MEDICAL DEVICE DIAGNOSTICS AND ALERTING

Methods and systems for medical device diagnostics and reporting. One system including an electronic processor configured to receive a new medical device dataset associated with a medical device. The electronic processor is also configured to determine an operation classification of the medical device using a model utilizing training information. The training information includes a plurality of archived medical device datasets and an associated operation classification for each of the plurality of archived medical device datasets. The electronic processor is also configured determine an operation status of the medical device based on the operation classification. The electronic processor is also configured to generate and provide a notification based on at least the operation status of the medical device.

FIELD

Embodiments described herein relate to medical device diagnostics and alerting, and, more particularly, to diagnosing whether a medical device is functioning properly or is subject to a cyber-attack.

BACKGROUND

Biomedical devices, such as pacemakers, insulin pumps, health monitors, cardiac defibrillators, spinal cord neurostimulators, transcutaneous electrical nerve simulators, and the like, are generally configured to monitor a health condition of an associated user, perform an operation associated with a health condition of the associated user, or a combination thereof. These biomedical devices may be controlled remotely. Therefore, these biomedical devices can be subject to various cyber-attacks. Accordingly, when patients are in jeopardy, it is difficult to determine whether cyber-attacks against the patient's life-critical biomedical devices are involved.

SUMMARY

Embodiments described herein relate to methods and systems of medical device diagnostics and alerting for determining whether a medical device is functioning properly or is subject to a cyber-attack (or had been the subject of a cyber-attack). Accordingly, embodiments described herein enable first responders and other medical professionals to diagnose implantable or wearable medical devices to determine whether a device has been or is malfunctioning such that it may be the subject of a cyber-attack. Embodiments collect medical device datasets, logs, and alerts from medical devices and translates nuanced cyber-factors included in the data into actionable information or instructions for clinical users.

Accordingly, embodiments described herein address the problem that when patients are in jeopardy, current approaches or systems cannot determine whether a device malfunction or cyber-attack against the patient's life-critical biomedical device(s) was involved. As one example, an EMT arrives at the scene of a patient having a cardiac event. The standard procedure may be to administer CPR. The EMT is alerted that the patient may have a pacemaker implanted on their person. However, utilizing the methods and systems described herein, the EMT may scan the implanted device to determine whether the device was the subject of a malfunction or cyber-attack. Therefore, the embodiments described herein enable medical care providers, first responders (for example, police, fire, EMT, and the like), and the like to utilize the systems and methods described herein to interface with medical devices (either external or implantable) to ascertain the overall health and functionality of the medical device, such that a device malfunction or a malicious actor impacting the devices through cyber means may be detected or ruled out.

Accordingly, embodiments described herein provide systems and methods for medical device diagnostics and alerting. For example, one embodiment provides a system for medical device diagnostics and alerting. The system includes an electronic processor configured to receive a new medical device dataset associated with a medical device. The electronic processor is also configured to determine an operation classification of the medical device using a model developed with machine learning using training information, the training information including a plurality of archived medical device datasets and an associated operation classification for each of the plurality of archived medical device datasets. The electronic processor is also configured to determine an operation status of the medical device based on the operation classification. The electronic processor is also configured to generate and provide a notification based on at least the operation status of the medical device.

Another embodiment provides a medical device diagnostics and alerting. The method includes receiving a new medical device dataset associated with a medical device. The method also includes determining, with an electronic processor, an operation classification of the medical device using a model developed with machine learning. The method also includes determining an operation status of the medical device based on the operation classification. The method also includes in response to determining the operation status of the medical device to be an abnormal operation status or a suspicious operation status, generating and providing, with the electronic processor, a notification based on at least the operation status of the medical device.

Yet another embodiment provides a non-transitory computer readable medium including instructions that, when executed by an electronic processor, causes the electronic processor to execute a set of functions. The set of functions includes receiving a new medical device dataset associated with a medical device. The set of functions also includes determining an operation classification of the medical device using a model developed with machine learning using training information, the training information including a plurality of archived medical device datasets and an associated operation classification for each of the plurality of archived medical device datasets. The set of functions also includes determining an operation status of the medical device based on the operation classification. The set of functions also includes, in response to determining the operation status of the medical device to be an abnormal operation status or a suspicious operation status, generating and providing a notification based on at least the operation status of the medical device.

