{"id":5185,"date":"2018-09-25T10:59:27","date_gmt":"2018-09-25T10:59:27","guid":{"rendered":"https:\/\/athis-consulting.com\/news\/?p=5185"},"modified":"2018-10-02T10:26:40","modified_gmt":"2018-10-02T10:26:40","slug":"ai-could-provide-moment-by-moment-nursing-for-a-hospitals-sickest-patients","status":"publish","type":"post","link":"https:\/\/athis-technologies.com\/news\/uncategorized\/2018\/ai-could-provide-moment-by-moment-nursing-for-a-hospitals-sickest-patients\/","title":{"rendered":"AI Could Provide Moment-by-Moment Nursing for a Hospital\u2019s Sickest Patients"},"content":{"rendered":"<p>In a hospital\u2019s intensive care unit (ICU),\u00a0the sickest patients receive round-the-clock care as they lie in beds with their bodies connected to a bevy of surrounding machines. This advanced medical equipment is designed to keep an ailing person alive. Intravenous fluids drip into the bloodstream, while mechanical ventilators push air into the lungs. Sensors attached to the body track heart rate, blood pressure, and other vital signs, while bedside monitors graph the data in undulating lines. When the machines record measurements that are outside of normal parameters, beeps and alarms ring out to alert the medical staff to potential problems.<\/p>\n<p>While this scene is laden with high tech, the technology isn\u2019t being used to best advantage. Each machine is monitoring a discrete part of the body, but the machines aren\u2019t working in concert. The rich streams of data aren\u2019t being captured or analyzed. And it\u2019s impossible for the ICU team\u2014critical-care physicians, nurses, respiratory therapists, pharmacists, and other specialists\u2014to keep watch at every patient\u2019s bedside.<\/p>\n<p>The ICU of the future will make far better use of its machines and the continuous streams of data they generate. Monitors won\u2019t work in isolation, but instead will pool their information to present a comprehensive picture of the patient\u2019s health to doctors. And that information will also flow to artificial intelligence (AI) systems, which will autonomously adjust equipment settings to keep the patient in optimal condition.<\/p>\n<p>At our company,\u00a0<a href=\"http:\/\/www.autonomoushealthcare.com\/\">Autonomous Healthcare<\/a>, based in Hoboken, N.J., we\u2019re designing and building some of the first AI systems for the ICU. These technologies are intended to provide vigilant and nuanced care, as if an expert were at the patient\u2019s bedside every second, carefully calibrating treatment. Such systems could relieve the burden on the overtaxed staff in critical-care units. What\u2019s more, if the technology helps patients get out of the ICU sooner, it could bring down the skyrocketing costs of health care. We\u2019re focusing initially on hospitals in the United States, but our technology could be useful all around the world as populations age and the prevalence of chronic diseases grows.<\/p>\n<p>The benefits could be huge. In the United States, ICUs are among the\u00a0<a href=\"https:\/\/www.sccm.org\/Communications\/Critical-Care-Statistics\">most expensive components<\/a>\u00a0of the health care system. About 55,000 patients are cared for in an ICU every day, with the\u00a0<a href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/15942342\">typical daily cost<\/a>\u00a0ranging from US $3,000 to $10,000. The cumulative cost is more than $80 billion per year.<\/p>\n<p>As\u00a0<a href=\"https:\/\/census.gov\/content\/dam\/Census\/library\/publications\/2015\/demo\/p25-1143.pdf\">baby boomers reach old age<\/a>\u00a0[PDF], ICUs are becoming increasingly important. Today, more than half of ICU patients in the United States are over the age of 65\u2014a demographic group that\u2019s expected to grow from 46 million in 2014 to 74\u00a0million by 2030. Similar trends in Europe and Asia make this a worldwide problem. To meet the growing demand for acute clinical care, ICUs will need to increase their capacity as well as their capabilities. Training more critical-care specialists is part of the solution\u2014but so is automation. Far from replacing humans, AI systems could become part of the medical team, allowing doctors and nurses to deploy their skills when and where they\u2019re needed most.<\/p>\n<aside class=\"inlay xlrg\">\n<h3 class=\"sb-hed\">Breathing Easier<\/h3>\n<figure role=\"img\"><figure style=\"width: 1240px\" class=\"wp-caption alignnone\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/image\/MzEzMzk1OQ.jpeg\" alt=\"illustration\" width=\"1240\" height=\"1100\" \/><figcaption class=\"wp-caption-text\">Illustration:\u00a0MCKIBILLO Critically ill patients who need help breathing are put on mechanical ventilators\u00a0[1]. These machines push air into the lungs, but the rhythm can get out of sync with natural breathing patterns, causing patients to \u201cfight the ventilator.