{"id":64454,"date":"2022-06-16T13:56:00","date_gmt":"2022-06-16T19:56:00","guid":{"rendered":"https:\/\/www.uchealth.org\/today\/?p=64454"},"modified":"2024-07-17T10:06:10","modified_gmt":"2024-07-17T16:06:10","slug":"monitoring-for-sepsis-artificial-intelligence-and-human-skill-combined","status":"publish","type":"post","link":"https:\/\/www.uchealth.org\/today\/monitoring-for-sepsis-artificial-intelligence-and-human-skill-combined\/","title":{"rendered":"Artificial intelligence and human skill combine to help patients avoid ICU"},"content":{"rendered":"
<\/div>

 <\/p>\n

\"A
A critical care nurse keeps tabs on patients flagged by the AI system in the UCHealth Virtual Health Center. Combining artificial intelligence and virtual health assists in monitoring sepsis and identifying early deterioration of a patient’s condition. Photo by Joel Blocker, for UCHealth.<\/figcaption><\/figure>\n

The patient, 56, had undergone heart surgery and was recovering in a UCHealth medical-surgical unit when his nurse got an alert from the UCHealth Virtual Health Center. The nurse had been periodically checking in on the patient, and he had seemed to be doing fine \u2013 certainly compared to another patient who had been her focus all morning.<\/p>\n

The Virtual Health Center had noted an onset of rapid breathing and a drop in oxygen saturation to just 86% (below 90% is a warning sign). The nurse checked the patient\u2019s heart rate and found an abnormal beat. The patient was deteriorating \u2013 a medical term for an often abrupt plunge in status that can land someone in an intensive care unit (ICU) or, too often, worse.<\/p>\n

The nurse ordered tests, upped the patient\u2019s oxygen, and supplemented low potassium and magnesium levels. Rather than deteriorating into an ICU visit, the patient recovered quickly. He was well enough to go home the next day.<\/p>\n

It was a success story with roots in a three-year UCHealth-wide effort to apply artificial intelligence (AI) to the problem of detecting patients who are poised to abruptly decline. The approach is saving more than 200 lives a year.<\/p>\n

First, monitoring for sepsis<\/strong><\/h2>\n

The initial focus was sepsis, a systemic blood infection that strikes 1.7 million people in the United States each year (that\u2019s twice the number of annual U.S. heart attacks), killing 270,000 of them, according to<\/a> the U.S. Centers for Disease Control and Prevention.<\/p>\n

\"Dr.
Dr. CT Lin<\/figcaption><\/figure>\n

Time is of the essence with sepsis treatment. The typically subdued CDC resorts to all-caps in its patient explainer: \u201cACT FAST. Get medical care IMMEDIATELY\u2026\u201d The same applies to those already hospitalized, and the problem\u2019s magnitude is such that it has attracted plenty of attention among technologists. Epic, whose electronic health record powers MyHealthConnection, offers an AI-powered algorithm with dozens of indicators that could point to incipient sepsis \u2013 high heart rate, low blood pressure, fever, shortness of breath, and lab results, among others. But Dr. CT Lin<\/a>, a University of Colorado School of Medicine internal medicine physician and UCHealth\u2019s chief medical information officer, says the technology, however sophisticated, isn\u2019t enough.<\/p>\n

\u201cMost programs around the country are very happy to draw the finish line at saying, \u2018Look, we built a sepsis algorithm,\u2019\u201d Lin said. \u201cAnd we\u2019re like, \u2018That\u2019s negligible work. It doesn\u2019t affect patient care. You haven\u2019t actually put it into place to change the quality of care. We\u2019ve actually done that.\u2019\u201d<\/p>\n

It wasn\u2019t easy. Using the Epic Sepsis Model and the Epic Deterioration Index as starting points, UCHealth data scientists and machine learning experts had to choose and customize algorithms to improve the models\u2019 predictive accuracy, Lin says, the aim being to foresee a patient\u2019s deterioration up to 12 hours in advance. They fed the system three years of historical patient data, with variables including dozens of vital signs, lab values, medications being administered, procedures, and more. There was also the matter of deciding how to determine when a patient should be considered to have deteriorated. The team settled on unplanned ICU transfers, unexpected mortality, and code blues (the patient\u2019s heart or breathing has stopped).<\/p>\n

