Patent Description:
Various embodiments of the present disclosure relate generally to displaying patient health data. More specifically, particular embodiments of the present disclosure relate to systems and methods for obtaining and processing patient health data, such as electrocardiograph (ECG) data, and displaying the data to a healthcare professional on a handheld device.

Remote monitoring of ambulatory patients enables doctors to detect or diagnose heart problems, such as arrhythmias, that may produce only transient symptoms and, therefore, may not be evident when the patients visit the doctors' offices. Several forms of cardiac event monitors have been used.

A "Holter" monitor is worn by a patient and collects and stores data for a period of time. typically at least <NUM> hours, and in some cases up to two weeks. After the data has been collected, the Holter monitor is typically brought or sent to a doctor's office, laboratory or the like, and the data is retrieved from the monitor and analyzed. Holter monitors are relatively inexpensive, but they cannot be used for real-time analysis of patient data, because the data is analyzed hours, days or weeks after it has been collected.

More timely analysis of heart data is made possible by presymptom (looping memory) event monitors. Such a device collects and stores patient data in a "loop" memory device that constantly overwrites previously stored data with newly collected data. The event monitor may include a button, which the patient is instructed to actuate if the patient feels ill or otherwise detects a heart-related anomaly. In response, the event monitor continues to record data for a short period of time and then stops recording, thereby retaining data for a time period that spans the button actuation. The retained data may then be sent via a modem and a telephone connection to a doctor's office or to a laboratory for analysis.

Mobile cardiovascular telemetry (MCT) refers to a technique that involves noninvasive ambulatory cardiac event monitors that are capable of continuous measurements of heart rate and rhythm over several weeks. For example, some MCT devices include an automatic electrocardiograph (ECG) arrhythmia detector that couples to a cellular telephone device to immediately transmit automatically detected abnormal ECG waveforms to a remote monitoring center, which can then alert a physician. Such devices also include a memory capable of storing ECG waveform data, which is transmitted to a cellular phone for analysis, and then to the remote monitoring center whenever an event is detected by the smartphone algorithms. Although data about automatically detected arrhythmias is sent immediately to the remote monitoring center, without requiring patient action, the computational resources and corresponding electrical power (battery) required to perform the automatic ECG analysis in the device are significant.

Some MCT devices continuously send all collected ECG data to a remote monitoring center for analysis. These MCT devices typically do not perform any ECG analysis of their own. Although no patient-initiated action is required, the large amount of data transmitted by the MCT wireless devices congests the wireless channels used to convey the data. Furthermore, a large amount of computational resources is required at the remote monitoring center to analyze the continuous stream of received data, especially when many patients are monitored by a single data center.

To improve the collection, transmission and processing of physiological data, InfoBionic of Lowell, MA has developed a novel system that collects high definition physiologic data, but sends a downsampled version of it to a remove server for the first-pass processing. When the remote server detects an arrhythmia, it requests the high resolution data from the transceiver for a second-pass analysis. Embodiments of this system are disclosed in <CIT>.

<CIT> discloses a computer method and apparatus for reading and analyzing ECG signals. The method includes applying a plurality of heart condition detectors to a subject's ECG data and generating a lattice annotated with likelihood indications of different respective heart conditions.

<CIT> discloses a system and method for analyzing physiological data to identify at least one morphological segment of the physiological data. A segment feature rating module is then used to identify at least one segment feature from the identified morphological segment and to rate the severity of the identified segment feature.

However, to date, regardless of how much ECG data is collected and analyzed, and whether ECG data is analyzed on a local or remote device, the resulting ECG data is typically presented to physicians in long, printed reports. Such printed reports of ECG data are static, and therefore do not include the latest ECG data obtained from a patient device, and are also not able to be manipulated by a reviewing physician. Moreover, printed reports are tedious to review and difficult to understand, which makes physicians less interested in reviewing those reports. As a result, review of printed reports of ECG data is sometimes delayed and/or delegated to junior physicians. Thus, while the systems and methods of the '<NUM> application address certain challenges associated with the collection and analysis of immense amounts of ECG data, a need remains for improved systems and methods for reporting and displaying collected and processed ECG data for a plurality of patients to healthcare professionals.

A method is disclosed for displaying patient ECG data. The method includes receiving ECG data including an ECG waveform; receiving analyzed ECG data to detect an arrhythmic event experienced by the patient; generating an indicia of the detected arrhythmic event; and displaying the indicia of the detected arrhythmic event in relation to the ECG waveform at a position associated with a time of the detected arrhythmic event.

