{"id":3681,"date":"2015-06-22T11:58:30","date_gmt":"2015-06-22T15:58:30","guid":{"rendered":"https:\/\/engineering.jhu.edu\/magazine-archive\/?p=3681"},"modified":"2017-04-25T11:55:03","modified_gmt":"2017-04-25T15:55:03","slug":"healthy-measures","status":"publish","type":"post","link":"https:\/\/engineering.jhu.edu\/magazine-archive\/2015\/06\/healthy-measures\/","title":{"rendered":"Healthy Measures"},"content":{"rendered":"

\"WSE_HealthyMeasures_header\"<\/a>
\nIn labs across Johns Hopkins, engineers and medical experts are teaming up to design innovative devices, analyze vast treasure troves of data, and optimize complex systems to revolutionize health care. <\/em><\/p>\n

A little more than a century ago, visionaries lauded the founding of a fledgling school in applied science and advanced technology at the Johns Hopkins University as both critical and fundamental to the future development of the United States. At the same time, Johns Hopkins Hospital was a mere 25 years old, but was already on its way to setting the global standard in patient care, medical research, and teaching by\u2014among other things\u2014harnessing cutting-edge science and technology to improve patient outcomes.<\/p>\n

The ensuing 20th-century collaboration between engineers and clinicians changed health care in significant ways and spawned entirely new disciplines\u2014including biomedical engineering, imaging science, and computational medicine\u2014which in turn changed the way researchers understand the mechanics of the human body and biological systems.<\/p>\n

Today, the connections between engineering, medicine, and health care are increasingly diverse and span all disciplines. These collaborations hold great promise. The key to success in the 21st century, researchers says, lies in the development of innovative devices, real-time data analysis, and system optimizations that\u2014if executed\u2014will enable researchers to revolutionize medical treatment as a whole and improve patient care on an individual level. Today\u2019s holy grail: Using computing power and engineering prowess to create smarter, more efficient health care.<\/p>\n

In laboratories across the Homewood and medical campuses, researchers are teaming up in collaborative partnership to do just that.<\/p>\n

\"WSE_HealthyMeasures_1\"<\/a>NUMBER ONE:<\/strong> A REVOLUTION IN STROKE REHAB<\/strong><\/p>\n

Strokes can cause horrific losses, and some of the worst involve motor skills. Within hours, a perfectly agile person can lose the ability to speak fluently, to walk, or to reach for a glass of water.<\/p>\n

John Krakauer believes almost everything that is typically done to help stroke patients regain their motor skills is ineffective or worse\u2014and he has begun to test a radical new model. Krakauer, a professor of neurology and neuroscience at the School of Medicine, has recruited computer scientists trained at the Whiting School of Engineering to create a software engineering team known as Kata (the name derives from\u00a0a term for training in Japanese martial arts). The Kata project\u2019s first mission was to develop an immersive video game for stroke patients. After three years of development, the game began clinical trials this spring at three sites: Johns Hopkins, Columbia University, and the University of Zurich.<\/p>\n

In the Kata project\u2019s game, stroke patients place their neurologically affected arms in a robotic exoskeleton and use the device to control the movements of a virtual dolphin. The dolphin programming grew from hundreds of hours of collaboration with experts at Baltimore\u2019s National Aquarium and is extraordinarily realistic. When patients attain higher levels of dexterity, the game offers them the deep reward of embodied motor control.<\/p>\n

\u201cWhen I came to Hopkins from Columbia [in 2010], I already knew that I wanted to work with gamers,\u201d Krakauer says. \u201cI knew that the Computer Science Department offered a minor in gaming, so I went looking for people there.\u201d Within a few months, Krakauer had assembled the core of his team: Omar Ahmad \u201999, PhD \u201911; Kat McNally, an animator who\u2019d recently graduated from the Maryland Institute College of Art; Kevin Olds, a doctoral student in biomedical engineering at Johns Hopkins; and Promit Roy \u201810, a master\u2019s student at Whiting with experience at Microsoft and other firms.<\/p>\n