Virtual Health Center provides continued monitoring<\/strong><\/h2>\n

A key decision was how to handle the familiar tradeoff between a diagnostic\u2019s sensitivity and specificity. Sensitivity is about detecting what could be a problem. Specificity is about nailing down an actual problem. The more sensitive, the more false alarms and the less specific. UCHealth leadership made the decision to go for sensitivity, Lin says. That way, they would be more certain to catch deteriorations.<\/p>\n

\u201cOur leadership team said, \u2018Don\u2019t miss anything,\u2019\u201d Lin recalled. \u201cBut when you tune the system to be super sensitive, guess what? The noise is going to be terrible. It\u2019s not going to be specific.\u201d<\/p>\n

The result was a system that would flag 61 patients a day for every one or two actual<\/em> deteriorations. That\u2019s not a problem in itself. But for an alert system to really work, you need four things, says Dr. Diana Breyer<\/a>, UCHealth\u2019s Northern Colorado medical director and the health system\u2019s chief quality analytics officer.<\/p>\n

\u201cYou need the data. You need the algorithms. Somebody has to notice. Then you have to do something,\u201d Breyer said.<\/p>\n

\"Dr.
Dr. Diana Breyer<\/figcaption><\/figure>\n

The team had accomplished the first two steps. The second two presented the entirely different challenges of noting and acting. Initially, the team fed the alerts directly to bedside nurses. But those nurses were already busy helping patients deteriorating now, not at risk of doing so 12 hours from now. The 61 alerts per day were more than they could digest.<\/p>\n

The answer came in the form of the UCHealth Virtual Health Center, which is staffed around the clock with a critical-care physician and four critical-care nurses who serve all 12 UCHealth hospitals.<\/p>\n

Virtual presence combined with AI<\/strong><\/h2>\n

The Virtual Health Center team monitored the deterioration status of patients the AI system flagged and called bedside nurses only with regard to patients, who their experience and expertise told them were at risk of deteriorating. Their ability to do so had to do with more than just having the time to dedicate to monitoring deterioration, says Amy Hassell, a critical-care nurse and the UCHealth\u2019s Virtual Health Center\u2019s nursing director.<\/p>\n

\u201cWe have sepsis-response teams through the system, but one team may only see a deterioration every 15 to 20 days, so it\u2019s not something that frontline nurses \u2013 especially novice nurses \u2013 have seen,\u201d Hassell said. \u201cSo it was a matter of making it scalable.\u201d<\/p>\n

That step improved the accuracy of the overall deterioration-detection system by a factor of 30. Now, someone flagged by a machine and filtered by a trained professional poses a greater than 50-50 risk of deteriorating in the next 12 hours.<\/p>\n

The result has been a sharply improved speed of administering fluids (from 96 minutes to 19 minutes) and IV antibiotics (from 94 minutes to 45 minutes). Those and other measures have saved an estimated 211 lives a year, Lin says.<\/p>\n

In March, UCHealth\u2019s hospitals in northern Colorado rolled out an additional monitoring tool: rollaway patient-observing cameras that the Virtual Health team monitors for six hours after a likely deterioration call is made. That system is slated to go live in UCHealth\u2019s southern Colorado hospitals in August and at UCHealth University of Colorado Hospital<\/a> on the Anschutz Medical Campus<\/a> in October, Breyer says.<\/p>\n

The team is also expanding beyond sepsis to deterioration of other sorts as well as applying its lessons to hospital-acquired pressure injuries (HAPI, a.k.a. bedsores) and to patients at high risk of falls, Lin says.<\/p>\n

He likens UCHealth\u2019s deterioration-monitoring program to the centaur<\/a> of Greek mythology, though rather than half-man, half-horse, it\u2019s half-person, half-machine \u2013 a distributed cyborg. Regardless, the program\u2019s key lessons \u2013 that success with AI depends on what human intelligence can do with the machine\u2019s outputs \u2013 will endure.<\/p>\n","protected":false},"excerpt":{"rendered":"

  The patient, 56, had undergone heart surgery and was recovering in a UCHealth medical-surgical unit when his nurse got an alert from the UCHealth Virtual Health Center. The nurse had been periodically checking in on the patient, and he had seemed to be doing fine \u2013 certainly compared to another patient who had been […]<\/p>\n","protected":false},"author":23,"featured_media":64459,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","footnotes":""},"categories":[5],"tags":[3273,9095],"class_list":["post-64454","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-innovative-care","tag-innovative-medical-technologies","tag-uchealth-virtual-health-center"],"acf":[],"yoast_head":"\nMonitoring for sepsis: virtual presence combined with AI - 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