The indicia of the detected arrhythmic event includes an indication of a detected severity of the detected arrhythmic event or an indication of a detected recency of the detected arrhythmic event. The indicia of the detected arrhythmic event has a size that increases based on a detected severity of the detected arrhythmic event, or a color or shape that changes based on a detected severity of the detected arrhythmic event.

The method further includes classifying the patient into one of a plurality of patient groups based on a detected severity or a detected recency of the detected arrhythmic event. The plurality of patient groups include: a first group of patients that have experienced a recent arrhythmic event, a second group of patients that have not experienced a recent arrhythmic event and a third group of patients that have completed a prescribed monitoring period. The method further includes generating a group indicia associated with each of the plurality of patient groups; wherein a size of a group indicia associated with the first group of patients is bigger than a size of a group indicia associated with the second group of patients, or a color of a group indicia associated with the first group of patients is brighter than a color of a group indicia associated with the second group of patients.

The method further includes generating a group indicia associated with each of the plurality of patient groups; and displaying the plurality of group indicia, each group indicia including an identification of one or more patients classified into the patient group of the group indicia. A color, shape, or size of each group indicia is changed based on a number of patients classified in the group, or a number or a severity of one or more detected arrhythmic events of patients classified in the group.

The method further includes generating a display of indicia of a plurality of patients, each indicia of each of the plurality of patients including an ECG waveform and indicia of a detected arrhythmic event associated with each respective patient; and sorting the displayed indicia of the plurality of patients based on a classifying of each of the plurality of patients into one of the plurality of patient groups. The method further includes generating a display of indicia of a plurality of patients, each indicia of each of the plurality of patients including an ECG waveform and indicia of a detected arrhythmic event associated with each respective patient; and sorting a sequence of the displayed indicia of the plurality of patients based on a number, a recency, or a severity of one or more detected arrhythmic events for each patient.

In a comparative example, the ECG data is received from a sensor associated with a patient. The method further includes receiving, from a physician, a request to view the ECG data; and transmitting, to the physician, one or more images that displays the indicia of the detected arrhythmic event in relation to the ECG waveform at a position associated with a time of the detected arrhythmic event.

The method further includes generating a group indicia associated with each of the plurality of patient groups, each group indicia being representative of a planet or astronomical object in the universe, and displaying the plurality of group indicia, each group indicia including an identification of one or more patients classified into the patient group of the group indicia.

A system is disclosed for displaying patient ECG data. The system includes a data storage device storing instructions for displaying patient ECG data; and a processor configured to execute the instructions to perform a method comprising: receiving ECG data including an ECG waveform; receiving analyzed ECG data to detect an arrhythmic event experienced by the patient; generating an indicia of the detected arrhythmic event; and displaying the indicia of the detected arrhythmic event in relation to the ECG waveform at a position associated with a time of the detected arrhythmic event.

The processor is further configured for: classifying the patient into one of a plurality of patient groups based on a detected severity or a detected recency of the detected arrhythmic event. The plurality of patient groups include: a first group of patients that have experienced a recent arrhythmic event, a second group of patients that have not experienced a recent arrhythmic event, and a third group of patients that have completed a prescribed monitoring period.

The processor is further configured for: generating a group indicia associated with each of the plurality of patient groups; wherein a size of a group indicia associated with the first group of patients is bigger than a size of a group indicia associated with the second group of patients, or a color of a group indicia associated with the first group of patients is brighter than a color of a group indicia associated with the second group of patients.

The processor is further configured for: generating a group indicia associated with each of the plurality of patient groups; and displaying the plurality of group indicia, each group indicia including an identification of one or more patients classified into the patient group of the group indicia. A color, shape, or size of each group indicia is changed based on a number of patients classified in the group, or a number or a severity of one or more detected arrhythmic events of patients classified in the group.

The processor is further configured for: generating a display of indicia of a plurality of patients, each indicia of each of the plurality of patients including an ECG waveform and indicia of a detected arrhythmic event associated with each respective patient; and sorting the displayed indicia of the plurality of patients based on a classifying of each of the plurality of patients into one of the plurality of patient groups.

