LeanTaaS to Showcase Impact of Predictive Analytics on Healthcare Operations at HIMSS18
Company President Sanjeev Agrawal to Speak on Using Data Science to Optimize Utilization of Operating Rooms.
- (1888PressRelease) February 22, 2018 - SANTA CLARA, Calif. - LeanTaaS, Inc., a Silicon Valley software innovator that increases patient access and transforms operational performance for healthcare providers, today announced that it will be exhibiting at the Healthcare Information and Management Systems Society (HIMSS) Annual Conference and Exhibition. Using lean principles, predictive analytics and machine learning, LeanTaaS captures and analyzes data to solve complex operational challenges and improves the patient experience for the country’s leading hospitals.
LeanTaaS will be exhibiting in booth 777 at the event to be held March 5-9, 2018, at the Venetian-Palazzo-Sands Expo Center in Las Vegas.
While virtually every hospital has implemented a version of an electronic health record that tracks the patient journey in their operations systems, most hospital administrators still use the tool in a “pull” manner; they start with something in mind and then look for the data. This causes problems when people aren’t clear on what to look for, or they get lost in reams of data available to them. The problem is not lack of data, it’s lack of insight into what data to look at — and when.
Health systems equipped with trend and anomaly detection powered by predictive analytics, machine learning and mobile technologies can transform “pull” into “push” and reclaim underutilized operating room time. Not only does pushing data out create better efficiency, it leads to enhanced communication and improved operations overall.
While the LeanTaaS approach to improving hospital operations is deeply rooted in lean principles, company president Sanjeev Agrawal is quick to point out the use of the “push not pull” construct in this case refers to how healthcare providers can be more proactive and less reactive to sharing key operational data with surgeons, nurse managers, schedulers, perioperative business leaders and administrative leaders.
“Since every minute of OR time can be worth hundreds of dollars in revenue, even a small improvement — as little as 3 to 5 percent — can increase revenue by half a million dollars per OR per year,” said Agrawal. “Instead of being overwhelmed by the enormity of data available, an email or SMS push notification can deliver the most relevant data at the right time. For example, surplus OR block time can be offered to surgeons to improve utilization rather than sitting unused.”
Agrawal will cover the topic in his HIMSS presentation, “Push Not Pull: Using Data Science to Improve OR Operations.” He will be joined by former UCHealth Perioperative Business Manager Ashley Walsh for the session to be held on Thursday, March 8, at 8:30 a.m. in Lando 4301.
The presentation also will include a close examination of how UCHealth deployed data science and mobile technologies to effect a cultural shift with surgeons and staff toward a data-driven block allocation and management solution that has increased revenue by over $400,000 per OR per year.
About LeanTaaS
LeanTaaS provides software solutions that combine lean principles, predictive analytics and machine learning to transform hospital and infusion center operations. More than 40 providers across the nation rely on the company’s iQueue cloud-based platform to increase patient access, decrease wait times, reduce healthcare delivery costs and improve revenues. LeanTaaS is based in Santa Clara, California. For more information about LeanTaaS, please visit www.leantaas.com and connect on Twitter/LeanTaaS, Facebook/LeanTaaS and LinkedIn/LeanTaaS.
LeanTaaS and iQueue are trademarks of LeanTaaS. All other brand names and product names are trademarks or registered trademarks of their respective companies.
Tags: LeanTaaS, iQueue, healthcare, HIMSS, UCHealth, operating rooms, predictive analytics, lean principles, data science, machine learning, infusion center, cancer center, hospitals
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