Mednition Introduces KATE

Machine Learning Startup to Debut Healthcare Industry’s First Real-Time, AI-Powered Triage Solution for Emergency Clinicians at Emergency Nursing 2019 Conference

Mednition, makers of machine learning-powered software solutions for healthcare, introduced KATE  the first real-time decision support solution designed specifically to help emergency nurses increase the accuracy of their triage decisions, improve quality of care and save lives.

“I’ve spent my entire nursing career in emergency care, and I’m passionate about making sure clinicians have the best tools, knowledge and support possible to be the best they can be”

Already in full commercial use by emergency nurses working in the high-volume emergency department at Adventist Health White Memorial (AHWM) in downtown Los Angeles, KATE will be demonstrated for the first time publicly at Emergency Nursing 2019. Dedicated to defining the future of emergency nursing, the conference is organized by the Emergency Nurses Association (ENA) and will be held in Austin at the Austin Convention Center, Sept. 29 – Oct. 2.

“I’ve spent my entire nursing career in emergency care, and I’m passionate about making sure clinicians have the best tools, knowledge and support possible to be the best they can be,” said Deena Brecher, Head of Patient Care, Mednition. “Everyone at Mednition shares that passion. That’s why I joined this company and why we created KATE. We’re committed to providing clinically accurate support to the entire healthcare team, enabling them to make the right decision for the right patient at the right time, decreasing preventable errors along the way and improving the patient experience.”

Read More: Datameer X Accelerates Machine Learning Analytics and Increases Model Accuracy

Past president of the ENA and former Clinical Director of Emergency Services at Cincinnati Children’s Hospital, Brecher noted the demands on emergency nurses are intense. More than 145 million people are treated annually in emergency departments nationwide. The use of those departments has increased 35% in the past 20 years while the number of them has shrunk by about 11%, according to the Centers for Disease Control and Prevention.

Designed with emergency nurses in mind, KATE was developed by clinicians for clinicians. The machine learning-powered SaaS solution integrates easily into popular electronic health records (EHR) systems and existing triage processes. It is the only solution architected to read, extract and understand the entirety of EHR, recognize potential under and over triage acuity assignment, and communicate with nurses in real time. Underlying KATE is the Mednition Clinical Data Engine of more than 10 billion de-identified patient clinical data points, providing deep clinical insight for every patient.

“We have first-hand evidence that machine learning is improving healthcare delivery and saving lives,” said Mara Bryant, Operations Executive at Adventist Health White Memorial. “Using KATE, we have documented increased clinical accuracy, patient safety and operational efficiency, all while using the same processes and our existing EHR systems. Our nurses see KATE as a critical new machine learning-powered solution, like an expert advisor who increases their accuracy, ensures patients receive the right care at the right time, and helps calm the chaos frequently associated with triage.”

Read More: Velodyne Lidar Announces Second Annual World Safety Summit On Autonomous Technology

Specific highlights of the hospital’s use of Mednition KATE include:

– Identifying High Risk Patients

• 500 high risk patients avoided the waiting room

– Increased timeliness of care

• 2.23 hour ED LOS decrease for patients admitted to the ICU with sepsis

– Improving Patient Flow

• 250 patients were redirected to fast track

Adventist Health White Memorial partnered with Mednition in late 2016 to research machine learning in support of clinical decision making. KATE was deployed in December 2018 when the study results showed the potential to improve triage accuracy by 26.9% for every patient presenting at their emergency department and up to 93.2% for high acuity patients. This study and its findings are currently in peer-review and can be accessed on the Mednition website.

“We’ve evolved the collaboration between Mednition and our emergency department from the initial study looking at triage assessment, to improving emergency clinicians’ ability to perform triage, to now showing outstanding outcomes and the impact on patients with objective data when triage is assigned more accurately at the beginning of a patient’s visit,” added Bryant.

Along with improving patient care, Mednition notes that KATE has had a positive impact on nurse and provider communications, invigorated deeper critical reasoning around difficult triage decisions, and helped improve EHR documentation. And with the Mednition Clinical Data Engine, hospital clinical leadership have a research grade platform to view all clinical and operational data in any way they need. AHWM used the platform to model the impacts of a proposed new sepsis protocol retrospectively on over 30,000 patients and forecasted clinical and operational impacts before deployment.

Read More: Bitcoin’s New Normal Bottom Is $10,000: DeVere CEO