Learned monitoring device correction

Methods, apparatus, and systems for medical procedures include a monitoring and processing apparatus that includes a memory configured to store a diagnostic algorithm, a sensor configured to sense a patient data of a first patient, a processor configured to generate a first diagnostic result based on the patient data and the diagnostic algorithm. A local computing device is provided and includes a processor configured to receive the first diagnostic result via a first network, receive a first correction indication that includes a correction of the first diagnostic result and transmit the first correction indication via a second network. A remote computing device may be provided and be configured to generate an updated diagnostic algorithm that is updated based on the first correction indication and transmit the updated diagnostic algorithm via the second network.

FIELD OF INVENTION

The present application provides systems, apparatuses, and methods for improving medical device procedures.

BACKGROUND

Medical monitoring and processing apparatuses that are attached to or implanted within a patient can provide useful information for the diagnosis and treatment of medical conditions experienced by the patient. Such devices may be attached to the surface of a patient's body, such as via an adhesive component. Alternatively, such devices can be internal to a patient's body (e.g., subcutaneously implantable) and may gather data from within the patient's body.

A monitoring and processing apparatuses that is attached to or inserted into a patient may provide data and analysis to external devices, which may further use the data and/or analysis for treatment purposes.

SUMMARY

Methods, apparatus, and systems for medical procedures are disclosed herein and include a monitoring and processing apparatus that includes a memory configured to store a diagnostic algorithm, a sensor configured to sense a patient data of a first patient, a processor configured to generate a first diagnostic result based on the patient data and the diagnostic algorithm. The monitoring and processing apparatus may be an external apparatus or an implantable apparatus. A local computing device is provided and may include a processor and configured to receive the first diagnostic result via a first network, receive a first correction indication that comprises a correction of the first diagnostic result and transmit the first correction indication via a second network. A remote computing device may be provided and be configured to generate an updated diagnostic algorithm that is updated based on the first correction indication and transmit the updated diagnostic algorithm via the second network. The updated diagnostic algorithm may be received by a plurality of second monitoring and processing apparatuses and the plurality of second monitoring and processing apparatuses may be selected based on at least one of a location, a predetermined grouping, or a patient-based grouping.

The updated diagnostic algorithm may be generated based on the first correction indication and a second correction indication. The updated diagnostic algorithm may be updated based on a threshold number of correction indications including the first correction indication and the second correction indication. The second correction indication may be based on a second patient. The diagnostic algorithm may include a first weight and the updated diagnostic algorithm may include an updated first weight.

The monitoring and processing apparatus sensor may configured to sense the patient data using one or more electrodes coupled to the monitoring and processing apparatus. The patient data may include an electrocardiograph (ECG) signal.

The first network may be a local area network (LAN) and the second network is a wide area network (WAN).

Patient data of a first patient may be sensed at a first monitoring and processing apparatus. The monitoring and processing apparatus may be an external apparatus or an implantable apparatus. A first diagnostic result may be generated based on the patient data and a diagnostic algorithm. The first diagnostic result may be transmitted via a first network. An updated diagnostic algorithm may be received from a local computing device via the first network and the updated diagnostic algorithm may be based at least on a first correction indication comprising a correction of the first diagnostic result and provided to the local computing device by a remote computing device via a second a second network. A second diagnostic result may be generated based on the updated diagnostic algorithm.

The updated diagnostic algorithm may be further based on a second correction indication. According to an embodiment, the updated diagnostic algorithm may be updated based on a threshold number of correction indications comprising the first correction indication and the second correction indication. The second correction indication may be based on a second patient. The diagnostic algorithm may include a first weight and the updated diagnostic algorithm may include an updated first weight.

Sensing patient data at the monitoring device may include sensing the patient data using one or more electrodes coupled to the monitoring and processing apparatus. The patient data may include an electrocardiograph (ECG) signal. The first network may be a local area network (LAN) and the second network is a wide area network (WAN).

According to an embodiment, a first monitoring and processing apparatus may include a sensor configured to sense a patient data of a first patient and a transmitter configured to transmit the patient data via a first network. A local computing device may be provided and may include a memory configured to store a diagnostic algorithm and a processor configured to receive the patient data via the first network, generate a first diagnostic result based on the patient data and the diagnostic algorithm, receive a first correction indication that comprises a correction of the first diagnostic result and transmit the first correction indication via a second network. A remote computing device may be provided and may be configured to generate an updated diagnostic algorithm that is updated based on the first correction indication and transmit the updated diagnostic algorithm via the second network.

DETAILED DESCRIPTION

According to embodiments of the disclosed subject matter, a monitoring and processing apparatus may be attached to or implanted in a patient, such as a cardiac patient, and may monitor the medical status of a patient. Patient biometrics acquired by the monitoring and processing apparatus, in response to signals generated by the patient (e.g., the patient's heart), and analysis or diagnosis derived from the patient data may be transmitted wirelessly to a local computing device (e.g., a computer or mobile device). The patient biometrics may be analyzed by the monitoring and processing apparatus and/or the local computing device and a diagnostic result may be provided based on the same. A diagnostic result may be determined by using a diagnostic algorithm.