Other aspects of the embodiments described herein will become apparent by consideration of the detailed description.

DETAILED DESCRIPTION

FIG.1schematically illustrates a system100for medical device diagnostics and alerting according to some embodiments. The system100includes a medical device105, a diagnostic device110, a server115, a device repository120, and a data repository125. In some embodiments, the system100includes fewer, additional, or different components than illustrated inFIG.1. For example, the system100may include multiple medical devices105, diagnostic devices110, servers115, device repositories120, data repositories125, or a combination thereof. Additionally, in some embodiments, one or more components of the system100may be distributed among multiple devices, servers, or databases, combined into a single device, server, or database. As one example, the device repository120and the data repository125may be combined into a single database.

The medical device105, the diagnostic device110, the server115, the device repository120, and the data repository125communicate over one or more wired or wireless communication networks140. Portions of the communication network140may be implemented using a wide area network (“WAN”), such as the Internet, a local area network (“LAN”), such as a Bluetooth™ network or Wi-Fi, and combinations or derivatives thereof. Alternatively or in addition, in some embodiments, components of the system100communicate directly as compared to through the communication network140. Also, in some embodiments, the components of the system100communicate through one or more intermediary devices not illustrated inFIG.1.

As illustrated inFIG.2, the server115includes an electronic processor200, a memory205, and a communication interface210. The electronic processor200, the memory205, and the communication interface210communicate wirelessly, over one or more communication lines or buses, or a combination thereof. The server115may include additional, fewer, or different components than those illustrated inFIG.2in various configurations. The server115may also perform additional functionality other than the functionality described herein. Also, the functionality (or a portion thereof) described herein as being performed by the server115may be distributed among multiple devices, such as multiple servers or devices included in a cloud service environment. In addition, in some embodiments, the diagnostic device110may be configured to perform all or a portion of the functionality described herein as being performed by the server115.

The electronic processor200includes a microprocessor, an application-specific integrated circuit (“ASIC”), or another suitable electronic device for processing data. The memory205includes a non-transitory computer readable medium, such as read-only memory (“ROM”), random access memory (“RAM”) (for example, dynamic RAM (“DRAM”), synchronous DRAM (“SDRAM”), and the like), electrically erasable programmable read-only memory (“EEPROM”), flash memory, a hard disk, a secure digital (“SD”) card, another suitable memory device, or a combination thereof. The electronic processor200is configured to access and execute computer-readable instructions (“software”) stored in the memory205. The software may include firmware, one or more applications, program data, filters, rules, one or more program modules, and other executable instructions. For example, the software may include instructions and associated data for performing a set of functions, including the methods described herein.

For example, as illustrated inFIG.2, the memory205may store a learning engine220and a model database225. In some embodiments, the learning engine220develops a model using one or more machine learning functions. Machine learning functions are generally functions that allow a computer application to learn without being explicitly programmed. In particular, a computer application performing machine learning functions (sometimes referred to as a learning engine) is configured to develop an algorithm based on training data or training information. For example, to perform supervised learning, the training data includes example inputs and corresponding desired (for example, actual) outputs, and the learning engine progressively develops a model that maps inputs to the outputs included in the training data. Machine learning may be performed using various types of methods and mechanisms including but not limited to decision tree learning, association rule learning, artificial neural networks, inductive logic programming, support vector machines, clustering, Bayesian networks, reinforcement learning, representation learning, similarity and metric learning, sparse dictionary learning, and genetic algorithms. Using one or more of these approaches, a computer program may ingest, parse, and understand data and progressively refine models for data analytics, including medical device diagnostics and alerting.

Accordingly, the learning engine220(as executed by the electronic processor200) may perform machine learning using training data to develop a model that maps a medical device dataset to an operation classification. The training data may include, for example, medical device datasets and their associated operation classifications. For example, the learning engine220may identify one or more unique characteristics, trends, or defects of the medical device dataset (for example, anomalies or outliers included in the medical device dataset, features indicating malicious activity or tampering, features indicating a malfunction, and the like) and develop a model that maps the one or more unique characteristics, trends, or defects to a particular operation classification, such as abnormal operation, suspicious operation, or normal operation. Accordingly, when a subsequent medical device dataset is received, the developed model may be used to determine an operation classification for that subsequent medical device dataset. In other words, the model, once trained, analyzes a medical device dataset to identify one or more characteristics, trends, or defects in the medical device dataset and assigns the medical device dataset an operation classification based on any detected characteristics, trends, or defects. As described in more detail below, in some embodiments, the model is applied to a medical device dataset (or a new medical device dataset) at the point of data acquisition from, for example, the medical device105with the diagnostic device110.