The processor is further configured for: generating a display of indicia of a plurality of patients, each indicia of each of the plurality of patients including an ECG waveform and indicia of a detected arrhythmic event associated with each respective patient; and sorting a sequence of the displayed indicia of the plurality of patients based on a number, a recency, or a severity of one or more detected arrhythmic events for each patient.

The ECG data is received from a sensor associated with a patient. The processor is further configured for: receiving, from a physician, a request to view the ECG data; and transmitting, to the physician, one or more images that displays the indicia of the detected arrhythmic event in relation to the ECG waveform at a position associated with a time of the detected arrhythmic event.

The processor is further configured for generating a group indicia associated with each of the plurality of patient groups, each group indicia being representative of a planet or astronomical object in the universe, and displaying the plurality of group indicia, each group indicia including an identification of one or more patients classified into the patient group of the group indicia.

Reference will now be made in detail to the exemplary embodiments of the disclosure, examples of which are illustrated in the accompanying drawings.

The objects and advantages of the disclosed embodiments will be realized and attained by means of the elements and combinations particularly pointed out in the appended claims.

In view of the challenges outlined above, systems and methods are disclosed for remote physiologic monitoring of a body of a patient, in association with a remote server. In one embodiment, the system may include a plurality of sensors and a transceiver assembly. Each sensor of the plurality of sensors may be configured to be coupled to the body of the patient to generate respective physiologic data about the body. The transceiver assembly may include a. memory, a controller and a wireless transceiver. The transceiver assembly may be communicatively coupled to the plurality of sensors. The transceiver assembly may be configured to receive the physiologic data from the plurality of sensors. The transceiver assembly may also be configured to store the received physiologic data in the memory.

In one embodiment, the transceiver and sensors may be configured to collect patient ECG data according to the embodiments and methods described in the '<NUM> application (described above). In particular, the transceiver assembly may be configured to send a subset of the received physiologic data (referred to as "less detailed data"), via the wireless transceiver, to the remote server. The less detailed data sent to the remote server may be characterized by a lower resolution than some "more detailed data" stored in the memory for a corresponding time period and/or a lower sampling rate than the more detailed data stored in the memory for a corresponding time period, and/or having been received from a different set of the sensors than the more detailed data stored in the memory for a corresponding time period. The transceiver assembly may be configured to fetch at least a portion of the more detailed physiologic data from the memory, in response to a signal from the remote server. In addition, in response to the signal from the remote server, the transceiver assembly is configured to send the fetched more detailed physiologic data to the remote server. The remote server may be configured to receive the less detailed physiologic data sent by the transceiver assembly and automatically analyze the received less detailed physiologic data for an indication of a health-related anomaly. If the health-related anomaly is indicated, the remote server may be configured to automatically send the signal to the transceiver assembly. The health-related anomaly may be or include an arrhythmia. In one embodiment, the wireless transceiver assembly may include a cellular telephone coupled via a short-range wireless link to the wireless transceiver. The cellular telephone may be configured to store the more detailed data in the memory, send the less detailed data to the remote server, responsive to the signal, fetch the at least the portion of the more detailed physiologic data from the memory, and send the fetched more detailed physiologic data to the remote server via a wireless carrier network. Although, the presently disclosed embodiments may be used with the "more detailed data" and "less detailed data" collection scheme described above and in the '<NUM> application, the presently disclosed embodiments may be used in relation to any remote arrhythmia detection system, regardless of the quantity of ECG or arrhythmia data collected.

Referring now to the enclosed figures, <FIG> is a schematic diagram of a system and environment for collecting, processing, and displaying ECG data, according to an exemplary embodiment of the present disclosure. As shown in <FIG>, the system and environment may include a plurality of physician devices <NUM> and patient devices <NUM> disposed in communication with an electronic network <NUM>. Electronic network <NUM> may be the Internet, or any other combination of wired and/or wireless electronic networks.

In one embodiment, each of physician devices <NUM> may include a server, personal computer, tablet computer, mobile device, smartphone, and/or personal digital assistant ("PDA") disposed in communication with electronic network <NUM>. For example, in one embodiment, each of physician devices <NUM> may be a touchscreen enabled device, such as an Apple iPad, Samsung Galaxy, Amazon Kindle, Microsoft Surface, or any other equivalent or similar device. Each of physician devices <NUM> may have a web browser or mobile browser installed for receiving and displaying content from web servers. Thus, each of physician devices <NUM> may be configured to receive and display data that is received and processed from patient devices <NUM>, over electronic network <NUM>.

