{"id":4763,"date":"2016-05-25T00:00:00","date_gmt":"2016-05-25T06:00:00","guid":{"rendered":"https:\/\/www.uchealth.org\/today\/2016\/05\/25\/uchealth-taps-machine-learning-to-boost-capacity\/"},"modified":"2021-09-07T17:50:12","modified_gmt":"2021-09-07T23:50:12","slug":"uchealth-taps-machine-learning-to-boost-capacity","status":"publish","type":"post","link":"https:\/\/www.uchealth.org\/today\/uchealth-taps-machine-learning-to-boost-capacity\/","title":{"rendered":"UCHealth taps machine learning to boost capacity"},"content":{"rendered":"
<\/div>

UCHealth is using the technology at the heart of Google\u2019s self-driving cars and Netflix\u2019s recommendation engine to help maximize the use of chemotherapy infusion beds, operating room time and, soon, much more.<\/p>\n

The technology is called machine learning<\/a>. It lets computers learn from vast data sets and uncover hidden patterns and insights as they do it. While UCHealth employs no shortage of technical talent, machine learning is a highly specialized field that combines data science and advanced mathematics.<\/p>\n

Steve Hess, UCHealth\u2019s chief information officer, says predictive analytics and machine learning can help boost capacity by optimizing operations \u2013 a key to increasing patient and provider satisfaction, improving efficiency and delaying the need for physical expansion.<\/figcaption><\/figure>\n

To tap the potential of machine learning to manage complex hospital operations, Steve Hess, UCHealth\u2019s chief information officer, turned to Santa Clara, Calif.-based LeanTaaS<\/a> for its expertise. The company\u2019s chief technology officer led the cloud computing team at Yahoo as well as distributed computing at Oracle. LeanTaaS\u2019s team of data scientists and mathematicians quickly customized the company\u2019s iQueue data analytics software for University of Colorado Cancer Center\u2019s Infusion Center. The system went live<\/a> in October 2015, analyzing the Infusion Center\u2019s operations.<\/p>\n

The results were striking. Patient wait times plunged 60 percent during peak hours and were an average of 33 percent lower throughout the day \u2013 despite increases of 16 percent and 7 percent, respectively, in patient peak-time and overall volumes, Hess said. There was also a 28 percent drop in Infusion Center staff overtime hours.<\/p>\n

\u201cSo it was win-win-win,\u201d Hess said.<\/p>\n

Complex puzzle
\n<\/strong><\/p>\n

The team, including Tom Gronow, chief operating officer of University of Colorado Hospital, considered other applications for the iQueue software and settled on operating rooms, where freeing up capacity, minimizing patient wait time and optimizing surgery-team time is a constant challenge. ORs are a prized hospital resource, too: Each percentage-point increase in a single OR\u2019s utilization adds $100,000 to a hospital\u2019s bottom line, Hess said.<\/p>\n

There are 38 ORs at UCH alone, Hess said, including 25 inpatient, eight outpatient, three Eye Center, and two Lone Tree Surgery Center spaces. Squeezing extra capacity through efficiency could delay or avoid having to build out new ORs, he added.<\/p>\n

UCH\u2019s OR block utilization \u2013 the share of actual OR time used of the total time allocated \u2013 is about 65 percent on a given day. Getting that number to a higher percentage could make a big difference, Hess said.<\/p>\n

\u201cIt means patients are getting elective surgery done in a more timely fashion, and it would be a huge win for docs,\u201d Hess said.<\/p>\n

Getting to that goal has been a challenge. Ashley Walsh, University of Colorado Hospital\u2019s perioperative business manager, serves as UCHealth\u2019s point person on the iQueue OR project. A big part of her job has been reporting OR utilization at UCH. That requires tapping into Epic electronic health record data analytics and reports, SharePoint cubes, web-based reports from the hospital\u2019s anesthesia partners and other inputs.<\/p>\n

\u201cWe go to many different places to get the numbers we want to see,\u201d Walsh said.<\/p>\n

The goal is to manage OR blocks \u2013 chunks of time allocated to particular surgeons for particular procedures \u2013 as efficiently as possible. It requires fitting OR blocks to procedure times (so that, for example, a two-hour surgery doesn\u2019t occupy a four-hour block), allocating blocks among the many ORs, and minimizing start-time delays as well as the time it takes to turn a room over between surgeries. It\u2019s complex puzzle even before taking into account surgeon preferences, surgeries that turn out to be more difficult than expected and a host of other surprises.<\/p>\n