The dolphin game offers two enormous advantages over traditional neurological rehabilitation, according to Krakauer. First, it is not task-based, but instead encourages free exploration, unlike standard rehabilitation. In typical rehabilitation, patients are often coached to perform the same simple task over and over\u2014say, reaching for a glass of water at a 45-degree angle. Krakauer and the Kata team predict that immersive and highly varied movements in a three-dimensional environment outside the confines of a single task will promote brain repair.<\/p>\n

Second, the game environment potentially allows for much more time spent on rehabilitation tasks. Stroke patients typically receive only a few hours of structured practice each day, but an engaging game environment could permit much longer training sessions.<\/p>\n

If the two-year clinical trials prove successful, Krakauer says, \u201cthey might point to a full-blown revolution in the way stroke rehabilitation is carried out. Our [current] rehabilitation practices have been built on a certain pessimism about what\u2019s possible. I want to show how wrong that is.\u201d<\/p>\n

This project would not have been possible, Krakauer is quick to say, without strong collaboration between the Whiting School and the School of Medicine.<\/p>\n

\u201cThe key point,\u201d he says, \u201cis that you need to bring people who otherwise would go off and work at Pixar and Google and Apple\u2014you\u2019ve got to bring them into the university environment. We need to provide a home for top-level engineering and computer-science talent, without necessarily expecting them to want to be promoted and get tenure and write papers. Ahmad and the rest of the Kata team want to make mission-oriented, high-quality products. You\u2019ve got to bring this Silicon Valley culture into the university without demanding that they jump through the hoops that we more conventional academics jump through.\u201d<\/p>\n


\nNUMBER TWO: MORE TIMELY TRIAGE\"WSE_HealthyMeasures_2\"<\/a><\/strong><\/p>\n

When patients walk into an emergency department, the staff\u2019s first responsibility\u00a0is to do a rapid triage assessment: Is this person at immediate risk of dying? Is the patient actually so well that he should have just stayed home and taken an aspirin? Or somewhere in between?<\/p>\n

Scott Levin, an associate professor of emergency medicine at the School of Medicine who holds a joint appointment in Civil Engineering and directs the Systems Institute at the Whiting School, believes far too many emergency-room patients are classified in a vague in-between zone. If hospitals developed more accurate predictions at the time of triage, he says, they would be better able to allocate their resources\u2014and ultimately save more lives.<\/p>\n

In typical clinical practice, more than half of emergency patients are classified in the middle level\u20143\u2014of the five-level Emergency Severity Index. \u201cThat\u2019s a problem,\u201d Levin says, \u201cbecause it doesn\u2019t set up an appropriate queue. If you look at what actually happens to these patients, it turns out that the \u20183\u2019 level mixes patients who are very sick with patients who are very well.\u201d<\/p>\n

Over the last two years, Levin and his colleagues have developed a new triage algorithm known as the HopScore. To build the system, Levin analyzed hundreds of thousands of past patients\u2019 electronic records across multiple emergency departments. His goal was to find a mix of clinical indicators that retrospectively \u201cpredicted\u201d which patients ultimately experienced a critical event (death, intensive care admission, emergent surgery, or catheterization) or required hospital admission.<\/p>\n

Now Levin and his team think they have found a simple algorithm that outperforms the predictive power of classical triage systems. The significant components turned out to include the patient\u2019s chief complaint, vital signs, age, gender, and whether the patient arrived at the ER by ambulance, all routinely collected at triage in emergency departments. The HopScore is now being calculated for each patient who arrives at the Johns Hopkins Hospital or Howard County General Hospital Emergency Department\u2014but it isn\u2019t yet affecting clinical practice.<\/p>\n

\u201cIf it turns out that these scores have good predictive value, and we very much expect that they will,\u201d Levin says, \u201cthen we\u2019ll begin to use the HopScore to make rapid decisions about where to place patients and how to allocate staff resources.\u201d<\/p>\n

This kind of collaboration between engineers and clinicians is crucial to the future of medicine, Levin believes. When he earned his PhD in biomedical engineering roughly 15 years ago, he was supported by a National Science Foundation grant that required deep interdisciplinary study. \u201cThis is how I was trained, and this is how I love to work,\u201d he says. \u201cWe shouldn\u2019t be separated by method or by subject area\u2014we should work together around real-world objectives like patient safety.\u201d<\/p>\n


