BED EXIT PREDICTION BASED ON PATIENT BEHAVIOR PATTERNS

A system for inferring a patient's future behavior may include a patient support apparatus. A data acquisition system may track data related to patient bed exits from the patient support apparatus. A controller may be in communication with the patient support apparatus. The controller may include a processor and a non-transitory memory device. The memory device may include instructions that, when executed by the processor, acquire data related to patient bed exits.

TECHNICAL FIELD

The present disclosure is related to a patient support apparatus that can predict patient bed exits. More specifically, the present disclosure is related to a patient support apparatus that includes a control system that can predict patient bed exit, displays information related to the exit on a user interface, and alerts the caregiver.

BACKGROUND

The mobility of a person supported on a patient support apparatus is of interest to caregivers in assessing the risk of the patient making unassisted bed exits. When making an unassisted bed exit, the patient may be at risk for falling and subsequent injury. Many devices, including bed exit alarms, real-time locating systems, and cameras may be used to monitor when a patient exits the bed.

SUMMARY

In a first aspect of the disclosed embodiments, a method for inferring a patient's future behavior may include acquiring data related to patient bed exits. The method may also include analyzing the data related to patient bed exits to detect patterns in patient bed exit behavior. The method may also include building a customized schedule of patient bed exit behavior to predicts future patient bed exits. The method may also include notifying a caregiver when a future patient bed exit is to occur.

In some embodiments of the first aspect, the method may include acquiring data related to patient bed exits includes acquiring data from a sensor in a patient support apparatus. The method may also include acquiring data related to patient bed exits includes acquiring data from a real time locating system. The method may also include acquiring data related to patient bed exits includes acquiring data from a camera in a patient room. The method may also include acquiring data related to patient bed exits includes acquiring data related to the patient's medical schedule. The method may also include acquiring data related to patient bed exits includes acquiring data related to the patient's mealtime schedule. The method may also include acquiring data related to patient bed exits includes acquiring data related to the patient's visiting hours schedule.

It may be desired in the first aspect that the method includes combining historical data from similar patients to the data related to patient bed exits to determine a model that predicts future patient bed exits. The method may also include notifying a caregiver when a future patient bed exit is to occur further comprises notifying a caregiver a predetermined time before the future patient bed exit is to occur. The method may also include comparing a patient's current behavior to a patient's predicted behavior. The method may also include updating the customized schedule based on differences between a patient's current behavior and a patient's predicted behavior. The method may also include notifying a caregiver when a future patient bed exit is to occur further comprises notifying the caregiver before the patient wakes up. The method may also include notifying a caregiver when a future patient bed exit is to occur further comprises notifying the caregiver before the patient uses the restroom.

In a second aspect of the disclosed embodiments, a system for inferring a patient's future behavior may include a patient support apparatus. A data acquisition system may track data related to patient bed exits from the patient support apparatus. A controller may be in communication with the patient support apparatus. The controller may include a processor and a non-transitory memory device. The memory device may include instructions that, when executed by the processor, acquire data related to patient bed exits, analyze the data related to patient bed exits to detect patterns in patient bed exit behavior, build a customized schedule of patient bed exit behavior to predicts future patient bed exits, and notify a caregiver when a future patient bed exit is to occur.

In some embodiments of the second aspect, the data acquisition system may include a sensor in a patient support apparatus. The data acquisition system may include a real time locating system. The data acquisition system may include a camera in a patient room. The data related to patient bed exits includes data related to the patient's medical schedule. The data related to patient bed exits may include data related to the patient's mealtime schedule. The data related to patient bed exits may include data related to the patient's visiting hours schedule.

It may be desired in the second aspect that historical data from similar patients is compared to the data related to patient bed exits to determine a model that predicts future patient bed exits. The caregiver may be notified a predetermined time before the future patient bed exit is to occur. A patient's current behavior may be compared to a patient's predicted behavior to update the customized schedule. The caregiver may be notified before the patient wakes up. The caregiver may be notified before the patient uses the restroom.

DETAILED DESCRIPTION

Referring toFIG. 1, a system100for a healthcare facility includes a patient support apparatus122, such as a hospital bed that includes a patient support structure such as a frame that supports a surface or mattress. Thus, according to this disclosure a bed frame, a mattress or both are examples of things considered to be within the scope of the term “patient support structure.” However, this disclosure is applicable to other types of patient support apparatuses and other patient support structures, including other types of beds, surgical tables, examination tables, stretchers, and the like.

