Hill-rom Services, Inc.

Welch Allyn, Inc.

Stacey A Fitzgibbons

Craig M Meyerson

Jotpreet Chahal

Lori Ann Zapfe

David Lance Ribble

Steven D Baker

Gene J Wolfe

David Edward Quinn

Charles A Lachenbruch

Eric R Meyer

Kirsten M Emmons

Michael Scott Hood

Yue Yun Wang

Allen R Hart

Gregory J Shannon

Eugene G Urrutia

Susan Kayser

Johannes De Bie

Heather Whitt

Brian Lee Lawrence

Eric D Agdeppa

Yongji Fu

Eric P Jensen

David L Kellner

Darren S Hudgins

Sherrod L Faulks

Christopher L Long

Yuan Shi

WonKyung McSweeney

Matthew Mccormick Riordan

Heather Dawn Kooiker

Ching Yue Yeung

Chiew Yuan Chung

Thomas A Gurgol

Rebecca Quilty-Koval

Christopher Keegan

Elaine Montambeau

Ryan Terry

Jared Prickel

Karrie Schwencer

Matthew M Riordan

Eugene Urrutia

Terry Ryan

Brian L Lawrence

This disclosure is directed towards a patient management system for selectively storing and/or sharing patient data. A wearable device may sense a condition associated with the wearer of the wearable device, and store values based on the condition for a period of time. The wearable device may determine that the wearable device is within range of another device, such that the wearable device can communicate with the other device, and output the values to the other device. Additionally, a computing device of the patient management system may receive, from a wearable device, first data associated with a condition of a wearer of the wearable device. The computing device may also receive second data associated with the condition from a sensing device. The computing device may determine a context of the wearer based on comparing the first and second data, and control output of an alert based on the context.

Patient management based on sensed inputs

A system for capturing one or more eye images includes an eye imaging device having a camera configured to capture the eye images. The system determines a workflow for capturing the eye images using the eye imaging device. The workflow is determined based on a risk score for a given patient. The system performs the workflow on the eye imaging device to capture the eye images, conducts a microvascular assessment based on the captured eye images, and adjusts the risk score based on the microvascular assessment.

Microvascular assessment using eye imaging device

A device for monitoring a physiological variable determines a starting value for a self-adjusting alarm limit based on an abnormal state of the physiological variable. The device determines a new value for the self-adjusting alarm limit from physiological data values received during a time window. When the new value for the self-adjusting alarm limit moves in a targeted direction, the device resets the self-adjusting alarm limit at the new value.

Continuous patient monitoring

One or more radar sensors can be used to monitor patients in a variety of different environments and embodiments. In one embodiment, radar sensors can be used to monitor a patient's breathing, including monitoring of tidal volume, chest expansion distance, breathing rate, etc. In another embodiment, a patient position can be monitored in a patient bed, which can be used as feedback for control of bladders of a patient bed. Additional embodiments are described herein.

Radar sensors for patient monitoring

A sensor device includes a housing defining a cavity, an inlet to receive fluid pumped from an instrument device, an outlet to return the fluid to a fluid reservoir, and a fluid channel defined inside the cavity between the inlet and the outlet. A heat pump is mounted inside the cavity, and has a side surface thermally coupled to the fluid channel and an opposite side surface thermally coupled to a plate. The heat pump is configured to induce a temperature change. A sensor unit is aligned with an aperture in the plate and includes an optical component and a thermal component. The optical component configured to measure a vascular endothelial response from the induced temperature change.

Sepsis detection and monitoring

An example system for microvascular assessment of a patient can include: a fundus imaging device including: a camera configured to capture one or more images of an eye of the patient; and at least one light source; and a microvascular assessment computing device including: a processor; and memory encoding instructions which, when executed by the processor, cause the system to: activate the light source to direct light at a fundus of the eye of the patient; capture, with the camera, one or more images of the fundus of the patient, the fundus comprising a plurality of blood vessels; and analyze with the microvascular assessment computing device, the one or more images to determine a microvasculature health index for the patient.

Fundus imaging for microvascular assessment

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

Respiration detection using radar

A patient support apparatus includes a sensor capable of detecting vital signs and includes structures for monitoring the vital signs and providing local and/or remote indications to caregivers if the vital signs fall outside of acceptable limits.

Patient support apparatus having vital signs and sepsis display apparatus

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.

Various methods, systems, and devices relate to identifying a sepsis risk of an individual based on a dwell time of an invasive device in the individual. An example method includes receiving, from multiple transceivers, location data indicating first signals transmitted between the multiple transceivers and a tag attached to the invasive device. A location of the invasive device can be identified based on the location data. The dwell time can be determined based on the location. In certain examples, a second signal indicating the sepsis risk can be generated.

Assessing sepsis risk based on dwell times of invasive devices

Growing community of inventors

DeWitt, NY, United States of America

Stacey A Fitzgibbons