Hill-rom Services, Inc.

Susan Kayser

David Lance Ribble

Craig M Meyerson

Lori Ann Zapfe

Stacey A Fitzgibbons

Jotpreet Chahal

Gene J Wolfe

Eugene G Urrutia

Johannes De Bie

Mary L Pfeffer

Olivia Gubitose Boyce

Charles A Lachenbruch

Timothy Joseph Receveur

Kirsten M Emmons

Eric D Agdeppa

Brian Lee Lawrence

Neal Wiggermann

Frank E Sauser

Yongji Fu

Bradley T Smith

Brian L Lawrence

Kathryn R Smith

Darren S Hudgins

Sherrod L Faulks

Jie Zhou

Matthew Mccormick Riordan

Yuan Shi

Chiew Yuan Chung

Marion Le Gall

Jennifer Marie Rizzo

Nuno M Azeredo

Karrie Schwencer

Christopher Keegan

Elaine Montambeau

Ryan Terry

Jared Prickel

Lance Shapiro

Barry Brown

Terry Ryan

Eugene Urrutia

Matthew M Riordan

An example method includes capturing images using a camera and detecting a medical device in a first image among the images. A request is transmitted to the medical device. Based on transmitting the request, the example method includes determining that the medical device has output a chirp signal in a second image among the images. Based on the chirp signal, the method includes causing the medical device to perform an action by transmitting a control message to the medical device.

Image-based pairing and controlling of devices in a clinical environment

This disclosure is directed towards a patient management system for analyzing images of wounds and tracking the progression of wounds over time. In some examples, a computing device of the patient management system receives an image, and determines that the image depicts a wound. The computing device inputs the image into a machine-learned model trained to classify wounds, and receives, from the machine-learned model, a classification of the wound. The computing device may then display the classification of the wound in a user interface. Additionally, the patient management system may train a machine-learned model to classify wounds.

Wound healing analysis and tracking

An example method includes identifying images of an individual and determining, based on the images, a facial feature of the individual. The example method further includes determining a contextual feature of the individual. Based on the facial feature and the contextual feature, an identity of the individual is determined.

Enhanced image-based tracking in clinical environments

A microclimate management system including a person support surface including one or more sensors, a fluid supply device coupled to an inlet of the person support surface and configured to supply fluid to an outlet the person support surface, and a controller configured to detect an inlet condition value at the inlet of the person support surface, detect an outlet condition value at the outlet of the person support surface, determine a difference between the inlet condition value and the outlet condition value, and transmit a first alert in response to determining that a rate of change of the difference between the inlet condition value and the outlet condition value is below a lower condition threshold.

Microclimate management systems for detecting surface performance

Apparatus for assessing medical risks of a patient includes an analytics engine and equipment that provides data to the analytics engine. The analytics engine analyzes the data from the equipment to determine a sepsis risk score, a falls risk score, and a pressure injury score. The apparatus further include displays that are communicatively coupled to the analytics engine and that display the sepsis, falls, and pressure injury risk scores. The displays include a status board display located at a master nurse station, an in-room display provided by a room station of a nurse call system, an electronic medical records (EMR) display of an EMR computer, and a mobile device display of a mobile device of a caregiver assigned to the patient.

Patient risk assessment based on data from multiple sources in a healthcare facility

Systems include a plurality of sensors coupled to a person support apparatus, at least one moisture sensor configured to sense a moisture level between the person and the support surface, and at least one computing device coupled to the plurality of sensors coupled to the person support apparatus and the at least one moisture sensor. The at least one computing device receives data from the plurality of sensors coupled to the person support apparatus and the at least one moisture sensor, obtains data from an electronic medical record associated with the person supported by the person support apparatus, calculates a pressure injury score indicative of a likelihood that the person will develop a pressure injury based on the data from the plurality of sensors, the at least one moisture sensor, and the electronic medical record, and alters a treatment plan for the person based on the calculated pressure injury score.

Systems and methods for determining an objective pressure injury score and altering a treatment plan based on the same

Apparatus for assessing medical risks of a patient includes an analytics engine and equipment that provides data to the analytics engine. The equipment includes a patient support apparatus such as a patient bed, a nurse call computer, a physiological monitor, a patient lift, a locating computer of a locating system, and an incontinence detection pad. The analytics engine analyzes the data from the equipment to determine a sepsis risk score, a falls risk score, and a pressure injury score. The apparatus further include displays that are communicatively coupled to the analytics engine and that display the sepsis, falls, and pressure injury risk scores. The displays include a status board display located at a master nurse station, an in-room display provided by a room station of a nurse call system, an electronic medical records (EMR) display of an EMR computer, and a mobile device display of a mobile device of a caregiver assigned to the patient.

Growing community of inventors

Batesville, IN, United States of America

Susan Kayser