A diagnostic result provided by the diagnostic algorithm may be provided to a healthcare professional via a local computing device. The healthcare professional may determine that the diagnostic result is incorrect, in whole or in part. The healthcare professional may provide a correction indication via the local computing device. For example, the healthcare professional may determine that a cardiac signal has been incorrectly identified by the diagnostic algorithm as indicating atrial fibrillation. The healthcare professional may determine that the cardiac signal indicates atrial tachycardia instead of an atrial fibrillation. Accordingly, the healthcare professional may provide a correction indication that corrects the diagnostic result to be atrial tachycardia instead of an atrial fibrillation.

The correction indication may be provided to a remote computing device that may be configured to receive correction indications from different local computing devices. The remote computing device may determine, based on the correction indication, or a plurality of correction indications, that the diagnostic algorithm should be updated. For example, the remote computing device may determine that one or more weights within the diagnostic algorithm should be updated based on the one or multiple correction indications received by the remote computing devices. The remote computing device may update the diagnostic algorithm, based on the determination, and may provide the updated diagnostic algorithm to one or more local computing devices and/or monitoring and processing apparatuses.

Embodiments described herein provide systems, apparatuses, and methods for remotely monitoring various biometric patient activity (e.g., electrocardiogram (ECG) signals, electroencephalography (EEG) signals, Electromyography (EMG) signals, blood pressure, temperature and other measurable biometrics) for use with different medical modalities (e.g., ECG monitoring, pressure monitoring, orthopedics, and pain management treatment) for treatment across various disease states.

Embodiments described herein include one or more monitoring and processing apparatuses that continuously or periodically monitor the patient biometrics (i.e., patient data) and temporarily store (e.g., in buffer memory) the patient data for a period of time (e.g., 1 hour). Upon detection of an event (e.g., a user input, a detected arrhythmia, a loss of contact or connection), the one or more monitoring and processing apparatuses may store a portion of the patient data at and within a range of time before and after the detected event to non-volatile memory. According to an embodiment, the one or more monitoring and processing apparatuses may analyze the patient data based on a diagnostic algorithm and generate a diagnostic result based on the same. Additionally, the one or more monitoring and processing apparatus may wirelessly communicate, via a short-range network (e.g., local area network (LAN) or personal area network (PAN)), the portion of the patient data to a local computing device in synchronous communication with the monitoring and processing apparatus. The local computing device may display one or more diagnostic results, all or a portion of the patient data, and information associated with the patient data (e.g. additional information from another device, etc.), and communicate, via a long-range network (e.g., wide area network (WAN), the internet, a cellular network), at least some of the patient data, one or more diagnostic results, and the associated information to a remote computing system.

FIG.1is a block diagram of an example system100for updating a diagnostic algorithm based on correction indications. In the example illustrated inFIG.1, the system100includes a patient biometric monitoring and processing apparatus102associated with a patient104, a local computing device106, a remote computing system108, a first network110and a second network120.

According to an embodiment, a monitoring and processing apparatus102may be an apparatus that is internal to the patient's body (e.g., subcutaneously implantable). The monitoring and processing apparatus102may be inserted into a patient via any applicable manner including oral injection, surgical insertion via a vein or artery, an endoscopic procedure, or a laparoscopic procedure.

According to an embodiment, a monitoring and processing apparatus102may be an apparatus that is external to the patient. For example, the monitoring and processing apparatus102may include an attachable patch (e.g., that attaches to a patient's skin). The monitoring and processing apparatus102may also include a blood pressure cuff, a weight scale, a bracelet or smart watch biometric tracker, a glucose monitor, a continuous positive airway pressure (CPAP) machine or virtually any device which may provide an input concerning the health or biometrics of the patient.

According to an embodiment, a monitoring and processing apparatus102may include both components that are internal to the patient and components that are external to the patient.

A single monitoring and processing apparatus102is shown inFIG.1. Example systems may, however, include a plurality of patient biometric monitoring and processing apparatuses. A patient biometric monitoring and processing apparatus may be in communication with one or more other patient biometric monitoring and processing apparatuses. Additionally, a monitoring and processing apparatus may be in communication with the network110.

One or more monitoring and processing apparatuses102may acquire patient data (e.g., electrical signals, blood pressure, temperature, blood glucose level or other biometric data) and receive at least a portion of the patient biometric data representing the acquired patient biometrics and additional formation associated with acquired patient biometrics from one or more other monitoring and processing apparatuses102. The additional information may be, for example, diagnosis information and/or additional information obtained from an additional device such as a wearable device. Each monitoring and processing apparatus102may process data, including its own acquired patient data as well as data received from one or more other monitoring and processing apparatuses102. One or more monitoring and processing apparatus102may analyze the patient data based on a diagnostic algorithm stored in a monitoring and processing apparatus102memory and provide one or more diagnostic results based on the analysis.

