Abstract: 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.

Abstract: A vital signs monitor for detecting sepsis in a patient includes a housing. At least one vital sign sensor is coupled to the housing and is configured to obtain at least one vital sign from a patient. A display is positioned on an outer surface of the housing. A controller is embedded in the housing and is operable to receive a vital sign signal from the at least one vital sign sensor.

Abstract: 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.

Abstract: 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.

Abstract: A patient support apparatus comprises a plurality of load cells, a frame supported on the load cells, a mattress, a plurality of air pressure sensors, and a control system. The mattress includes a plurality of inflatable zones positioned on the frame, the mattress and frame cooperating to direct any patient load through the mattress and frame to the load cells. Each of the plurality of air pressure sensors measures the pressure in a respective inflatable zone of the mattress. The control system includes a controller operable to receive a separate signal from each of the plurality of load cells and each of the plurality of air pressure sensors and process the signals to identify movement data and communicate the movement data to a caregiver.

Abstract: A patient support apparatus comprises a plurality of load cells, a frame supported on the load cells, a mattress, a plurality of air pressure sensors, and a control system. The mattress includes a plurality of inflatable zones positioned on the frame, the mattress and frame cooperating to direct any patient load through the mattress and frame to the load cells. Each of the plurality of air pressure sensors measures the pressure in a respective inflatable zone of the mattress. The control system includes a controller operable to receive a separate signal from each of the plurality of load cells and each of the plurality of air pressure sensors and process the signals to identify motion of the patient. A motion classifier assesses major and minor motions based on the amplitude and frequency of patient movement and determines a mobility score for the patient.

Abstract: An example patient scheduling system can include: at least one processor; and system memory encoding instructions which, when executed by the at least one processor, cause the system to: provide a schedule for a patient, the schedule defining care items associated with the patient; allow for acceptance of a patient care activity; and notify a caregiver and the patient of changes to the patient care activity.

Abstract: 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.

Abstract: 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.

Abstract: A mobility management system receives movement data from sensors that detect movements of a patient resting in a patient support apparatus and movement data from one or more mobility detection devices that detect movement of the patient when the patient exits the patient support apparatus. The system determines a mobility status based on at least the movement data received from the patient support apparatus and the one or more mobility detection devices, and adjusts a care protocol for the patient based on the mobility status.

Abstract: 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.

Abstract: Systems and methods for predicting patient bed exit utilize motion sensors to determine when a patient is removing covers from the patient's bed. Sensors are used to detect movement of a patient's feet, the covers, or both. The sensors could be one or more of RFID sensors (tags), infrared motion detection, video motion detection, accelerometers, and load sensors in the bed. An algorithm generated from training data obtained in controlled experiments is used to analyze the sensor information to determine when patient movements indicate that a blanket or covers are being removed by a patient in a bed. When such patient movements are detected, an alert can be issued to caregivers through a call system so that the caregiver is notified that a patient at risk for falling needs assistance in getting out of bed.

Abstract: Automatically evaluating a level of mobility of a patient using a machine-learning model and load sensors integrated into a patient support system. A machine-learning model implemented on a computing system analyzes the load sensor data to determine whether the patient has difficulty standing up. This automated analysis can replace existing mobility assessments that were previously performed by caregivers. The mobility assessment data generated by the computing system can be recorded in the patient's electronic medical record (EMR) and utilized as a factor in determining patient fall risk.

Abstract: A sepsis monitoring system determines an initial sepsis risk assessment score, and automatically and continuously updates the sepsis risk assessment score using a sepsis risk assessment model that receives vital signs data, electronic medical records data, and admissions data to continuously update the sepsis risk assessment score. The system determines whether the patient is likely to develop sepsis based on the updated sepsis risk assessment score, and in response to determining that the patient is likely to develop sepsis, generates a notification to drive an early intervention by one or more caregivers.

Abstract: 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.

Abstract: 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.

Abstract: A fall reporting system gathers, extracts, and compiles data from multiple electronic sources to generate reports regarding patient falls. Individual patient investigation reports are automatically generated when a device detects a patient fall. Additionally, the system can aggregate data from multiple patients to provide facility investigation reports for a unit, department, or entire healthcare facility. Reports can be customized to filter and visualize falls-related data. The system can be cloud based or part of a local area network. The system accessible via a webportal that provides a single sign-on configuration application.

Abstract: 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.

Abstract: A sepsis automated reporting system generate a sepsis report that is accessible on one or more workstations. The sepsis report includes a display area that is customizable based on a selection of one or more parameters in a parameter configuration box. The customizable display area and the parameter configuration box are both displayed in the sepsis report, and the display area is automatically updated based on the selection of the one or more parameters.

Abstract: An example system for estimating a likelihood of a fall for a patient includes: at least one processor; and memory encoding instructions which, when executed by the at least one processor, cause the at least one processor to: access first data associated with acute action of and environment for the patient; access second data associated with attributes of the patient over time; calculate a composite fall score based upon the first data and the second data.