\u201d A smart control system could read airflow measurements\u00a0[2] and identify different types of ventilator asynchrony\u00a0[3] in real time via a machine-learning algorithm. In a fully autonomous system, an adaptive controller\u00a0[4] would constantly adjust the ventilator\u2019s airflow to keep it in sync with the patient. As a step toward the goal of full autonomy, a similar system could be used as a decision-support tool in the ICU, providing recommendations that respiratory therapists could use to make adjustments.<\/figcaption><\/figure><figcaption class=\"hi-cap\"><\/figcaption><\/figure>\n<\/aside>\n<p><strong>In ICUs today<\/strong>, the data from the raft of bedside monitors is usually lost as the monitor screens refresh every few seconds. While some advanced ICUs are now trying to archive these measurements, they still struggle to mine the data for clinical insights.<\/p>\n<p>A human doctor typically has neither the time nor the tools to make sense of the rapidly accumulating data. But an AI system does. It could also take actions based on the data, such as adjusting the machines involved in crucial ICU tasks. At Autonomous Healthcare, we\u2019re focusing first on AI systems that could manage a patient\u2019s ventilation and fluids. Mechanical ventilators come into play when a patient is sedated or suffers lung failure, a common ICU condition. And careful fluid management maintains the proper volume of blood flowing through a patient\u2019s circulatory system, therefore ensuring that all the tissues and organs get enough oxygen.<\/p>\n<p>Our methodologies spring from an unlikely source: the aerospace industry. Two of us,\u00a0<a href=\"https:\/\/www.ae.gatech.edu\/people\/wassim-m-haddad\">Haddad<\/a>\u00a0and\u00a0<a href=\"https:\/\/www.linkedin.com\/in\/behnood-gholami-389b784\/\">Gholami<\/a>, are aerospace control engineers. We met at Georgia Tech\u2019s school of aerospace engineering, where Haddad is a professor of dynamical systems and control and Gholami formerly worked as a doctoral researcher.\u00a0<a href=\"https:\/\/www.ngpg.org\/james-bailey-phd-md\">Bailey<\/a>\u00a0joined the collaboration in the early 2000s when he was an associate professor of anesthesiology at the Emory University school of medicine. Haddad and Bailey first worked on control methods to automate anesthesia dosing and delivery in the operating room, which we tested in clinical studies at Emory University Hospital, in Atlanta, and Northeast Georgia Medical Center, in Gainesville, Ga. We then set our sights on more complex and broader control problems for the ICU. In 2013, Haddad and Gholami founded Autonomous Healthcare to commercialize our AI systems. Gholami is the company\u2019s CEO, Haddad is chief science advisor, and Bailey is chief medical officer.<\/p>\n<p>How is aerospace similar to medicine? Both fields involve vast amounts of data that must be processed quickly to make decisions while lives hang in the balance, and both require that many tasks be performed simultaneously to keep things running smoothly. In particular, we see a role for feedback control technology in critical-care medicine. These technologies use algorithms and feedback to modify the behavior of engineered systems through sensing, computation, and actuation. They have become ubiquitous in the safety-critical systems of flight control and air traffic control.<\/p>\n<p>However, there\u2019s a key difference between an airplane and a hospital patient. An airplane\u2019s design and control is based on well-established theories of mechanics and aerodynamics, whereas the human body involves highly complex biological systems that function and interact in ways we don\u2019t yet entirely understand.<\/p>\n<p>Consider the management of mechanical ventilation. ICU patients may require this support because of direct trauma, lung infection, heart failure, or an inflammatory syndrome such as sepsis. The ventilator alternates between forcing air into the lungs and allowing the lungs to passively deflate. The device can be dialed up or down to do all of the work or to assist the patient\u2019s own efforts.<\/p>\n<p>The interaction between human and machine is a subtle thing to manage. The human body has its own automatic mechanism to govern breathing, in which the nervous system triggers the\u00a0<a href=\"https:\/\/en.wikipedia.org\/wiki\/Thoracic_diaphragm\">diaphragm muscle<\/a>\u00a0to contract and pull downward on the lungs, thus initiating the intake of air. The ventilator must work with this innate drive; it should be synchronized with the patient\u2019s natural transitions between inhaling and exhaling, and it should match the natural air volume of the patient\u2019s breathing.