InFIG.1, network110is an example of a short-range network (e.g., local area network (LAN), or personal area network (PAN)). Information may be sent, via short-range network110, between monitoring and processing apparatus102and local computing device106using any one of various short-range wireless communication protocols, such as Bluetooth, Wi-Fi, Zigbee, Z-Wave, near field communications (NFC), ultraband, Zigbee, or infrared (IR).

Network120may be a wired network, a wireless network or include one or more wired and wireless networks. For example, a network120may be a long-range network (e.g., wide area network (WAN), the internet, or a cellular network). Information may be sent, via network120, using any one of various long-range wireless communication protocols (e.g., TCP/IP, HTTP, 3G, 4G/LTE, or 5G/New Radio).

The patient monitoring and processing apparatus102may include a patient biometric sensor112, a processor114, a memory118, and a transmitter-receiver (i.e., transceiver)122. The patient monitoring and processing apparatus102may continually or periodically monitor, store, process and communicate, via network110, any number of various patient biometrics. The patient data may be monitored and communicated for treatment across any number of various diseases, such as cardiovascular diseases (e.g., arrhythmias, cardiomyopathy, and coronary artery disease) and autoimmune diseases (e.g., type I and type II diabetes).

Patient biometric sensor112may include, for example, one more sensors configured to sense patient biometrics. For example, patient biometric sensor112may include one or more electrodes configured to acquire electrical signals (e.g., heart signals, brain signals or other bioelectrical signals), a temperature sensor, a blood pressure sensor, a blood glucose sensor, a blood oxygen sensor, a pH sensor, an accelerometer and a microphone). The patient biometric sensor112may be powered by a power source (not shown) such as a battery and/or chargeable capacitor that may also supply operating power to other components of the monitoring and processing apparatus102. The power source may be researchable, for example, by radio-frequency (RF) energy.

According to an example, the monitoring and processing apparatus102may be a continuous glucose monitor (CGM) for continuously monitoring blood glucose levels of a patient on a continual basis for treatment of various diseases, such as type I and type II diabetes. The CGM may include a subcutaneously disposed electrode, which may monitor blood glucose levels from interstitial fluid of the patient. The CGM may be, for example, a component of a closed-loop system in which the blood glucose data is sent to an insulin pump for calculated delivery of insulin without user intervention.

Transceiver122may include a transmitter and receiver. Alternatively, transceiver122may include a transmitter and receiver integrated into a single device. Transceiver122may be configured to receive and transmit signals using network110.

Processor114may be configured to store data, such as patient data (e.g., patient biometric data) in memory118acquired by patient biometric sensor112, and communicate the patient data, across network110, via a transmitter of transceiver122. Data from one or more other monitoring and processing apparatus102may also be received by a receiver of transceiver122.

The local computing device106of system100may be in communication with the monitoring and processing apparatus102and may be configured to act as a gateway to the remote computing system108through the second network120. The local computing device106may be, for example, a smart phone, smartwatch, tablet or other portable smart device configured to communicate with other devices via network. Alternatively, the local computing device106may be a stationary or standalone device, such as a stationary base station including, for example, modem and/or router capability, a desktop or laptop computer using an executable program to communicate information between the processing apparatus102and the remote computing system108via the PC's radio module, or a USB dongle. Patient data may be communicated between the local computing device106and the patient biometric monitoring and processing apparatus102using a short-range wireless technology. The local computing device106may be configured to display the acquired patient data and information, such as diagnostic results, associated with the acquired patient data.

FIG.2is a block diagram of an example of the local computing device106ofFIG.1. As described above, the local computing device106may be a mobile device (e.g., smart phone, smart watch, tablet or other portable smart device configured to communicate with other devices via network). Alternatively, the local computing device106may be a stationary device (e.g., a stationary base station including for example, modem and/or router capability, a desktop or laptop computer or other dedicated standalone device). As shown inFIG.2, the local computing device106may include one or more of a user interface202, a processor204, a network interface206(e.g., for a mobile device or a stationary device with a network connection), memory208, and a transceiver210.

The memory208may be configured to store a diagnostic algorithm. The processor204may be configured to receive patient data and provide a diagnostic result based on the diagnostic algorithm stored in memory208. Although a single diagnostic algorithm is described herein, it should be understood that multiple diagnostic algorithms may be stored (e.g., in memory208) and used (e.g., by processor204) to provide one or more diagnostic results.

User interface202may be, for example, a touch screen configured to display information, such as patient biometrics, diagnostic results and/or receive user inputs. Processor204may be configured to control the user interface to execute an application that displays the monitored patient data received from one or more monitoring and processing apparatus102ofFIG.1. Alternatively or additionally, processor204may also be configured to provide an indication to a sensor112ofFIG.1. The indication may be a signal (e.g., activation or wake up signal, activity signal, or inactivity signal), data, or the like.FIG.4, as further described herein, shows an example user interface202.