\n\"WSE_HealthyMeasures_3\"<\/a>NUMBER THREE: BRINGING BIG DATA TO RADIATION THERAPY<\/strong><\/p>\n

It might seem hard to imagine how computers could assist radiation oncologists more than they already do. Throughout the world, clinicians routinely use sophisticated digital tools to analyze patients\u2019 tumors, to calculate radiation doses, and to operate the machines that actually deliver the radiation.<\/p>\n

That might sound very state of the art. But a team of Johns Hopkins engineers and physicians believes that another wave of digital transformation in radiation oncology is\u00a0just starting to take shape. What would happen, the Johns Hopkins team wonders, if the computers in radiation clinics around the world began to learn from one another?<\/p>\n

\u201cWhat we have tried to do,\u201d says computer scientist Russell Taylor \u201970, the John C. Malone Professor at the Whiting School, \u201cis use all of the data from patients who have been previously treated to improve planning for future patients. If you build a learning system, you can use technology to make radiation therapy safer, more accurate, and more effective.\u201d<\/p>\n

The central challenge of radiation therapy is doing maximum damage to tumors while minimizing damage to nearby healthy tissue. In typical practice, radiation dosimetrists must go through a long trial-and-error process to calculate the optimal plan for each patient. But by statistically analyzing thousands of past patients\u2019 plans, Taylor and his colleagues have built computer tools that can make the dosimetry-planning process much faster and more efficient.<\/p>\n

\u201cWe can analyze outcomes data, toxicity data, and the three-dimensional relationships between target tissues and the tissues we want to spare,\u201d says the project\u2019s leader, Todd McNutt, an associate professor of radiation oncology physics at the School of Medicine. As the database\u2014known as Oncospace\u2014 gets larger over time, its accuracy and predictive power will only grow, McNutt adds.<\/p>\n

The system has already begun to improve clinical practice for head-and-neck cancer patients at Johns Hopkins. There is early evidence, McNutt says, that Oncospace-enhanced planning has reduced Johns Hopkins patients\u2019 rates of xerostomia, the severe dry mouth that often occurs when radiation damages the salivary glands.<\/p>\n

\u201cWhat has made this successful,\u201d McNutt says, \u201cis that we have a few physicians who are very sold on the concept. We\u2019ve been able to demonstrate to them that there\u2019s value in these processes\u2014value to their patients, and also academic value.\u201d<\/p>\n

This kind of partnership between engineers and clinicians is indispensable, says Taylor, a surgical-robotics pioneer who arrived at Johns Hopkins in 1995 after a long career in industry. \u201cI came to Hopkins,\u201d he says, \u201cbecause I realized that if I wanted to do this kind of work, it made a lot more sense to be in the same institution as the physicians who will use these projects.\u201d<\/p>\n


\n\"WSE_HealthyMeasures_4\"<\/a>NUMBER FOUR: MAKING LUPUS AND MS MORE PREDICTABLE <\/strong><\/p>\n

Autoimmune disorders, like multiple sclerosis and lupus, are notorious for their unpredictability. The illness flares\u2014often without any obvious trigger\u2014and then recedes, and then flares again. Some patients deteriorate rapidly, while others can live for years before the most severe symptoms arrive. This fog of unpredictability can make it very difficult for doctors and patients to choose treatment plans.<\/p>\n

Suchi Saria, an assistant professor of computer science at the Whiting School, believes she can shine a light through this fog. By statistically analyzing thousands of patients\u2019 electronic medical records, she hopes to discover previously unknown patterns of progression in autoimmune disease. Do lupus patients fall into distinct types that can be identified near the time of diagnosis? Does a change in, say, serum potassium levels predict that a patient with multiple sclerosis will soon suffer a flare-up?<\/p>\n

\u201cThese are diseases that are very poorly understood,\u201d Saria says. \u201cWe want\u00a0to develop computational frameworks for understanding them in greater levels of detail. Rather than treating the \u2018average\u2019 patient, we want doctors to be able to use large-scale population data to tailor decision-making for each individual.\u201d<\/p>\n