Models generated by the learning engine220may be stored in the model database225. As illustrated inFIG.2, the model database225is included in the memory205of the server115. In some embodiments, the model database225may be located external to the server115. In such embodiments, the server115may communicate with and access data from the model database225directly or through one or more of the communication network(s)140. Also, in some embodiments, the model database225may be included in or part of the device repository120, the data repository125, the medical device105, the diagnostic device110, or a combination thereof, which the server115may similarly access.

As illustrated inFIG.2, the memory205may also store device diagnostic software240. The device diagnostic software240is a software application executable by the electronic processor200. As described in more detail below, the device diagnostic software240, when executed by the electronic processor200, performs medical device diagnostics and reporting. As one example, the device diagnostic software240may receive or access a medical device dataset and determine an operation classification for the medical device105using one or more models included in the model database225. Alternatively or in addition, in some embodiments, the device diagnostic software240generates and provides information or instructions (for example, as a notification) based on the medical device dataset, the operation classification for the medical device associated with the medical device dataset, or a combination thereof.

The communication interface210allows the server115to communicate with devices external to the server115. For example, as illustrated inFIG.1, the server115may communicate with the medical device105, the diagnostic device110, the device repository120, the data repository125, or a combination thereof through the communication interface210. In particular, the communication interface210may include a port for receiving a wired connection to an external device (for example, a universal serial bus (“USB”) cable and the like), a transceiver for establishing a wireless connection to an external device (for example, over one or more communication networks140, such as the Internet, a LAN, a WAN, and the like), or a combination thereof.

As illustrated inFIG.1, the device repository120stores device information150. Device information150may include a plurality of device manuals, where each device manual includes information associated with a particular medical device (for example, the medical device105). A device manual may be, for example, a manufacturer's manual for an associated medical device, a user's guide for an associated medical device, or the like. Alternatively or in addition, in some embodiments, the device information150includes a listing of diagnostic codes for a particular medical device (for example, the medical device105). In some embodiments, the device information150stored in the device repository120is used as (or is part of) the training information for the models stored in the model database225.

Accordingly, the device database120provides for the storage and retrieval of device information150. In some embodiments, the device information150may be stored within a plurality of databases, such as within a cloud service. Although not illustrated inFIG.1, the device repository120may include components similar to the server115, such as an electronic processor, a memory, a communication interface, and the like. For example, the device repository120may include a communication interface configured to communicate (for example, receive data and transmit data) over the communication network140.

The data repository125stores one or more datasets170(referred to herein collectively as “the datasets170” and individually as “the dataset170”). A dataset170may also be referred to herein as a medical device dataset. In some embodiments, each of the datasets170stored in the data repository125correspond with at least one medical device. In other words, a dataset170includes data or readings associated with at least one corresponding medical device. As one example, the dataset170may include historical or previously collected medical data or readings from the medical device105. As another example, the dataset170may include medical data or readings from an additional or different medical device, such as a medical device associated with another user. Accordingly, in some embodiments, the datasets170stored in the data repository125is a collection of aggregated data from one or more medical devices (for example, a plurality of archived medical device datasets). In some embodiments, the datasets170stored in the data repository125are used as the training information for the models stored in the model database225. In such embodiments, the data repository125also stores an associated operation classification for one or more of the datasets170.

Accordingly, the data repository125provides for the storage and retrieval of the datasets170corresponding to one or more medical devices (for example, the medical device105, another user's medical device, and the like). Although not illustrated inFIG.1, the data repository125may include components similar to the server115, such as an electronic processor, a memory, a communication interface and the like. For example, the data repository125may include a communication interface configured to communicate (for example, receive data and transmit data) over the communication network140.