FIG.3Ais a diagram of an example method300of generating an updated diagnostic algorithm. At310of method300, patient data may be obtained at a monitoring and processing apparatus. The monitoring and processing apparatus may be the same as or similar to monitoring and processing apparatus102ofFIG.1. The patient data may be obtained by one or more sensors (e.g., sensor112) configured to sense patient biometric data. The patient data may be sampled continuously or at pre-determined or dynamically determined time intervals. As an example, patient data may be sampled at 5 second intervals. As another example, the frequency of the sampling may be determined based on a patient history such that a higher sampling rate may be utilized for a patient that is more prone to a medical condition and a lower sampling rate may be utilized for a patient that is less prone to a medical condition. The patient data may be stored in a memory, such as memory118ofFIG.1.

According to an embodiment, the patient data may be sampled based on a patient input. A patient input may include an input via a local computing device, a voice command, a gesture, or the like.FIG.7shows an example of a gesture based patient input where a patient701or a caregiver (not shown) may initiate the sampling of patient data by performing a motion703using a mobile device702. The mobile device702may include one or more sensors, such as an accelerometer, which may provide an indication that a predetermined gesture is performed. The predetermined gesture may be converted into an input to sample patient data and an input signal may be provided to a monitoring and processing apparatus via network110ofFIG.1. Further, the mobile device702may be placed in a receive mode and may be configured to receive data from a monitoring and processing apparatus. According to an embodiment, an input signal may be generated by the mobile device702based on both the motion703as well as the change in proximity of the mobile device702to a monitoring and processing apparatus (not shown inFIG.7).

At312of method300, the patient data may be analyzed at the monitoring and processing apparatus. The patient data may be analyzed by a processor, such as processor114ofFIG.1. The processor may analyze the patient data based on a diagnostic algorithm that may be stored in monitoring and processing apparatus memory, such as memory118.

The processor may analyze the patient data based on the diagnostic algorithm in any applicable format such as by utilizing the patient data as inputs for the diagnostic algorithm and then matching the output of the diagnostic algorithm to pre-determined and/or stored diagnoses. Accordingly, one or more diagnoses that most closely match the output of the diagnostic algorithm may be determined to be the diagnostic result.

Alternatively, the processor may analyze the patient data by comparing the patient data to known data that corresponds to one or more diagnostic results. Accordingly, one or more known data, corresponding to one or more diagnoses that most closely match the patient data may be identified and the one or more diagnoses may be determined to be the diagnostic result.

According to an embodiment, the processor may receive additional data from an additional device, such as the external sensing apparatus504ofFIG.5, as further disclosed herein. As a simplified example, an additional device may be a thermometer that provides patient or environmental temperature readings as additional data. The additional data may be utilized as an input for the diagnostic algorithm.

The diagnostic algorithm may include weights that are configured to enable the diagnostic algorithm to determine a diagnostic result. The weights may enable the diagnostic algorithm to match the patient data to a diagnosis and, accordingly, to provide a diagnostic result. As a simplified example, a monitoring and processing apparatus may include electrodes that sense local activation time (LAT) values of a patient's heart. The diagnostic algorithm may apply a given weight X to LAT values within a first range and a different weight Y to LAT values within a second range. The diagnostic algorithm may then compare the result weighted LAT values to the pre-mapped values of a known diagnosis (e.g., atrial fibrillation) to determine if given patient data corresponds to the known diagnosis. If the weighted LAT values correspond to the pre-mapped values of the known diagnosis, the known diagnosis may be provided as a diagnostic result.

The diagnostic algorithm may be pre-installed and/or stored in the monitoring and processing apparatus memory at a time of manufacturing or prior to the monitoring and processing apparatus being placed on/in a patient. Alternatively, the diagnostic algorithm may be transmitted onto the monitoring and processing apparatus after it is placed on/in the patient.

One or more diagnostic results, as determined by the monitoring and processing apparatus' processor, may be transmitted via a local area network such as network110ofFIG.1. At314of method300, the one or more diagnostic results may be received by a local computing device, such as local computing device106ofFIGS.1and2. According to an embodiment, the diagnostic result may be pushed to the local computing device, via the local area network, upon a determination of the diagnostic result. For example, upon a determination and transmission of the diagnostic result, a notification may be provided on a user interface202of the local computing device106.

According to an embodiment, a diagnostic result may be transmitted to a local computing device based on a patient input. A patient input may include an input via a local computing device, a voice command, a gesture, or the like.FIG.7shows an example of a gesture based patient input where a patient701or a caregiver (not shown) may initiate the transfer of a diagnostic result by performing a motion703using a mobile device702. The mobile device702may include one or more sensors, such as an accelerometer, which may provide an indication that a predetermined gesture is performed. The predetermined gesture may be converted into an input signal to instruct a monitoring and processing apparatus to transmit one or more diagnostic results and an input signal may be provided to a monitoring and processing apparatus via network110ofFIG.1. Further, the mobile device702may be placed in a receive mode and may be configured to receive data from a monitoring and processing apparatus. According to an embodiment, an input signal may be generated by the mobile device702based on both the motion703as well as the change in proximity of the mobile device702to a monitoring and processing apparatus (not shown inFIG.7).