<\/p>\n<p>&nbsp;<\/p>\n<figure class=\"lt med\" role=\"img\">\n<p><figure style=\"width: 283px\" class=\"wp-caption alignleft\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/image\/MzEzNTI3OQ.jpeg\" alt=\"\" width=\"283\" height=\"219\" \/><figcaption class=\"wp-caption-text\">Photo : ventilator management . In Sync:\u00a0To keep patients breathing in time with their mechanical ventilators, the Syncron-E system from Autonomous Healthcare analyzes airflow.<\/figcaption><\/figure><figcaption class=\"hi-cap\">Unfortunately, mismatches between the patient\u2019s demand and the machine\u2019s delivery are all too common, which can cause a patient to \u201cfight the ventilator.\u201d For example, a patient may naturally need more time to inhale, but the ventilator transitions to the exhalation prematurely. This and other synchronization problems with mechanical ventilation are associated with longer stints on the ventilator, longer stays in the ICU, and increased risk of death. Experts don\u2019t yet know why asynchrony has these detrimental effects, but patients clearly experience discomfort when trying to breathe out while the machine is pushing air into their lungs, and their laboring muscles experience an additional workload. In ICUs in the United States, the share of patients on ventilators who experience severe asynchrony has been estimated to be between 12 and 43 percent.<\/figcaption><\/figure>\n<p>The first step in addressing this problem is to detect it. Experienced respiratory therapists can identify different types of asynchrony if they continuously monitor the waveforms on a ventilator\u2019s display screen indicating the pressure and flow. But in an ICU, one respiratory therapist typically oversees 10 or more patients and can\u2019t possibly monitor all of them\u00a0constantly.<\/p>\n<p>At our company, we\u2019ve designed a machine-learning framework that replicates that human expertise in detecting different types of asynchrony. To train our system, we used a data set of waveforms from patients on ventilators, in which each waveform had been evaluated by a panel of clinical experts. Our algorithm learned the signatures of different asynchrony types\u2014such as a particular dip in the flow signal at a specific point in time. In our first assessments of the algorithm\u2019s performance, we focused on what\u2019s called cycling asynchrony, which is the most challenging type to detect. Here the ventilator\u2019s initiation of the exhale doesn\u2019t match the patient\u2019s own exhalation. The accuracy of our algorithm in detecting cycling asynchrony in a new data set matched that of human experts.<\/p>\n<p>We\u2019re now testing the algorithm at Northeast Georgia Medical Center\u2019s ICU to detect respiratory asynchrony in real patients and in real time. The technology has been incorporated into a clinical-decision support system, which is designed to help respiratory therapists assess a patient\u2019s needs. This framework can also provide researchers with a tool to better understand the underlying causes of asynchrony and its impact on patients. Our long-term goal is to design mechanical ventilators that can automatically adjust their own settings in response to each patient\u2019s needs.<\/p>\n<p><strong>When you picture an ICU,<\/strong>\u00a0your mental image probably includes patients with plastic bags hanging from stands by their bedsides, fluids continually dripping into their veins through IVs. About 75 percent of patients require such fluid management at some point during their stay in the ICU.<\/p>\n<p>However, calibrating the correct amount of fluid is far from an exact science. Just tracking a patient\u2019s fluid levels is a hard task: No existing medical sensors can directly monitor fluid volume, so doctors rely on indirect indicators like blood pressure and urine volume. The amount of fluids that patients need depends on their illness and medications, among other things.<\/p>\n<aside class=\"inlay xlrg\">\n<h3 class=\"sb-hed\">Fluid Movements<\/h3>\n<figure role=\"img\"><figure style=\"width: 1240px\" class=\"wp-caption alignnone\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/image\/MzEzMzk5OA.jpeg\" alt=\"\" width=\"1240\" height=\"1100\" \/><figcaption class=\"wp-caption-text\">Illustration:\u00a0MCKIBILLO Most ICU patients require infusion pumps and IVs\u00a0[1] to drip fluid into their veins. Getting the fluid volume right is crucial: If levels are either too low or too high in the circulatory system, serious complications can arise. A smart control system could track real-time measurements\u00a0[2] such as arterial blood pressure and the amount of blood pumped by the heart; the system could then feed the data into a physiological model\u00a0[3] that represents how fluids move through the body\u2019s blood vessels and tissues. In a fully autonomous system, an adaptive controller\u00a0[4] could continuously adjust fluid inputs to keep the patient stable. Initially, ICU physicians could use the technology as a decision-support system that provides recommendations.