In one embodiment, each of patient devices <NUM> may include a combination of physiologic sensors, a memory, a battery, and/or a transceiver, one or more of which may be disposed within or in communication with a mobile device, such as a smartphone, PDA, or other handheld or wearable electronic device. In one embodiment, the physiologic sensors may be disposed in short-range wireless, Bluetooth, radio-frequency (RFID), and/or near-field communications (NFC) communication with a mobile device carried or worn by the patient. Each of patient devices <NUM> may be connected to electronic network <NUM> through a cellular network and/or a Wi-Fi network. Thus, each of patient devices <NUM> may be configured to collect physiological data from a patient, and transmit collected physiological data over electronic network <NUM>. Each of patient devices <NUM> may also have a web browser or mobile browser installed for receiving and displaying content from web servers.

As shown in <FIG>, a plurality of server systems <NUM>, a browser web server <NUM>, and/or a mobile web server <NUM> may also be disposed in communication with electronic network <NUM>. In one embodiment, server systems <NUM> may be configured to receive physiological data from patient devices <NUM> over electronic network <NUM>. Any of the devices or functionality of server systems <NUM>, browser web server <NUM>, and/or a mobile web server <NUM> may be combined together or separated, and may be operated by a single administrative entity, or outsourced to one or more other entities, such as a web hosting entity, web storage entity, and/or cloud computing service.

As shown in the embodiment of <FIG>, server systems <NUM> may include a physiological data analyzer <NUM>, which may be configured to perform high-sensitivity analysis and high specificity analysis on received physiological data. Specifically, physiological data analyzer <NUM> may be configured to analyze received physiological data for detecting arrhythmic events, determine a severity of any detected arrhythmic events, and/or perform any other analysis, classification, and/or sorting of detected arrhythmic events and/or patients having experienced arrhythmic events, as will be described in more detail below.

Server systems <NUM> may also include one or more databases <NUM>, where data analyzer <NUM> may be configured to store the received physiological data. As described above with respect to the '<NUM> application, server system <NUM> may be configured to receive and store either "less detailed data" and/or "more detailed data," or a portion thereof. Any received data may be stored in the databases <NUM> in an encrypted form to increase security of the data against unauthorized access.

Server systems <NUM> may also include a physician application program <NUM> that allows a physician to control parameters of the system, such as threshold values used by the data analyzer <NUM> in performing high-sensitivity and/or high-specificity analyses. The physician application program <NUM> also displays data to the physician and allows the physician to select types of data to display, time periods of the data to display, levels of data detail to display and other operating parameters of the system. For example, the physician may select a beginning and ending time surrounding a suspected or verified arrhythmia for display. In response to a query by the physician, the physician application program <NUM> may fetch and display data from the databases <NUM>. If the requested data is not available in the databases <NUM>, or if the requested data is not available in the database <NUM> at the level of detail requested by the physician, the physician application program <NUM> may automatically communicate with the transceiver of a patient device <NUM> to fetch the appropriate data in the appropriate amount of detail.

The physician application program <NUM> may implement appropriate security protocols, such as requiring the physician to enter logon credentials, so as to appropriately limit access to patient data and comply with regulations, such as the Health Insurance Portability and Accountability Act (HIPAA).

As shown in <FIG>, server systems <NUM> may be disposed in communication with a browser web server <NUM> and/or a mobile web server <NUM>. Each of browser web server <NUM> and/or mobile web server <NUM> may be configured to interact with physician devices <NUM>, such as to accept user (physician, patient or administrator) inputs and generate appropriate displays to facilitate user interaction with the physician application program <NUM>. For example, browser web server <NUM> and/or mobile web server <NUM> may be configured to generate a window-metaphor based computer user interface on a screen of physician device(s) <NUM> or screen (not shown) coupled to the remote server systems <NUM>, or the browser web server <NUM> and/or mobile web server <NUM> may generate web pages that are rendered by a browser or application of the physician devices <NUM>. The physician devices <NUM> and the browser web server <NUM> and/or mobile web server <NUM> may communicate with each other using an appropriate encrypted protocol, such as Hypertext Transfer Protocol Secure (HTTPS).