A local computing device that receives a diagnostic result may be accessed by a healthcare professional such as a physician or a medical technician. Additionally, the patient data and/or a portion or indication of the patient data may also be received from the monitoring and processing apparatus at the local computing device.FIG.4shows an example user interface202of a local computing device. Patient data401is provided via the user interface202and may be displayed along with a diagnostic result412as well as one or more alternative diagnoses414.

At316of method300, the local computing device may receive a correction indication based on a diagnostic result provided at312. The correction indication may be provided by a healthcare professional or may be automatically determined based on software located at the local computing device. The health care professional or the local computing device software may provide the correction indication based on one or more factors such as, but not limited to, the patient data (e.g., patient data401ofFIG.4), patient history, data from an additional device (e.g., a blood pressure cuff, a weight scale, a bracelet or smart watch biometric tracker, a glucose monitor, a CPAP machine or any device which may provide an input concerning the health or biometrics of the patient), and/or patient observation.

The correction indication received at316of method300may be provided by a touch input, voice command, via an accessory (e.g., keyboard, mouse, etc.), a gesture, or the like. As an example, the correction indication may be provided by selecting one of the diagnoses414from the user interface202ofFIG.4, using a computer mouse.

As shown at316of method300, the correction indication may be provided to a remote computing device such as remote computing device106ofFIG.1. A more detailed description of the remote computing device106is provided inFIG.6. The correction indication may be provided to a remote computing device via a wide area network such as wide area network120ofFIG.1.

The remote computing device may be configured to receive correction indications from one or more local computing devices, as further described herein. Further, the diagnostic algorithm or a version of the diagnostic algorithm may be stored at a remote computing device.

At step318of process300, the remote computing device may make a determination whether a received correction indication is sufficient to warrant a change to the diagnostic algorithm. The determination may be made based on one or more factors such as, but not limited to, the source of the correction indication (e.g., a first source may be weighted higher than a second source), the number of similar correction indications received, the frequency of similar correction indications received, the frequency of the corrected diagnostic result, the frequency of correction indication received for the given diagnostic result, a patient population, or a location. The determination may be made based on a pre-determined or dynamically determined threshold. For example, if the remote computing device receives a threshold number of correction indications when a first diagnostic result is provided to health care professionals, then318may be triggered in the affirmative and320may be performed. Continuing the example, if a correction indication does not meet a threshold number of received correction indications, then318may not be triggered and the method300may cycle back to310and patient data may be analyzed based on an existing diagnostic algorithm.

If318is triggered in the affirmative,320of method300may be performed. At320, an updated diagnostic algorithm may be generated based at least on the correction indication. The updated diagnostic algorithm may include one or more updated weights.

Alternatively, or additionally, the updated diagnostic algorithm may include one or more updated variables such that additional or different portions of the patient data are utilized when determining diagnostic results. An updated variable may be added, for example, based on additional data provided by an external sensing apparatus (e.g., the external sensing apparatus504ofFIG.5). As an example, an external sensing apparatus may provide elevation data that provides the sea level recorded when a correction indication is received. A determination may be made that a given correction indication is repeatedly received when the sea level indicated by a plurality of different external sensing apparatus in different locations indicate a sea level above a threshold sea level. Accordingly, an elevation-based variable may be added to a diagnostic algorithm such that if a sea level reading or other elevation data is available, then the elevation-based variable is a factor in determining diagnostic results using the updated diagnostic algorithm.

The updated diagnostic algorithm may be applied such that patent data analyzed based on the updated diagnostic algorithm can provide different results (e.g., diagnostic results) than the results provided by analyzing the same patient data based on the original diagnostic algorithm.

An updated diagnostic algorithm, as generated at320of method300, may be stored at a remote computing device memory. According to an embodiment, the updated diagnostic algorithm may be provided to one or more monitoring and processing devices. The updated diagnostic algorithm may be provided to the monitoring and processing apparatuses when the diagnostic algorithm is updated at320. Alternatively, the updated diagnostic algorithm may be provided during a software update.

FIG.3Aillustrates a method300that includes analyzing patient data based on a diagnostic algorithm that is stored at a monitoring and processing apparatus.FIG.3Bis a flow diagram of a method340that includes analyzing patient data based on a diagnostic algorithm that is stored at a local computing device. As methods300and340are similar, though not the same, overlapping disclosure and examples are omitted. However, it should be understood that description and examples provided with respect to method300ofFIG.3Aalso apply to the method340described inFIG.3B.