Saria\u2019s first test case is scleroderma, a rare autoimmune disorder that can cause lung scarring, pulmonary hypertension,and severe thickening of the skin. With the support of a major grant from the National Science Foundation, Saria and her doctoral student Peter Schulam have dived deeply into the electronic records of more than 3,000 patients with scleroderma. Those records have been painstakingly collected over a period of more than 20 years by the Johns Hopkins Scleroderma Center, which is led by a professor of rheumatology at the School of Medicine.<\/p>\n

Saria\u2019s team has found several distinct patterns of progression: Some patients, for example, have stable lung function for a long period of time but then abruptly deteriorate; others have lung function that declines in a constant, linear pattern; still others have faulty lung function near the time of diagnosis but then recover.<\/p>\n

The next step is to look for biomarkers that might correspond to these subtypes and offer early clues about how a newly diagnosed patient is likely to progress. Those biomarkers might also suggest new targets for drug development. Saria is launching that effort in collaboration with Johns Hopkins rheumatologists Fredrick Wigley, Livia Casciola-Rosen, and Laura Hummers, MS \u201910 (BSPH).<\/p>\n

\u201cJust by knowing that these patterns exist,\u201d Saria says, \u201cwe can now start to ask, \u2018Are you the individual who\u2019s going to stabilize, or are you the one who\u2019s going to show active decline?\u2019 If you\u2019re the one who\u2019s going to show active decline, I as a clinician may be more aggressive about starting therapies that might cause more side effects.\u201d<\/p>\n


\n\"WSE_HealthyMeasures_5\"<\/a>NUMBER FIVE: HARNESSING THE DATA IN NEUROIMAGING <\/strong><\/p>\n

Most engineers and clinicians at Johns Hopkins have a solid grasp of basic statistical techniques. But cutting-edge studies of medical imaging or robotics sometimes involve data sets that are so complex that they require guidance from top-level statisticians.<\/p>\n

That’s where Brian Caffo, a professor of biostatistics at the Bloomberg School of Public Health, comes in.<\/p>\n

Caffo\u2019s lab has collaborated with physicians and engineers throughout Johns Hopkins on a huge range of problems\u2014most recently on neuroimaging studies that aim to shed light on the structure and function of the brain.<\/p>\n

\u201cThe people we work with are really smart,\u201d Caffo says. \u201cThey know a lot of statistics. They know how to do most of this on their own. So what they come to us with are their most difficult data sets.\u201d<\/p>\n

In the last two years, Caffo has collaborated with clinicians and neuroscientists on brain-imaging studies involving autism, motor learning, attention disorders, and football-related concussions.<\/p>\n

What makes brain imaging so statistically challenging, Caffo says, is the sheer scale of the data. \u201cYou have complex spatial relationships in three dimensions, because certain areas of the brain are functionally related to their counterparts in the opposite hemisphere. You also have complex temporal relationships. On top of that, these studies are\u00a0beginning to involve larger numbers of subjects. Functional MRI studies used to include just a few patients, but now there can be hundreds.\u201d<\/p>\n

Neuroimaging is far from Caffo\u2019s only clinical interest. He also has recently provided statistical muscle for studies of sleep-disordered breathing, infection prevention in ICUs, and retinal damage in multiple sclerosis.<\/p>\n

Successful collaboration with clinicians, Caffo says, requires all parties to have a basic grasp of one another\u2019s language. \u201cI tell my graduate students, if you want to work with scientists and you don\u2019t just want to be a tourist in the field, you\u2019ve got to have at least a basic understanding of their clinical areas. For example, you can\u2019t walk into a neuroimaging project without knowing basic brain anatomy and function.\u201d<\/p>\n

Much of Caffo\u2019s work is in statistical method or theory. But that work is grounded in and motivated by his applied, collaborative work across Johns Hopkins. \u201cI work alongside the best research hospital in the world and the best school of public health,\u201d he says. \u201cIt would be a waste for me and the institution if I didn\u2019t take advantage of that.\u201d<\/p>\n


\n\"WSE_HealthyMeasures_6\"<\/a>NUMBER SIX: BETTER TRAINING FOR NOVICE SURGEONS<\/strong><\/p>\n