The patient support apparatus122includes a plurality of sensors312that are used to determine the patient's mobility score. In some embodiments, these sensors may be load cells. In some embodiments, these sensors maybe air pressure bladders. In some embodiments, these sensors may be contact sensors. In some embodiments, these sensors maybe force sensing resistors. In some embodiments, a patient support apparatus may have multiple such sensors. The sensors312are utilized to detect bed exit events by monitoring a pressure on the patient support apparatus122and determining when the pressure is removed, which is indicative of the patient exiting the bed. The sensors312may also be utilized to monitor when a patient is positioned on a side of the patient support apparatus122and at risk for falling.

As shown inFIG. 1, in one embodiment, the patient support apparatus122, includes communication circuitry300, a controller302, and an interface324. The controller302is capable of controlling operational functionality of the patient support apparatus122and/or interpreting data signals from the various sensors312. The communication circuitry300is capable of establishing connections and facilitating communications to and from the patient support apparatus122. The controller302is further configured to provide, or relay, status indications to a remote location, such as the nurse call system, via the communication circuitry300. The status indications may include any type of indication of a component, or a patient relative to a component, of the patient support apparatus122. The communication circuitry300may be embodied as any communication circuit, device, or collection thereof, capable of enabling communications over a network116between the patient support apparatus122and a hospital information system102. The communication circuitry300may be configured to use any one or more communication technologies (e.g., wired or wireless communications) and associated protocols (e.g., Ethernet, Bluetooth®, Zigbee®, Wi-Fi®, WiMAX, etc.) to effect such communication.

In some embodiments, the patient support apparatus is connected to a bedside/patient support apparatus connector350via the communications circuitry300. The bedside/patient support apparatus connector may have a computing device and server to connect to the network116.

The controller302is connected to various sensors capable of being monitored and interpreted by the controller302, and various actuators capable of being controlled by the controller302. The controller302is configured to receive data (i.e., electrical signals) from the various sensors and components of the patient support apparatus122, and control the operation of the components of the patient support apparatus122relative to the received data, as is known in the art. To do so, the controller302includes a number of electronic components commonly associated with controllers utilized in the control of electromechanical systems. For example, the controller302may include, amongst other components customarily included in such devices, a processor304and a memory device306. The memory device306may be, for example, a programmable read-only memory device (“PROM”) including erasable PROM's (EPROM's or EEPROM's). In use, the memory device306is capable of storing, amongst other things, instructions in the form of, for example, a software routine (or routines) which, when executed by the processor304, allow the controller302to control operation of the features of the patient support apparatus122.

The system100includes a hospital information system102of one or more hospitals communicatively coupled over a network116to various care assets, such as a patient support apparatus122. To facilitate the transfer of data and other network communications across the hospital information system102, the hospital information system102includes a number of computing devices104. Each of the computing devices104may be embodied as any type of computation or computer device capable of performing the functions described herein, including, without limitation, a server (e.g., stand-alone, rack-mounted, blade, etc.), a network appliance (e.g., physical or virtual), a high-performance computing device, a web appliance, a distributed computing system, a computer, a processor-based system, a multiprocessor system, a smartphone, a tablet computer, a laptop computer, a notebook computer, and/or a mobile computing device. The illustrative computing device104ofFIG. 2includes a processor106and a memory110. Of course, the computing device104may include additional and/or alternative components, such as those commonly found in a computer (e.g., various input/output devices), in other embodiments. Additionally, in some embodiments, one or more of the illustrative components may be incorporated in, or otherwise form a portion of, another component. For example, the memory110, or portions thereof, may be incorporated in the processor106in some embodiments.

The processor106may be embodied as any type of processor capable of performing the functions described herein. For example, the processor106may be embodied as a single or multi-core processor(s), digital signal processor, microcontroller, or other processor or processing/controlling circuit. The memory110may be embodied as any type of volatile or non-volatile memory or data storage capable of performing the functions described herein. In operation, the memory110may store various data and software used during operation of the computing device104such as operating systems, applications, programs, libraries, and drivers. The memory110is communicatively coupled to the processor106via a I/O subsystem, which may be embodied as circuitry and/or components to facilitate input/output operations with the processor106, the memory110, and other components of the computing device104. For example, the I/O subsystem may be embodied as, or otherwise include, memory controller hubs, input/output control hubs, firmware devices, communication links (i.e., point-to-point links, bus links, wires, cables, light guides, printed circuit board traces, etc.) and/or other components and subsystems to facilitate the input/output operations.