At350of method340, patient data may be obtained at a monitoring and processing apparatus. The patient data may be obtained by one or more sensors configured to sense patient biometric data. The patient data may be sampled continuously or at pre-determined or dynamically determined time intervals and may be stored in a memory, such as memory118ofFIG.1. The obtained patient data may be transmitted by a monitoring and process apparatus transceiver, such as transceiver122ofFIG.1, and may be received by a local computing device such as local computing device106. The patient data may be transmitted over a local area network110. According to an embodiment, the patient data may be sampled based on a patient input. A patient input may include an input via a local computing device, a voice command, a gesture, or the like.FIG.7shows an example of a gesture based patient input, as described in relation to310of method300.

At352of method340, the patient data may be analyzed at the local computing device. The patient data may be analyzed by a processor, such as processor204ofFIG.2. The processor may analyze the patient data based on a diagnostic algorithm that may be stored in the local computing device memory, such as memory208. The processor may analyze the patient data based on the diagnostic algorithm in any applicable format such as those described in relation to312of method300.

The diagnostic algorithm may include weights that are configured to enable the diagnostic algorithm to determine a diagnostic result. The weights may enable the diagnostic algorithm to match the patient data to a diagnosis and, accordingly, to provide a diagnostic result. The diagnostic algorithm may be pre-installed and/or stored in the local computing device memory at a time of manufacturing or initial configuration of the local computing device. Alternatively, the diagnostic algorithm may be transmitted onto the local computing device as part of an application installation, application update, or diagnostic algorithm download.

At354of method340, one or more diagnostic results, as determined by the local computing device's processor, may be provided to a healthcare professional via the local computing device (e.g., via a user interface202FIG.2). Additionally, patient data and/or a portion or indication of the patient data obtained by the monitoring and processing apparatus at350of method340may also be provided via the local computing device.FIG.4shows an example user interface202of a local computing device. Patient data401is provided via the user interface202and may be displayed along with a diagnostic result412as well as one or more alternative diagnoses414.

At356of method300, the local computing device may receive a correction indication based on a diagnostic result provided at354. The correction indication may be provided by a healthcare professional or may be automatically determined based on software located at the local computing device. The health care professional or the local computing device software may provide the correction indication based on one or more factors such as, but not limited to, the patient data (e.g., patient data401ofFIG.4), patient history, data from an additional device (e.g., a blood pressure cuff, a weight scale, a bracelet or smart watch biometric tracker, a glucose monitor, a CPAP machine or any device which may provide an input concerning the health or biometrics of the patient), and/or patient observation.

The correction indication received at356of method340may be provided by a touch input, voice command, via an accessory (e.g., keyboard, mouse), a gesture, or the like. As an example, the correction indication may be provided by selecting one of the diagnoses414from the user interface202ofFIG.4, using a computer mouse.

As shown at356of method340, the correction indication may be provided to a remote computing device such as remote computing device106ofFIG.1andFIG.6. The correction indication may be provided to the remote computing device via a wide area network such as wide area network120ofFIG.1.

At358of method300, the remote computing device may make a determination whether a received correction indication is sufficient to warrant a change to the diagnostic algorithm, as described in relation to318of method300.

If358is triggered in the affirmative,360of method340is performed. At360, an updated diagnostic algorithm may be generated based at least on the correction indication, as described in relation to320of method300.

An updated diagnostic algorithm, as generated at360of method340, may be stored at a remote computing device memory. According to an embodiment, the updated diagnostic algorithm may be provided to one or more local computing devices. The updated diagnostic algorithm may be provided to the local computing devices when the diagnostic algorithm is updated at360. Alternatively, the updated diagnostic algorithm may be provided during a software update, application download, or the like.

FIG.5is a diagram of an example system500that includes multiple patients. For simplified explanatory purposes, a local computing device106inFIG.5is described as a stationary device, which may be, for example, a healthcare professional computer or a mobile device, which may be, for example, a mobile phone accessible to a health care professional. As described above, local computing devices106may be mobile devices or stationary devices. Each local computing device106inFIG.5may be in communication with attachable monitoring and processing apparatuses102aand implantable monitoring and processing apparatuses102bassociated with one of the users502a. . .502z. For example, information may be sent between each of the local computing devices106and associated monitoring and processing apparatuses102aand102bvia wireless communication channels520using any one of various short-range wireless communication protocols. Wireless communication channels520may be the same as or similar to the network110ofFIG.1.

As shown inFIG.5, a plurality of attachable monitoring and processing apparatuses102a(e.g., monitoring and processing apparatus102shown inFIG.1) and/or a plurality of implantable monitoring and processing apparatuses102b(e.g., implanted subcutaneously within a patient) may be used to monitor patient biometric data of each user502. The number of attachable monitoring and processing apparatuses102aand implantable monitoring and processing apparatuses102bshown inFIG.5is merely an example. Any number of attachable monitoring and processing apparatuses102aand implantable monitoring and processing apparatuses102bmay be used. The location of the attachable monitoring and processing apparatuses102aand implantable monitoring and processing apparatuses102bshown inFIG.5are also example locations.