It\u2019s a century-old rite of passage for novice surgeons: being raked over the coals by an attending physician with thousands of hours more experience in dissecting, suturing, and manipulating tissues. But soon that traditional friendly (or not-so-friendly) instruction might be augmented by a much more dispassionate kind of guidance: computer systems that quantitatively analyze novice surgeons\u2019 hand movements and tell them exactly how and where they can improve.<\/p>\n

That is the vision of a team of Johns Hopkins engineers and surgeons led by Gregory Hager, Mandell Bellmore Professor and past chair of Computer Science. By analyzing tool movements in hundreds of surgeries, Hager\u2019s team has identified movement \u201csignatures\u201d that are characteristic of novice and expert surgeons.<\/p>\n

\u201cWhat we aim to do,\u201d Hager says, \u201cis to break complex tasks down into small units so we can provide surgeons-in-training with feedback on which parts of a particular task they\u2019re not doing well on.\u201d<\/p>\n

The team is already piloting such an approach at the Johns Hopkins Minimally Invasive Surgical Training and Innovation Center, with support from the National Science Foundation and the Johns Hopkins Science of Learning Institute. Among other things, the project, which focuses on robotic surgery, will follow individual surgeons throughout their training, noting the pace at which their characteristic hand movements make the transition from novice to expert. One focus of Hager\u2019s efforts is septoplasty, a nasal surgery commonly performed to correct a deviated septum. In collaboration with Lisa Ishii MS \u201909 (BSPH) and Masaru Ishii, associate professors of otolayrngology at the School of Medicine, Hager\u2019s team is testing new methods of training novices to perform the procedure.<\/p>\n

\u201cSeptoplasty is traditionally very difficult to teach,\u201d Hager says, \u201cbecause it\u2019s hard to show a trainee exactly what an expert is doing deep in the nose.\u201d<\/p>\n

By attaching tiny motion sensors to surgical instruments in septoplasty, Hager and his colleagues have been able to provide new kinds of feedback to novice surgeons.<\/p>\n

\u201cSomewhat accidentally,\u201d Hager says, \u201cwe set up a computer screen showing the instrument tracking, just so we could see what was going on. That in itself was already\u00a0an innovation\u2014the attendings and the trainees could now see on a screen how the tool was moving inside the nose. Now they can say, for example, \u2018Your strokes are too short and tentative; you should move further under the skin flap here and make longer strokes.\u2019\u201d<\/p>\n

Computers will never replace human instruction, of course. As Hager notes, surgical skill involves far more than hand movements. Nonetheless, he believes that data-driven improvements in technical-skill measurement could profoundly transform the practice of surgery.<\/p>\n

\u201cWe\u2019re really just at the beginning of the age of studying surgery as a data science,\u201d he says. \u201cCould we start to quantify interventional procedures in such a way that we can advance practice and improve patient outcomes? Can we develop scorecards that can follow surgeons throughout their careers and help them to maintain and improve their skills?\u201d \u201cWe couldn\u2019t do any of this without having the School of Medicine completely on board,\u201d says Hager. \u201cThis kind of sustained teamwork sets the stage for new discoveries.”<\/p>\n","protected":false},"excerpt":{"rendered":"

In labs across Johns Hopkins, engineers and medical experts are teaming up to design innovative devices, analyze vast treasure troves of data, and optimize complex systems to revolutionize health care. A little more than a century ago, visionaries lauded the founding of a fledgling school in applied science and advanced technology at the Johns Hopkins…<\/p>\n","protected":false},"author":4,"featured_media":3682,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[28],"tags":[],"class_list":["post-3681","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-features","issue-summer-2015"],"acf":[],"yoast_head":"\nHealthy Measures - JHU Engineering Magazine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/engineering.jhu.edu\/magazine-archive\/2015\/06\/healthy-measures\/\" \/>\n<link rel=\"next\" href=\"https:\/\/engineering.jhu.edu\/magazine-archive\/2015\/06\/healthy-measures\/2\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Healthy Measures - JHU Engineering Magazine\" \/>\n<meta property=\"og:description\" content=\"In labs across Johns Hopkins, engineers and medical experts are teaming up to design innovative devices, analyze vast treasure troves of data, and optimize complex systems to revolutionize health care. 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