<FIG> is a flow diagram of a method <NUM> for collecting, processing, and displaying ECG data, e.g., using the exemplary system and devices of <FIG>, according to an exemplary embodiment of the present disclosure. As shown in <FIG>, method <NUM> may initially include receiving ECG data from one or more patients (step <NUM>). For example, server systems <NUM> may receive ECG data from one or more patient devices <NUM>, which may then be stored in databases <NUM>. In one embodiment, patient devices <NUM> may include or may be disposed in communication with a plurality of sensors.

<FIG> is a schematic diagram of a device, e.g., sensors, positioned on a patient torso for collecting patient ECG data, according to an exemplary embodiment of the present disclosure. Specifically, <FIG> is a schematic diagram illustrating one possible combination of physiological sensors <NUM>, <NUM> and <NUM> and a possible placement of the sensors on a torso <NUM> of a patient. One of the sensors <NUM> may be attached at about the elevation of the diaphragm of the patient. Each sensor <NUM>-<NUM> may be attached to the torso <NUM> using known gel pads or other conventional attachment techniques. Any combination of known physiological electrodes may be used for the sensors <NUM>-<NUM>. For example, the sensors <NUM>-<NUM> may include any combination of SpO2 sensors, blood pressure sensors, ECG electrodes, respiration sensors, movement and activity sensors, and the like. Movement or activity may be sensed with appropriate accelerometers or gyroscopes, such as micro electromechanical system (MEMS) devices. The sensors <NUM>-<NUM> may be connected via wires or optical cables <NUM> and <NUM> or via wireless links, such as Bluetooth links. Respiration data may be derived from ECG baseline data, as is known to those of skill in the art. Optionally, other sensors, such as a patient weight measuring device, blood pressure cuff, etc., may be disconnectably coupled via wires, optical cables or wirelessly to a transceiver assembly of patient devices <NUM>. Thus, as discussed above, patient devices <NUM> may be configured to collect physiologic data, store the collected data in a memory, and send a full detail or less-detailed version of the data to the remote server systems <NUM> for storage in databases <NUM>.

Referring now back to <FIG>, method <NUM> includes processing the received ECG data (step <NUM>). In one embodiment, processing the received ECG data includes detecting arrhythmic events (step <NUM>). For example, the ECG data may be processed by the data analyzer <NUM> to automatically classify heartbeats using morphology and heartbeat interval features, as described by <NPL>. In other words, collected data may be processed before a determination is made whether an anomaly has been detected. As noted, arrhythmia may be suspected or verified (or both) using ECG data, non-ECG data, or a combination thereof. For example, an arrhythmia may be suspected or verified, based in whole or in part on respiration rate. The respiration rate may be determined based on data from one or more accelerometers in the sensors attached to the torso of the patient, as shown for example in <FIG>. Chest movements detected by the accelerometers may be filtered, such as within expected frequencies and amplitudes, to derive the respiration rate. For example, one accelerometer may be included in the sensor <NUM> (<FIG>), which is located adjacent the patient's diaphragm, and another accelerometer may be include in the sensor <NUM> or <NUM>. Relative motion between the two locations on the torso <NUM> represented by the two accelerometers closely represents diaphragm movement and, therefore, breathing.

In addition to detecting arrhythmic events, the processing of the ECG data (of step <NUM>) also includes generating an indicia of each detected arrhythmic event (step <NUM>). For example, any type of indicia, such as an icon of a box, circle, planet, sphere, or any other shape may be generated to represent a detected arrhythmic event, as will be described in more detail below. In one embodiment, the generated indicia of the detected arrhythmic event may include an indication of a detected severity of the detected arrhythmic event or an indication of a detected recency of the detected arrhythmic event. In yet another embodiment, the generated indicia of the detected arrhythmic event may have a size that increases based on a detected severity of the detected arrhythmic event, or a color or shape that changes based on a detected severity of the detected arrhythmic event.

The processing of the ECG data (of step <NUM>) also includes associating the generated indicia with patient ECG data (step <NUM>). This allows for the indicia of a detected arrhythmic event to be displayed in relation to the ECG waveform of a patient, at a position associated with a time of the detected arrhythmic event.

The processing of the ECG data (of step <NUM>) alos includes categorizing patients based on the detected arrhythmic events (step <NUM>). Method <NUM> includes classifying each patient into one of a plurality of patient groups based on at least a detected recency of the detected arrhythmic event. In one embodiment, the plurality of patient groups may include a first group of patients that have experienced a recent arrhythmic event, a second group of patients that have not experienced a recent arrhythmic event, and a third group of patients that have completed a prescribed monitoring period. In one embodiment, a size of a group indicia associated with the first group of patients may be bigger than a size of a group indicia associated with the second group of patients, or a color of a group indicia associated with the first group of patients may be brighter than a color of a group indicia associated with the second group of patients.