In some embodiments, the device repository120and the data repository170are combined to form a single repository that stores the device information150, the datasets170, or a combination thereof. Additionally, in some embodiments, the device repository120and the data repository170are combined into multiple individual repositories, where each individual repository stores device information150associated with a particular medical device and stores datasets170associated with that particular medical device. Alternatively or in addition, the datasets170, the device information150, or a combination thereof may be stored within a plurality of databases or repositories, such as within a cloud service.

The medical device105is configured to monitor a health condition of an associated user, perform an operation associated with a health condition of the associated user, or a combination thereof. The medical device105may be, for example, a pacemaker, an insulin pump, a health monitor, a cardiac defibrillator, a spinal cord neurostimulator, a transcutaneous electrical nerve simulator, and the like. Accordingly, in some embodiments, the medical device105is implanted within a user's body. However, in other embodiments, the medical device105is external to the user's body, such as a wearable medical device.

As illustrated inFIG.3, the medical device105includes a controller300, a sensor305, one or more electro-mechanical (“EM”) elements310(referred to collectively as “the EM elements310and individually as “the EM element310”), and a medical device communication interface315. The controller300, the sensor305, the EM elements310, and the medical device communication interface315communicate wirelessly, over one or more communication lines or buses, or a combination thereof. The medical device105may include additional, fewer, or different components than those illustrated inFIG.3in various configurations. As one example, the medical device105may include one or more energy sources configured to power the medical device105(or the components thereof). As another example, the medical device105may include multiple sensors305, controllers300, or a combination thereof. The medical device105may also perform additional functionality other than the functionality described herein. Also, the functionality (or a portion thereof) described herein as being performed by the medical device105may be distributed among multiple devices, such as multiple networked medical devices105.

The sensor305collects data related to a health condition of the user (for example, a medical device dataset). The sensor305may include, for example, a force sensor, a strain sensor, an image sensor, a vibration sensor, a photo optic sensor, a piezoelectric sensor, a pressure sensor, a position sensor, a temperature sensor, a blood glucose sensor, an electrocardiogram (“ECG”) sensor, a motion sensor, an inertial sensor, and the like. The data collected by the sensor305may be stored in a memory (not shown) of the medical device105. The EM element310is configured to perform an action or operation related to a health condition of a user. The EM element310may include, for example, a valve, an actuator, a pulse generator, an electrode, a reservoir, a motor, a pump, or the like. In some embodiments, the controller300controls one or more of the EM elements310based on data collected by the sensor305. As one example, the controller300may access the collected data and determine whether to perform an action or operation based on the collected data. Alternatively or in addition, the controller300may determine what action or operation to perform, how to perform that action or operation based on the collected data, or a combination thereof. When the controller300determines that a particular action should be performed, the controller300may transmit a control signal to a corresponding EM element310. In response to receiving the control signal, the EM element310may perform the action or operation according to the control signal.

The medical device communication interface315allows the medical device105to communicate with devices external to the medical device105. For example, as illustrated inFIG.1, the medical device105may communicate with the server115, the diagnostic device110, the device repository120, the data repository125, or a combination thereof through the medical device communication interface315. In particular, the medical device communication interface315may include a port for receiving a wired connection to an external device (for example, a USB cable and the like), a transceiver for establishing a wireless connection to an external device (for example, over one or more communication networks140, such as the Internet, a LAN, a WAN, and the like), or a combination thereof. As one example, the medical device105may communicate one or more medical device datasets to the diagnostic device110through the medical device communication interface315.

The diagnostic device110is a computing device and may include, for example, a desktop computer, a terminal, a workstation, a laptop computer, a tablet computer, a smart watch or other wearable, a smart television or whiteboard, or the like. Alternatively or in addition, the diagnostic device110may be a mobile communication device, such as a smart cellular device or phone. Although not illustrated, the diagnostic device110may include similar components as the server115(an electronic processor, a memory, and a communication interface). As seen inFIG.1, the diagnostic device110may also include a human-machine interface180for interacting with a user. The human-machine interface180may include one or more input devices, one or more output devices, or a combination thereof. Accordingly, in some embodiments, the human-machine interface180allows a user to interact with (for example, provide input to and receive output from) the diagnostic device110. For example, the human-machine interface180may include a keyboard, a cursor-control device (for example, a mouse), a touch screen, a scroll ball, a mechanical button, a display device (for example, a liquid crystal display (“LCD”)), a printer, a speaker, a microphone, or a combination thereof. In some embodiments, the human-machine interface180includes a display device. The display device may be included in the same housing as the diagnostic device110or may communicate with the diagnostic device110over one or more wired or wireless connections. For example, in some embodiments, the display device is a touchscreen included in a laptop computer, a tablet computer, or a mobile communication device. In other embodiments, the display device is a monitor, a television, or a projector coupled to a terminal, desktop computer, or the like via one or more cables.