A data storage device112may be embodied as any type of device or devices configured for short-term or long-term storage of data such as, for example, memory devices and circuits, memory cards, hard disk drives, solid-state drives, or other data storage devices. In use, as described below, the data storage device112and/or the memory110may store security monitoring policies, configuration policies, or other, similar data. Communication circuitry114may be embodied as any communication circuit, device, or collection thereof, capable of enabling communications between the computing devices104and/or between one of the computing devices104and the patient support apparatus122. The communication circuitry114may be configured to use any one or more communication technology (e.g., wired or wireless communications) and associated protocols (e.g., Ethernet, Bluetooth®, Wi-Fi®, WiMAX, etc.) to effect such communication.

The computing devices104of the hospital information system102may be configured into separate subsystems for managing data and coordinating communications throughout the hospital information system102.

The network116may be embodied as any type of wired or wireless communication network, including cellular networks (e.g., Global System for Mobile Communications (GSM), 3G, Long Term Evolution (LTE), Worldwide Interoperability for Microwave Access (WiMAX), etc.), digital subscriber line (DSL) networks, cable networks (e.g., coaxial networks, fiber networks, etc.), telephony networks, local area networks (LANs) or wide area networks (WANs), global networks (e.g., the Internet), or any combination thereof. As previously described, at least a portion the patient support apparatuses122may be in communication with the hospital information system102over the network116. Accordingly, the network116may include any number of network devices (e.g., access points, routers, switches, servers, etc.) as needed to facilitate communications between the hospital information system102and the patient support apparatus122.

Referring still toFIG. 1, a real-time locating system (RTLS)152is provided to track the whereabouts of the patient. RTLS152includes a patient tag154worn by the patient and in communication with a multitude of transceivers156. Transceivers156may be dispersed throughout the patient room. Tag154and transceiver156each include a housing that contains associated circuitry. The circuitry of tag154and transceiver156includes for example a processor such as a microprocessor or microcontroller or the like, memory for storing software, and communications circuitry including a transmitter, a receiver and at least one antenna, for example. Tag154also includes structure to enable attachment to the patient. For example, tag154may include a necklace so that a caregiver can wear the tag154around their neck or may include a clip so that the caregiver can attach the tag154to their clothing. The tag154may include a wristband so that the tag154can be worn on the wrists of the associated patients. Transceivers156each include mounting hardware, such as brackets or plates or the like, in some embodiments, to permit the transceivers156to be mounted at fixed locations in the patient room with fasteners such as screws or the like.

Transceiver156communicates wirelessly with tag154using radio frequency (RF). According to this disclosure, system152operates as a high-accuracy locating system which is able to determine the location of the tag154within one foot (30.48 cm) or less of the tag's actual location. System152is operable to determine the location of the tag154in 2-dimensional space. One example of a high-accuracy locating system contemplated by this disclosure is an ultra-wideband (UWB) locating system. UWB locating systems operate within the 3.1 gigahertz (GHz) to 10.6 GHz frequency range. Accordingly, the tag154is tracked by the RTLS152to monitor when the patient has left the patient support apparatus122. Data related to movement of the patient is transmitted over the network116to the hospital information system102.

Additionally, cameras150may be positioned in the patient room and in communication with the network116. As such, the cameras150are utilized to monitor when the patient exits the beds. That is time-stamped video of the patient is captured by the camera150and transmitted to the hospital information system102, where the video is monitored for bed exits. In some embodiments, the processor106may operate instructions to track bed exit behavior in the video feeds. Therefore, data from the sensors312, the RTLS152, and the cameras150may all be utilized or individually utilized to track when the patient exits the bed. As set forth below, this data may be used to determine a bed exit schedule for the patient.

In the illustrated embodiment, the processor106of the hospital information system102can access information gathered through the hospital information system from the memory110. In some embodiments, the processor can access the current patient's daily routine. In some embodiments, the processor can access the current patient's medical schedule. In some embodiments, the processor can access the current patient's visiting hours. In some embodiments, the processor can access the historical behavior of similar patients. The processor106can access mobility information of the current patient from the memory110of the patient support apparatus the memory122over the network166. The processor106in a computing device104of the hospital information system102uses the current patient's information, the patient's mobility score, and the historical information from similar patients to perform analysis to determine the patient's behavior patterns. In some embodiments, the processor304of the patient support device122can access all relevant information from the memory110of the computing device104which is a part of the hospital information system102over the network116to do the analysis to predict patient behavior. In some embodiments, the patient support device is connected to a bedside connector that accesses the relevant information for patient behavior analysis over the network116.