The processing of the ECG data (of step <NUM>) may also include sorting patients based on the detected arrhythmic events (step <NUM>). For example, method <NUM> may include generating a display of indicia of a plurality of patients, each indicia of each of the plurality of patients including an ECG waveform and indicia of any detected arrhythmic event associated with each respective patient, and sorting the displayed indicia of the plurality of patients based on a classifying of each of the plurality of patients into one of the plurality of patient groups. In one embodiment, method <NUM> may include sorting a sequence of the displayed indicia of the plurality of patients based on a number, a recency, or a severity of one or more detected arrhythmic events for each patient.

Method <NUM> may further include, either concurrently with or asynchronously from processing the ECG data, receiving a request for ECG data from a physician (step <NUM>). For example, a physician may use a browser or other software installed on a physician device <NUM> to generate a request for ECG data from browser web server <NUM>, mobile web server <NUM>, and/or server systems <NUM>. The physician may generate the request by simply manipulating a user interface, such as touching a user element associated with a patient for whom the physician desires to review ECG data. Alternatively, the physician may request ECG data for all of the physician's patients, or all of the patients of the physician's practice (e.g., the physician's patients and patients of the physician's partners, nurse practitioners, residents, supervising physicians, etc.).

Method <NUM> may further include transmitting the processed ECG data to a physician (step <NUM>). For example, method <NUM> may include transmitting one or more images of ECG data and/or processed ECG data to the physician device <NUM> over electronic network <NUM>. Transmission of ECG data may include displaying an ECG waveform for one or more patients to the physician. Transmission of ECG data may also include displaying indicia of detected arrhythmic events, indicia of groups of patients, and/or indicia of groups of arrhythmic events. For example, method <NUM> may include displaying to the physician a plurality of group indicia generated for each of a plurality of patient groups, where each group indicia may include an identification of one or more patients classified into the patient group of the group indicia.

Method <NUM> may also include receiving an input from a physician to modify a display of ECG data (step <NUM>). For example, method <NUM> may include receiving an input from a physician based on the physician's manipulation of a user element of a user interface of a physician device <NUM>. In one embodiment, the input may include a swiping, squeezing, or pinching a display of an ECG waveform associated with a patient. <FIG> contains a hypothetical ECG waveform <NUM>, representing detailed data collected from the sensors of patient devices <NUM>. In one embodiment, the collected data may have a relatively high sampling rate and a relatively high resolution. Alternatively or additionally, the collected data may be down-sampled or have lower resolution. As shown in <FIG>, the waveform <NUM> may include a portion <NUM>, during which the waveform is anomalous, e.g., representing a detected arrhythmia.

Method <NUM> may then include modifying a display of ECG data based on received physician input (step <NUM>). For example, a displayed waveform <NUM> may be advanced through time, expanded to cover more time, or compressed to "zoom in" on a shorter interval of time, as will be shown in more detail with respect to the exemplary physician interface screenshots of <FIG>.

Exemplary embodiments of an application operating on physician devices <NUM> will now be described with reference to the screenshots depicted in <FIG>. It will be appreciated that the screenshots are only exemplary, and that any desired user interface, touch interface mobile application, user elements, or manipulatable icons or shapes may be used to execute the method of <FIG>.

<FIG> is a screenshot of a physician interface for logging into an application for reviewing patient ECG data. As discussed above, the log-in interface of <FIG> may appropriately limit access to patient data and comply with regulations, such as the Health Insurance Portability and Accountability Act (HIPAA).

<FIG> is another screenshot of a physician interface for reviewing patient ECG data, according to an exemplary embodiment of the present disclosure. As shown in <FIG>, patients may be classified into one of a plurality of patient groups based on a number, recency, and/or severity of detected arrhythmic events. For example, as shown in <FIG>, patient groups may be represented by indicia, in this case, a plurality of indicia having different colors and sizes. In one embodiment, the patient groups may include a first group for "review," including patients that have experienced an arrhythmic event within a recent time threshold, such as within the past month, week, or day, or since the physician last reviewed the interface of <FIG>. Patient groups may include a second group for "continuing," including patients that have not experienced an arrhythmic event within a recent time threshold, such as within the past month, week, or day, or since the physician last reviewed the interface of <FIG>. Patient groups may include a third group for "convert," or "diagnosis," including patients that have completed a prescribed monitoring period.

