Hill-Rom Services, Inc.

Eugene G Urrutia

Britten J Pipher

Stephen R Embree

Lori Ann Zapfe

Matthew D Morgan

Matthew Mccormick Riordan

Timothy Joseph Receveur

Kirsten M Emmons

Kenzi L Mudge

Frederick Collin Davidson

Stacey A Fitzgibbons

Jotpreet Chahal

Brandon M Ayers

Susan Kayser

Johannes De Bie

Yuan Shi

Chiew Yuan Chung

Aziz Ali Bhai

Dan R Tallent

Craig M Meyerson

Eric D Agdeppa

Brian Lee Lawrence

Yongji Fu

Darren S Hudgins

Sherrod L Faulks

Ryan Terry

Jared Prickel

Karrie Browne

Karrie Schwencer

Christopher Keegan

Kristen Keaton Lightcap

Elaine Montambeau

Monitoring patients utilizing computer vision to analyze patient movements on or near a patient support apparatus such as a bed. Computer vision is employed to analyze video feed of patients to detect movements indicative of clinical parameters. Information generated by the computer vision processor can be recorded to a patient's electronic medical record. Such information can be used to make clinical assessments, diagnoses, or detect critical patient events requiring attention of caregivers. In instances where caregiver assistance is required, an alert can be communicated to one or more caregivers through a caregiver call system.

Machine vision to predict clinical patient parameters

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

A location system can be used to identify locations of assets in a clinical environment. An example location system receives, from a primary receiver, timing data indicating times at which multiple receivers including the primary receiver received a wireless signal from a tag. The multiple receivers may be located in the clinical environment. The example location system further identifies that the timing data includes a flag indicating that a user of the tag has requested assistance, identifies an identifier of the tag based on the timing data; and transmits, to a reporting system, a first message indicating an identifier of the tag and that the user of the tag has requested assistance.

Modular location engine for tracking the locations of assets in a clinical environment

An exit prediction system receives movement data, divides the movement data into segments of time, extracts features from each segment of time, and determines a pattern of movement from the extracted features. A patient exit from a patient support apparatus is predicted based on the determined pattern of movement.

Exit monitoring system for patient support apparatus

Automatically gathering and processing data relating to risk of patient falls. The data is analyzed to determine a level of risk each patient has for falling. Patient risk for falls is stratified and protocols are implemented to mitigate that risk. The system communicates instructions and alerts to caregivers to complete tasks or provide aid to patients. Updates to patient medical information and updates to best practices in fall risk management result in updates to patient risk stratifications. In turn, tasks and alerts are updated to reflect updates to patient risk stratifications.

Falls risk management

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.

A system for automatically assessing orthostatic hypotension for a patient supported on a patient support apparatus. The system receives position data identifying a first position of a patient supported on a patient support apparatus, and after a delay, receives vital signs data of the patient. The system receives position data identifying a second position of the patient supported on the patient support apparatus, and after a delay, receives vital signs data of the patient. The system determines an orthostatic hypotension assessment based on a difference in the vital signs data between the first and second positions. Based on the orthostatic hypotension assessment, the system modifies one or more conditions on the patient support apparatus to mitigate a risk for patient fall.

Automated orthostatic hypotension assessment

A system identifies a first position of a tag in a clinical environment based on first times at which first receivers received a first wireless signal from the tag. The system estimates a second position of the tag in the clinical environment based on second times at which second receivers received a second wireless signal from the tag. The system determines that a boundary is located between the first position and the second position, defines a path range around the first position of the tag based on an expected movement of the tag during a time interval between the first and second wireless signals, determines that the boundary lacks a door within the path range, adjusts the second position of the tag based on the boundary map, and transmits a message indicating that the tag is located at the adjusted position at the second time.

Intelligent location estimation for assets in clinical environments

A system can identify a first position of a tag in a clinical environment based on first times at which first receivers received a first wireless signal from the tag. The system can estimate a second position of the tag in the clinical environment based on second times at which second receivers received a second wireless signal from the tag. The system determines that a boundary is located between the first position and the second position, defines a path range around the first position of the tag based on an expected movement of the tag during a time interval between the first and second wireless signals, determines that the boundary lacks a door within the path range, adjusts the second position of the tag based on the boundary map, and transmits a message indicating that the tag is located at the adjusted position at the second time.

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Apex, NC, United States of America

Eugene G Urrutia