"commonwealth-club","title":"Commonwealth Club of California Podcast","info":"The Commonwealth Club of California is the nation's oldest and largest public affairs forum. 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