As shown in <FIG>, in one embodiment, the physician interface may include a "galaxy" interface <NUM> including a plurality of planets functioning as indicia of each patient group, including a "review" planet <NUM> including three patients (represented as moons of the review planet) that have experienced arrhythmic events within a specified time period; a "convert or diagnose" planet <NUM> including six patients (represented as moons of the convert or diagnose planet) that have completed their prescribed monitoring period; and a "continuing" planet <NUM> including <NUM> patients (represented as moons of the continuing planet) that have not experienced arrhythmic events within a specified time period. Also as shown in <FIG>, the "review" patient group may be represented by a relatively large and/or bright indicia, relative to a smaller and/or darker "convert or diagnose" patient group, and still smaller and/or darker "continuing" patient group. In one embodiment, all of the patients reflected in the plurality of patient groups of the "galaxy" interface of <FIG> may be patients of a single physician. In one embodiment, a number or other indicia may be included on each patient indicia to indicate a number of recent or total arrhythmic events experienced by the respective patient. For example, as shown in <FIG>, the moon associated with each patient may have a number that indicates the number of arrhythmic events experienced by the patient since the patient began a monitoring period. Such a number could also or alternatively indicate a number of arrhythmic events experienced by the patient since the physician reviewed the "galaxy" interface. Again, it will be appreciated that the galaxy/planet/moon theme is only one of many suitable themes for indicia that change in color, size, and/or shape to indicate the identity and/or contents of a plurality of patient groups, based on number, severity, and/or recency of detected arrhythmic events.

<FIG> is another screenshot of a physician interface for reviewing patient ECG data, according to an exemplary embodiment of the present disclosure. <FIG> reflects that a physician may view a plurality of interfaces <NUM>, <NUM>, <NUM>, <NUM> (e.g., separate "galaxy" interfaces as shown in <FIG>), where each interface displays the patients of a different physician. Thus, in the "universe" view of <FIG>, a physician may review the "galaxy" view of each physician in the physician's practice, cohort, or other collaborative group. The view of <FIG> provides a highly engaging, simple, and effective way for a physician to quickly identify patients of concern across a plurality of patients under the care of several physicians.

<FIG> is another screenshot of a physician interface for reviewing patient ECG data, according to an exemplary embodiment of the present disclosure. Physician interface <NUM> of <FIG> may be configured to display ECG data and detected arrhythmic event indicia for a plurality of patients. In addition, interface <NUM> may be configured to sort ECG data and detected arrhythmic event indicia for the plurality of patients based on a classification of the patients into one or more of the patient groups described above, including e.g. a "review" group, "convert or diagnose" group, and "continuing" group. In one embodiment, interface <NUM> may first display, at a top of the interface, the patient ECG indicia <NUM> for patients in the "review" group because those patients have experienced an arrhythmic event within a threshold time period, and are therefore of most concern. Interface <NUM> may next display, after patients in the "review" group, the patient ECG indicia <NUM> for patients in the "convert or diagnose" group because those patients have completed their prescribed monitoring period, and should therefore be diagnosed or converted to a different type of monitoring device or treatment. Interface <NUM> may finally display, at the bottom of the interface, the patient ECG indicia <NUM> for patients in the "continuing" group because those patients have not experienced an arrhythmic event within a threshold time period, and are therefore of relatively less concern.

As shown in <FIG>, the ECG indicia for each patient may include a plurality of different combinations of data, indicia of data, and/or indicia of conditions, e.g., arrhythmic events. For example, in one embodiment, each patient's ECG indicia may include a representation of a raw, received ECG waveform, a heart rate trend line <NUM>, and indicia of any detected arrhythmic events displayed in relation to the ECG waveform at a position associated with a time of the detected arrhythmic event. In one embodiment, the indicia of each detected arrhythmic event may change based on a severity, type, or recency of the detected arrhythmic event. For example, as shown in <FIG>, each arrhythmic event is shown either as a major event <NUM> (represented by a large, dark red circle), a moderate event <NUM> (represented by a medium red circle), or a minor event (represented by a small grey circle). Of course, it will be appreciated that detected arrhythmic events may be represented by any size, color, or shape of indicia, and that the size, color, or shape of the indicia may be changed in any desired way depending on any number of parameters, such as severity, type, or recency of the detected arrhythmic event. Thus, the interface <NUM> of <FIG> provides physicians with a useful, effective, and engaging way to review numerous patients under the physician's care, where the patients with the most recent and/or severe detected arrhythmic events are displayed more prominently than other patients with less recent or severe detected arrhythmic events.