<\/figcaption><\/figure><figcaption class=\"hi-cap\"><\/figcaption><\/figure>\n<\/aside>\n<p>Getting the fluids right is particularly important for patients with\u00a0<a href=\"https:\/\/www.healthline.com\/health\/sepsis\">sepsis<\/a>, a life-threatening syndrome characterized by inflammation throughout the body. In these patients, the blood vessels dilate, thus reducing blood pressure, and fluid leaks from the tiniest vessels, the capillaries. As a result, less oxygen-carrying blood reaches the organs, which can cause organs to fail and patients to die. Doctors combat sepsis by dispensing drugs to boost blood pressure and pumping extra fluids into patients\u2019 circulatory systems.<\/p>\n<p>It\u2019s important to add enough fluid, but not too much\u2014an\u00a0excess can cause complications such as\u00a0<a href=\"https:\/\/www.healthline.com\/health\/pulmonary-edema\">pulmonary edema<\/a>, a buildup of fluid in the lungs that can interfere with breathing. Studies have shown that fluid overload is associated with more days on mechanical ventilators, longer stays in the hospital, and higher rates of mortality.<\/p>\n<p>Doctors therefore aim to maintain their patients\u2019 fluids at certain levels, which are based on models for an average patient. When the doctors come through the ICU on their rounds, they try to determine whether the patient is holding steady at the goal level by checking the mix of gases in the blood and monitoring blood pressure and urine output. Deciding when to add fluids and how much to add is highly subjective, and there\u2019s considerable debate about the best practices.<\/p>\n<p>An AI system could do better. Rather than basing its decisions on goals established for an average patient, it could analyze a wide variety of physiological indicators for an individual patient in real time, and continuously dispense fluids according to that patient\u2019s specific needs.<\/p>\n<p>At Autonomous Healthcare, we\u2019ve developed a fully automated system that looks at indirect measurements of a patient\u2019s fluid levels (such as blood pressure and variation in the volume of blood pumped out by each heartbeat) and then feeds the data into a sophisticated physiological model. Our system uses these measurements to assess how fluids are moving between the body\u2019s blood vessels and tissues, constantly adjusting the parameters as new measurements come in. Our proprietary adaptive controller then adjusts the fluid-infusion settings accordingly.<\/p>\n<p>One advantage of our technology is its attention to what control engineers call closed-loop system stability, which means that any perturbations to a normal state lead to only small and fleeting variations. Many engineering applications use control systems that guarantee closed-loop stability\u2014when an airplane runs into powerful turbulence, for instance, the autopilot system compensates to keep the shaking to a minimum. However, most control systems for medical devices have no such guarantee. If doctors judge that a sepsis patient\u2019s fluid levels have dramatically dropped, they might push a large volume of fluid into the bloodstream, perhaps overcompensating.<\/p>\n<figure class=\"lt med\" role=\"img\">\n<p><figure style=\"width: 352px\" class=\"wp-caption alignleft\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/image\/MzEzNDAyMw.jpeg\" alt=\"photo, fluid management\" width=\"352\" height=\"282\" \/><figcaption class=\"wp-caption-text\">Photo:\u00a0Autonomous Healthcare Inflow:\u00a0To prevent ICU patients from getting too much or too little fluid from their infusion pumps, Autonomous Healthcare\u2019s CLARC system takes readings from the circulatory system.<\/figcaption><\/figure><figcaption class=\"hi-cap\"><\/figcaption>We\u2019ve already tested our automated fluid-management system in collaboration with William Muir, a veterinary anesthesiologist and cardiovascular physiologist. Working with dogs that were experiencing hemorrhages, we used our system to regulate their fluid infusions. Our system successfully kept the dogs in stable condition as measured by the volume of blood pumped with every\u00a0heartbeat.<\/figure>\n<p>We need to do more testing in order to win regulatory approval for a fully automated fluid-management system for humans. As with our work on ventilator management, we can start by building a decision support system for the ICU. This \u201chuman in the loop\u201d system will present information and recommendations to the clinician, who will then adjust the settings of the infusion pump accordingly.<\/p>\n<p><strong>Looking beyond ventilation<\/strong>\u00a0and fluid management, other key aspects of patient care that could be automated include pain management and sedation. In the ICU of the future, we envision many such clinical operations being monitored, coordinated, and controlled by AI systems that assess each patient\u2019s physiological state and adjust equipment settings in real time.