In the illustrated embodimentFIG. 2, a flowchart shows the different steps performed to determine a patient behavior. The processor304of a patient support apparatus122such as bed, can access historical data of similar patients (block502) from memory306or from the memory110over a wireless network116at step500. The current patient's information such as medical schedule, routine, visiting hours (block510), and mobility information (bock504) can be accessed from memory306or from memory110over a wireless network116at step500. At step512, the processor304of a patient support apparatus122accesses memory306or the memory110over a wireless network116to decide it is the first assessment of the day. If it is the first assessment, steps514,516and520are executed prior to step522, else step518is executed prior to step522. At step518, the processor304accesses the model developed for the current patient that is stored in memory306or from memory110over a wireless network116. At step514, weighted sum is used by the processor306to determine a probability chart for the current patient's behavior based on the information obtained in step500. An illustrative probability chart600, is shown inFIG. 3. At step516, a model to predict current patient's behavior is developed using machine learning (ML) techniques such as supervised learning or reinforcement learning. This model can be used to predict the current patient's behavior. In some embodiments, this behavior is bed exits. At step520, the model developed is stored in memory306or in memory110over a wireless network116or in both. At step522, the model developed is used to make predictions about the current patient's bed exit behavior. If the patient is predicted to exit the bed, the prediction is communicated to the caregiver over the wireless network116at step524and added to the historical database at step526. If the patient is not predicted to exit the bed, caregiver is not contacted and the information is added to the historical database at step522. The processor can access mobility and scheduling information of the patient from various patient support apparatuses112controller302over the wireless network116. In another embodiment, all the processing of information is done by the processor106in the computing device104which is a part of the hospital information system102. The memory processor106can access all relevant information from memory110and memory306over the network116.

The current patient's potential bed exits are monitored on Day 1 of the current patient's stay in the hospital. Any changes to the initial assumptions based on the current patients schedule and the historical data of similar patients is monitored. Machine learning methods such as supervised learning or reinforcement learning is used to update the probability of bed exit and update the model making predictions. This builds a more accurate characterization of the patient's behavior. The refined model is used as the starting point for predictive events on Day 2. This process is repeated each day that the current patient is in the hospital room.

In some embodiments, patient diagnosis is entered as an input called patient condition to the model at the user interface324on Day 1. Diagnosis is used to predict how the model will change over time. For example. A less critical diagnosis would expect the patient to become more mobile over time, a more critical diagnosis may expect the patient to deteriorate over time. The patient condition is used to update the probability of bed exit and update the model making predictions each day. This builds a more accurate characterization of the patient's behavior. The refined model is used as the starting point for predictive events on Day 2. This process is repeated each day that the current patient is in the hospital room. The current patient's data is integrated into historical data for use with next similar category patient and stored in the memory306of the patient support apparatus and also transmitted to the hospital information system102to be stored in the memory110and used by the processor106.

The analysis done is used to provide alerts to the caregivers based on predicted events. Such alerts are proactive and planned care is provided to the patients based on the probability of the events that are likely to occur. These alerts are organized to reduce the cognitive burden by informing the caregivers about the most active time and are timed to reduce the occurrence of fall events. Such alerts are timed to improve patient safety and potential hospital liability and to increase staff effectiveness.

Using the data, the system can observe and predict future bed exit events to alert caregivers before the event occurs. That is, the system uses the data to track patient movement throughout the patient's stay in the healthcare facility. The system pinpoints the patient's position in the patient room and determines what time the patient is typically in bed, when the patient uses the restroom, when the patient eats lunch, and when the patient generally is not in the patient bed. These behavioral patterns are utilized to predict when the patient may exit the bed. For example, the system may determine that the patient generally wakes up at the same time each day and build a customized rounding schedule to check on the patient 10-20 minutes before they usually wake up. In another example, the system may determine that the patient uses the restroom an average of 60 minutes after eating and proactively alert a caregiver that the patient may need assistance after eating. In yet another example, the system may determine that the patient exits the patient bed for an average of two hours. Using this data, caregivers may be alerted to check on the patient after a predetermined time.

Although certain illustrative embodiments have been described in detail above, variations and modifications exist within the scope and spirit of this disclosure as described and as defined in the following claims. The drawings are provided to facilitate understanding of the disclosure, and may depict a limited number of elements for ease of explanation. Except as may be otherwise noted in this disclosure, no limits on the scope of patentable subject matter are intended to be implied by the drawings.