Hess and Walsh saw the ability of LeanTaaS to help meet these challenges. But getting there has required plowing new ground. LeanTaaS hadn\u2019t yet tailored its software to ORs. With UCHealth as its pilot customer and partner in development, the company launched a project to develop \u201ciQueue for Operating Rooms\u201d in February. As of mid-May, iQueue for ORs was analyzing live UCHealth OR utilization data as part of the project\u2019s first phase, which centers on gathering and analyzing operational data.<\/p>\n

In the project\u2019s second phase, iQueue for ORs will start applying what it has \u201clearned\u201d about the hospital\u2019s OR operations and will recommend changes to OR block scheduling and other possible efficiency improvments. The third phase will add \u201cmobile block exchange,\u201d which will allow surgeons and administrators to release or add OR blocks on the fly using their mobile phones so that valuable surgical time isn\u2019t wasted.<\/p>\n

Rapid deployment
\n<\/strong><\/p>\n

It\u2019s all happening fast. The plan is to have mobile block exchange in place at UCH by the end of July. By about October, Hess and colleagues will know whether iQueue for ORs is making a difference; if it is, they\u2019ll assess the potential for rollout across other UCHealth hospitals, he said.<\/p>\n

From what Walsh has seen so far, it\u2019s a job made for LeanTaaS\u2019s machine learning.<\/p>\n

UCHealth\u2019s partnership with Silicon Valley firm LeanTaaS is symbiotic: LeanTaaS brings PhD-level expertise in machine learning and data analytics; UCHealth serves as both customer and partner in developing solutions that meet the needs of diverse health care settings.<\/figcaption><\/figure>\n

\u201cLeanTaaS turns around advanced analytic outputs in a week where it could take an in-house team a month or more to produce,\u201d she said.<\/p>\n

The benefits flow both ways, too. Walsh shares UCHealth subject-matter expertise, which informs iQueue for ORs.<\/p>\n

\u201cI\u2019m telling them what I do, what I need, and why I need it, and they\u2019re turning it around,\u201d she said. \u201cThey\u2019re taking it to the next degree \u2013 to where we\u2019ve wanted to take it.\u201d<\/p>\n

Peter Witt, MD<\/a>, a University of Colorado neurosurgeon, said he\u2019s looking forward to using iQueue for ORs.<\/p>\n

\u201cIt will allow for very immediate \u2013 almost \u2018live\u2019 \u2013 access to usage data related to my block time in the OR and will let me see if I am effective in using the allotted time,\u201d Witt said in an email.<\/p>\n

Assuming LeanTaaS can do for ORs what it has done for infusion centers, Hess would like to take it further yet. The next step is to apply machine learning to managing inpatient bed capacity. For example, based on analysis of scads of historical capacity data, Hess hopes LeanTaaS\u2019s systems can pinpoint ahead of time expected constraints in particular units that frequently blossom into broader capacity problems.<\/p>\n

Ambulatory clinics, radiology, pharmacy, the clinical lab \u2013 any high-demand hospital resource could benefit from the technology, Hess said.<\/p>\n

\u201cThe idea is to figure out what the constraints are going to be tomorrow or even two days from now so you can start moving levers today,\u201d he said.<\/p>\n","protected":false},"excerpt":{"rendered":"

UCHealth is using the technology at the heart of Google\u2019s self-driving cars and Netflix\u2019s recommendation engine to help maximize the use of chemotherapy infusion beds, operating room time and, soon, much more. The technology is called machine learning. It lets computers learn from vast data sets and uncover hidden patterns and insights as they do […]<\/p>\n","protected":false},"author":23,"featured_media":2728,"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":[168,293,263],"class_list":["post-4763","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-innovative-care","tag-chemotherapy","tag-lone-tree-surgery-center","tag-university-of-colorado-hospital"],"acf":[],"yoast_head":"\nUCHealth taps machine learning to boost capacity - UCHealth Today<\/title>\n<meta name=\"description\" content=\"UCHealth is using the technology at the heart of Google\u2019s self-driving cars and Netflix\u2019s recommendation engine to help maximize the use of chemotherapy infusion beds, operating room time and, soon, much more. 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