In addition to or alternative to the monitoring and processing apparatuses102aand monitoring and processing apparatuses102bshown inFIG.5, an external sensing apparatus504may be optionally used to monitor data and may communicate with an associated local computing device106via one of the wireless communication channels520. Although a single external sensing apparatus504is shown inFIG.5, examples include any number of external sensing apparatuses504in communication with an associated mobile device.

As shown inFIG.5, each local computing device106may be connected to network120(e.g., the Internet) via a wireless communication channel505. Network120is also shown inFIG.1. A cloud database512is an example of a remote computing system108ofFIG.1and is also connected to the network120via communication channel510. The cloud database512may be implemented using one or more of the components of the computing environment600shown inFIG.6. In some instances, the cloud database512is implemented by a public cloud computing platform (such as Amazon Web Services, or Microsoft Azure), a hybrid cloud computing platform (such as HP Enterprise OneSphere) or private cloud computing platform.

The cloud database512may include a machine learning system such as a neural network. The machine learning system may include predetermined or dynamically updated rules and thresholds for updating one or more diagnostic algorithms such as those described with respect toFIGS.3A and3B. The cloud database512may receive correction indications from local computing devices106via wireless communication channel505of network120. As disclosed herein, the cloud database512may determine whether one or more correction indications are sufficient to warrant a change in a diagnostic algorithm (e.g.,318of method300and358of method340).

If an updated diagnostic algorithm is generated at the cloud database512, the updated diagnostic algorithm may be stored in the memory of cloud data base512(e.g., system memory630ofFIG.6). Alternatively, or additionally, the cloud database512may transmit the updated diagnostic algorithm (e.g., via network120) to one or more local computing devices (e.g., in accordance with process340) and/or monitoring and processing apparatuses102(e.g., in accordance with process300).

According to an example of the embodiments disclose herein, raw ECG signals from a patient104ofFIG.1may be sensed by sensor112of monitoring and processing apparatus102. The processor114of monitoring and processing apparatus102may be configured to identify components of the ECG waveform, such as the P wave, the QRS complex, the R wave, the Q wave, the T wave, and the U wave. The components may be identified after initial noise filtration, including removal of baseline wander. The identified components may be analyzed using a diagnostic algorithm stored in memory118. The diagnostic algorithm may be applied to quantify respective values of the components, such as the respective amplitudes, intervals and/or durations of the P wave, the QRS complex, and the T wave, the R wave and the Q wave, as well as the periods of the P-R interval, the Q-T interval, and the S-T interval. Other parameters that may be quantified may include the slopes of all or some of the waves, the absence or presence of fractionation, and the fractionation value if present.

The diagnostic algorithm may further apply weights to the quantified values, and may analyze the weighted values to generate, as an output, a diagnostic result based on the received ECG signals. The diagnostic result may be provided to a healthcare professional via a computer and the healthcare professional may indicate that the diagnostic result is incorrect based on a review of the raw ECG signal that is also provided to the healthcare professional via the computer. Accordingly, the healthcare professional may provide a correction indication which may provide the correct diagnostic result.

A remote computing device may receive the correction indication and may determine that the number of correction indications received based on the same application of the diagnostic algorithm exceeds a threshold correction indication. Accordingly, the weights contained within the diagnostic algorithm neural network may be updated such that an updated diagnostic algorithm what receives similar ECG signals, as those originally provided in this example, would produce a different diagnostic result. The updated diagnostic algorithm may be provided to one or more local computing devices and/or monitoring and processing apparatuses accordingly.

According to an embodiment, an updated diagnostic algorithm may be provided to a group of monitoring and processing apparatuses (e.g.,320of process300) and/or a group of local computing devices (e.g.,360of process340). The group of monitoring and processing apparatuses or local computing devices that receives an updated diagnostic algorithm (collectively referred to as distribution group) may be determined based on one or more factors. A distribution group may expand to include additional monitoring and processing apparatuses or local computing devices if an initial distribution of an updated diagnostic algorithm to a smaller distribution group yields a successful result (e.g., lowers the number of correction indications received from within the smaller distribution group). A distribution group may be determined based on the location of corresponding monitoring and processing apparatuses or local computing devices, based on license settings, based on patient volume, based on local regulations and/or requirements, based on random selection, or the like.

