Hill-rom Services, Inc.

Matthew Mccormick Riordan

Eugene G Urrutia

Kirsten M Emmons

Lori Ann Zapfe

Yuan Shi

Chiew Yuan Chung

Timothy Joseph Receveur

Craig M Meyerson

Brian Lee Lawrence

Eric D Agdeppa

Yongji Fu

Stacey A Fitzgibbons

Jotpreet Chahal

Darren S Hudgins

Susan Kayser

Johannes De Bie

Kristen Keaton Lightcap

Karrie Browne

Ryan Terry

Jared Prickel

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.

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

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

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Apex, NC, United States of America

Matthew Mccormick Riordan