The diagnostic device110is configured to read (or receive) medical device datasets from medical devices (for example, the medical device105) and provide information associated with the medical device datasets to a user of the diagnostic device110. Additionally, in some embodiments, the diagnostic device110transmits the medical device datasets for external analysis by, for example, the server115. In some embodiments, the diagnostic device110is able to distinguish the medical device105from other medical devices or equipment within the surrounding of or near by the medical device105.

A user of the diagnostic device110may include, for example, a first responder (for example, a firefighter, a paramedic, or the like), a medical or clinical personal (for example, a treating physician, a nurse, a physician's assistant, or the like), another user (for example, a user of the medical device105), or a combination thereof. As one example, the diagnostic device110may be a mobile communication device issued to a first responder. As another example, the diagnostic device110may be a mobile communication device of the user of the medical device105, such as the user's cellphone. As yet another example, the diagnostic device110may be a computing device located within a medical clinic or hospital.

FIG.4illustrates a method400for medical device diagnostics and reporting using the system ofFIG.1according to some embodiments. The method400is described herein as being performed by the server115and, in particular, the device diagnostic software240as executed by the electronic processor200. However, as noted above, the functionality performed by the server115(or a portion thereof) may be performed by other devices, including, for example, the diagnostic device110(via an electronic processor executing instructions). The method400is described with reference toFIG.5.FIG.5is an example communication diagram illustrating the communication between components of the system100according to some embodiments.

As illustrated inFIG.4, the method400includes receiving, with the electronic processor200, a new medical device dataset associated with the medical device105(at block405), as illustrated inFIG.5. The new medical device dataset may be medical data or readings collected by the medical device105(for example, by the sensor305). In some embodiments, the electronic processor200receives the new medical device dataset directly from the medical device105. However, in other embodiments, the electronic processor200receives the new medical device dataset from the diagnostic device110, which originally received the new medical device dataset from the medical device105. As described above, the diagnostic device110is configured to read (or receive) datasets from medical devices, such as the new medical device dataset from the medical device105. In such embodiments, the diagnostic device110forwards (or transmits) the new medical device dataset to the server115(the electronic processor200) for remote analytics and diagnostics. In some embodiments, the diagnostic device110automatically transmits the new medical device dataset to the server115in response to receiving the new medical device dataset. However, in other embodiments, the diagnostic device110transmits the new medical device dataset to the server115in response to a request, such as a manual request from a user of the diagnostic device110.

After receiving the new medical device dataset (at block405), the electronic processor200determines an operation classification of the medical device based on the new medical device dataset (at block410). In some embodiments, the electronic processor200determines the operation classification using one or more models developed with machine learning using training information, such as one or more of the models stored in the model database225. As noted above, the training information may include a plurality of archived medical device datasets (for example, the datasets170stored in the data repository125, as seen inFIG.5) and an associated operation classification for each of the plurality of archived medical device datasets. Accordingly, in such embodiments, the electronic processor200accesses the one or more models from the model database225and analyzes the new medical device dataset using the accessed one or more models (for example, applies the one or more models to the new medical device dataset).

The electronic processor200may then determine an operation status of the medical device105(at block412). Each associated operation classification may indicate an operation status for an associated medical device. The operation status of the medical device105may include, for example, an abnormal operation status, a normal operation status, or a suspicious operation status. A normal operation status may indicate that the medical device105is operating or functioning normally. An abnormal operation status may indicate that the medical device105is not operating or functioning normally. As one example, the electronic processor200may determine the operation status to be an abnormal operation status when the medical device105is malfunctioning, such as a malfunction resulting from a fault experienced by a component of the medical device105. A suspicious operation status may indicate that the medical device105was tampered with or subjected to a cyber-attack (or is currently being tampered with or subjected to a cyber-attack). As one example, the electronic processor200may determine the operation status to be a suspicious operation status when the medical device105was hacked.