<\/p>\n<p>To make this vision a reality, though, it won\u2019t be enough for engineers to produce reliable technology. We must also find our way through many regulatory barriers and institutional requirements at hospitals.<\/p>\n<p>Clearly, regulators need to scrutinize any new autonomous medical system. We suggest that regulatory agencies make use of two testing frameworks commonly used in the automotive and aerospace industries. The first is in silico trials, which test an algorithm through computer simulations. These tests are useful only if the simulations are based on high-fidelity physiological models, but in certain applications this is already possible. For example, the U.S. Food and Drug Administration recently approved the use of in silico testing as a replacement for animal testing in efforts to develop an\u00a0<a href=\"https:\/\/www.fda.gov\/MedicalDevices\/ProductsandMedicalProcedures\/HomeHealthandConsumer\/ConsumerProducts\/ArtificialPancreas\/default.htm\">artificial pancreas<\/a>\u00a0for diabetics.<\/p>\n<p>The second useful framework is hardware-in-the-loop testing, where hardware stands in for the object of interest, whether it\u2019s a jet engine or the human circulatory system. You can then test a device\u2014an autonomous fluid pump, say\u2014on the hardware platform, which will generate the same type of data you\u2019d see on an actual patient\u2019s bedside monitor. These hardware-in-the-loop trials can show that the device performs well in real time and in the real world. Once these technologies have been proven with stand-ins for critically ill humans, testing can begin with real patients.<\/p>\n<p>To bring these technologies into hospitals, the final step is to win the trust of the medical community. Medicine is a generally conservative environment\u2014and for good reason. No one wants to make changes that might threaten the health of patients. Our approach is to prove our technologies in stages: We\u2019ll first commercialize decision-support systems to demonstrate their efficacy and benefits, and then move to truly autonomous systems. With the addition of AI, we believe ICUs can be smarter, safer, and healthier places.<\/p>\n<p><em>This article appears in the October 2018 print issue as \u201cAI in the ICU.\u201d<\/em><\/p>\n<h2>About the Authors<\/h2>\n<p><a href=\"https:\/\/www.linkedin.com\/in\/behnood-gholami-389b784\">Behnood Gholami<\/a>\u00a0and\u00a0<a href=\"http:\/\/haddad.gatech.edu\/\">Wassim Haddad<\/a>\u00a0are cofounders of\u00a0<a href=\"http:\/\/www.autonomoushealthcare.com\/\">Autonomous Healthcare<\/a>, based in Hoboken, N.J. Gholami is now the company\u2019s CEO. Haddad, an IEEE Fellow, serves as chairman of the board and chief scientific advisor, as well as being the David Lewis Professor of Dynamical Systems and Control in the School of Aerospace Engineering at the Georgia Institute of Technology.\u00a0<a href=\"http:\/\/www.autonomoushealthcare.com\/bailey\/\">James M. Bailey<\/a>\u00a0is the company\u2019s chief medical officer and also medical director of critical care at the Northeast Georgia Physicians Group, in Gainesville, Ga.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In a hospital\u2019s intensive care unit (ICU),\u00a0the sickest patients receive round-the-clock care as they lie in beds with their bodies connected to a bevy of surrounding machines. This advanced medical equipment is designed to keep an ailing person alive. Intravenous fluids drip into the bloodstream, while mechanical ventilators push air into the lungs. Sensors attached [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":5191,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"amp_status":"","_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false}}},"categories":[208,207,241,1],"tags":[127,293,400,125,336,399],"jetpack_publicize_connections":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.8 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>AI Could Provide Moment-by-Moment Nursing for a Hospital\u2019s Sickest Patients - AthisNews<\/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:\/\/athis-technologies.com\/news\/uncategorized\/2018\/ai-could-provide-moment-by-moment-nursing-for-a-hospitals-sickest-patients\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI Could Provide Moment-by-Moment Nursing for a Hospital\u2019s Sickest Patients - AthisNews\" \/>\n<meta property=\"og:description\" content=\"In a hospital\u2019s intensive care unit (ICU),\u00a0the sickest patients receive round-the-clock care as they lie in beds with their bodies connected to a bevy of surrounding machines. 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