According to an example, a remote computing device (e.g., remote computing device108ofFIG.1) may generate an updated diagnostic algorithm based on a threshold number of correction indications. The remote computing device may provide the updated diagnostic algorithm to a distribution group of100randomly selected local computing devices. The remote computing device may then monitor the correction indications received from those100randomly selected local computing devices. Notably, the remote computing device may monitor the specific type of correction indications received from the100randomly selected local computing devices and compare them to the correction indications that resulted in the updated diagnostic algorithm. Based on the monitoring, the remote computing device may determine whether the updated diagnostic algorithm lead to more desirable results. For example, the remote computing device may determine whether the updated diagnostic algorithm resulted in fewer correction indications. If the updated diagnostic algorithm leads to more desirable results, the distribution group may be expanded (e.g., to 1000 randomly selected local computing devices). The monitoring and expanding steps may be repeated for multiple iterations.

FIG.6is a system diagram of an example of a computing environment600in communication with network120. In some instances, the computing environment600is incorporated in a public cloud computing platform (such as Amazon Web Services or Microsoft Azure), a hybrid cloud computing platform (such as HP Enterprise OneSphere) or a private cloud computing platform.

As shown inFIG.6, computing environment600includes remote computing system108(hereinafter computer system), which is one example of a computing system upon which embodiments described herein may be implemented.

The remote computing system108may, via processors620, which may include one or more processors, perform various functions. The functions may include analyzing monitored patient biometrics and the associated information and, according to physician-determined or algorithm driven thresholds and parameters, providing (e.g., via display666) alerts, additional information or instructions. As described in more detail below, the remote computing system108may be used to provide (e.g., via display666) healthcare personnel (e.g., a physician) with a dashboard of patient information, such that such information may enable healthcare personnel to identify and prioritize patients having more critical needs than others.

As shown inFIG.6, a computing environment600may include a communication mechanism such as a bus621or other communication mechanism for communicating information within the computing environment600. The computer system600further includes one or more processors620coupled with the bus621for processing the information. The processors620may include one or more CPUs, GPUs, or any other processor known in the art.

The computing environment600also includes a system memory630coupled to the bus621for storing information and instructions to be executed by processors620. The system memory630may include one or more diagnostic algorithms and may include multiple versions of one or more diagnostic algorithms. The system memory630may include computer readable storage media in the form of volatile and/or nonvolatile memory, such as read only system memory (ROM)631and/or random access memory (RAM)632. The system memory RAM632may include other dynamic storage device(s) (e.g., dynamic RAM, static RAM, and synchronous DRAM). The system memory ROM631may include other static storage device(s) (e.g., programmable ROM, erasable PROM, and electrically erasable PROM). In addition, the system memory630may be used for storing temporary variables or other intermediate information during the execution of instructions by the processors620. A basic input/output system633(BIOS) may contain routines to transfer information between elements within computer system610, such as during start-up, that may be stored in system memory ROM631. RAM632may contain data and/or program modules that are immediately accessible to and/or presently being operated on by the processors620. System memory630may additionally include, for example, operating system634, application programs635, other program modules636and program data637.

The computing environment600also includes a disk controller640coupled to the bus621to control one or more storage devices for storing information and instructions, such as a magnetic hard disk641and a removable media drive642(e.g., floppy disk drive, compact disc drive, tape drive, and/or solid state drive). The storage devices may be added to the computer system610using an appropriate device interface (e.g., a small computer system interface (SCSI), integrated device electronics (IDE), Universal Serial Bus (USB), or FireWire).

The computing environment600may also include a display controller665coupled to the bus621to control a monitor or display666, such as a cathode ray tube (CRT) or liquid crystal display (LCD), for displaying information to a computer user. The illustrated computer system610includes a user input interface660and one or more input devices, such as a keyboard662and a pointing device661, for interacting with a computer user and providing information to the processor620. The pointing device661, for example, may be a mouse, a trackball, or a pointing stick for communicating direction information and command selections to the processor620and for controlling cursor movement on the display666. The display666may provide a touch screen interface that may allow input to supplement or replace the communication of direction information and command selections by the pointing device661and/or keyboard662.

The computing environment600may perform a portion or each of the functions and methods described herein in response to the processors620executing one or more sequences of one or more instructions contained in a memory, such as by utilizing one or more diagnostic algorithms in the system memory630. Such instructions may be read into the system memory630from another computer readable medium, such as a hard disk641or a removable media drive64. The hard disk641may contain one or more data stores and data files used by embodiments described herein. Data store contents and data files may be encrypted to improve security. The processors620may also be employed in a multi-processing arrangement to execute the one or more sequences of instructions contained in system memory630. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions. Thus, embodiments are not limited to any specific combination of hardware circuitry and software.

The computing environment600may further include a networked environment using logical connections to local computing device106and one or more other devices, such as a personal computer (laptop or desktop), mobile devices (e.g., patient mobile devices), a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to computer system610. When used in a networking environment, computer system610may include modem672for establishing communications over a network120, such as the Internet. Modem672may be connected to system bus621via network interface670, or via another appropriate mechanism.

Network120, as shown inFIGS.1and6, may be any network or system generally known in the art, including the Internet, an intranet, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a direct connection or series of connections, a cellular telephone network, or any other network or medium capable of facilitating communication between computer system610and other computers (e.g., local computing device106).