As seen inFIG.4, the electronic processor200may generate and provide a notification based on at least the operation status of the medical device105(at block410). In some embodiments, the notification includes a set of instructions for treating a user of the medical device105. As one example, when the electronic processor200determines that the operation status of the medical device105is an abnormal operation status, the electronic processor200may generate and provide a notification including an instruction to “Start reading patient vitals by another means due to a malfunction.” As another example, when the electronic processor200determines that the operation status of the medical device105is a suspicious operation status, the electronic processor200may generate and provide a notification including an instruction to “Please alert cyber security or IT team that device was tampered with.” Alternatively or in addition, the notification may include the operation status of the medical device105. As one example, when the electronic processor200determines that the operation status of the medical device105is a normal operation status, the electronic processor200may generate and provide a notification indicating that the medical device105is exhibiting normal operation. In some embodiments, the notification includes information associated with a user of the medical device105. Such information may include, for example, an identification of the user (for example, a name of the user), a characteristic of the user (for example, an age, a blood type, or the like), a health condition of the user, a medicine list of the user, and the like. Alternatively or in addition, the notification includes information associated with the medical device105, such as information included in the device information150for the medical device105. Alternatively or in addition, the notification may include the new medical device dataset, including raw data or readings of the new medical device dataset.

In some embodiments, the electronic processor200generates and provides the notification in response to determining the operation status of the medical device105is an abnormal operation status, a suspicious operation status, or a combination thereof. Accordingly, in such embodiments, the electronic processor200may generate and provide the notification in situations where the medical device105may have been tampered with (a suspicious operation status) or is malfunctioning (an abnormal operation status).

In some embodiments, the electronic processor200generates and provides the notification by transmitting the notification to the diagnostic device110, as seen inFIG.5. In such embodiments, the diagnostic device110may provide the notification to a user of the diagnostic device110in response to receiving the notification from the electronic processor200. As one example, the diagnostic device110may display the notification via the human-machine interface180(for example, a display device of the diagnostic device110). As another example, the diagnostic device110may verbally provide the notification via a microphone component of the diagnostic device110.

One or more embodiments are described and illustrated in the following description and accompanying drawings. These embodiments are not limited to the specific details provided herein and may be modified in various ways. Furthermore, other embodiments may exist that are not described herein. Also, the functionality described herein as being performed by one component may be performed by multiple components in a distributed manner. Likewise, functionality performed by multiple components may be consolidated and performed by a single component. Similarly, a component described as performing particular functionality may also perform additional functionality not described herein. For example, a device or structure that is “configured” in a certain way is configured in at least that way, but may also be configured in ways that are not listed. Furthermore, some embodiments described herein may include one or more electronic processors configured to perform the described functionality by executing instructions stored in non-transitory, computer-readable medium. Similarly, embodiments described herein may be implemented as non-transitory, computer-readable medium storing instructions executable by one or more electronic processors to perform the described functionality. As used in the present application, “non-transitory computer-readable medium” comprises all computer-readable media but does not consist of a transitory, propagating signal. Accordingly, non-transitory computer-readable medium may include, for example, a hard disk, a CD-ROM, an optical storage device, a magnetic storage device, a ROM (Read Only Memory), a RAM (Random Access Memory), register memory, a processor cache, or any combination thereof.

In addition, the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. For example, the use of “including,” “containing,” “comprising,” “having,” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. The terms “connected” and “coupled” are used broadly and encompass both direct and indirect connecting and coupling. Further, “connected” and “coupled” are not restricted to physical or mechanical connections or couplings and can include electrical connections or couplings, whether direct or indirect. In addition, electronic communications and notifications may be performed using wired connections, wireless connections, or a combination thereof and may be transmitted directly or through one or more intermediary devices over various types of networks, communication channels, and connections. Moreover, relational terms such as first and second, top and bottom, and the like may be used herein solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.

Various features and advantages of the embodiments are set forth in the following claims.