<FIG> is another screenshot of a physician interface for reviewing patient ECG data, according to an exemplary embodiment of the present disclosure. Specifically, <FIG> depicts an interface <NUM> which provides a somewhat more detailed view of a physician interface for reviewing a patient's ECG data <NUM>, while still also providing a limited view of ECG data <NUM> for other patients under the physician's care. In one embodiment, the ECG data <NUM> for other patients may resemble the ECG data displayed in the overview interface <NUM> of <FIG>, while a more detailed ECG data <NUM> is displayed for the selected patient. The physician may request and therefore receive the interface view of <FIG> by tapping or otherwise selecting one of the patients in the interface view of <FIG> (e.g., the physician tapped or selected "Eric Benoit" in the view of <FIG> to obtain the interface view of <FIG>). The physician may switch from a detailed view of one patient to a detailed view of another patient by tapping, swiping, or otherwise selecting one of the other patients <NUM> displayed in interface <NUM>.

As shown in <FIG>, in the detailed view of interface <NUM>, additional ECG or other health data may be displayed for the selected patient, including a categorized list of arrhythmic events <NUM> (e.g., "SVT" [supraventricular tachycardia], "VT" [ventricular tachycardia], "Pauses," and "Bradycardia"), and heart rate parameters <NUM> (e.g., beats per minute ("bpm"), average bpm, and maximum bpm). Also, as in the interface <NUM>, interface <NUM> may display the ECG waveform, heart rate trendline <NUM>, and indicia of detected arrhythmic events in relation to the ECG waveform at a position associated with a time of the detected arrhythmic event. The indicia of detected arrhythmic events may include minor event indicia <NUM>, moderate event indicia <NUM>, and major event indicia <NUM>. In addition to what is displayed in interface <NUM>, the more detailed interface <NUM> may also display for the selected patient an extended ECG waveform <NUM>, which may be a "zoomed-in" display of a subset selection <NUM> of an even more extended duration ECG waveform <NUM>. The physician may then slide subset selection <NUM>, as defined, e.g., by a shaded portion or bracket along extended duration ECG waveform <NUM> to change the displayed portion of zoomed-in waveform <NUM>. The physician may also use various input methods to expand or compress the subset selection <NUM>, such as by squeezing or pinching a touchscreen interface of the physician device <NUM>. In one embodiment, various portions of the extended waveform <NUM> or waveform <NUM> may be highlighted, darkened, bolded, or otherwise indicated as being associated with an arrhythmic event. Accordingly, the interface <NUM> may prompt a physician to investigate and review various heart parameters, statistics, and ECG data associated with events of significant import.

<FIG> is another screenshot of a physician interface for reviewing patient ECG data, according to an exemplary embodiment of the present disclosure. Specifically, <FIG> depicts an even more detailed interface <NUM> of a specific patient under review. Interface <NUM> may again include arrhythmic events <NUM> (e.g., "SVT," "VT," "Pauses," and "Bradycardia"), and heart rate parameters (e.g., beats per minute ("bpm"), average bpm, and maximum bpm), but also patient-triggered complaints <NUM> (e.g., "chest discomfort," "palpitations," "dizziness," etc.) associated with each arrhythmic event. In addition, detailed interface <NUM> may also categorize detected arrhythmic events into day/night events <NUM>, based on the number and/or duration of the events, to assist a physician in associating events with certain day or night activities, and for facilitating proposed treatments or interventions. As in the interface <NUM> of <FIG>, interface <NUM> may also include a heartrate trendline <NUM>, and an extended ECG waveform <NUM> defined by subset selection <NUM> of further extended ECG waveform <NUM>. Also, as in interface <NUM>, a physician may interact with a touchscreen of physician device <NUM> to manipulate subset selection <NUM> to identify, investigate and review various heart parameters, statistics, and ECG data associated with arrhythmic events of significant import.

Although embodiments of the present invention have been described as detecting and verifying suspected arrhythmias, other embodiments may be similarly configured and used to detect and verify other health or fitness conditions, such as inappropriate insulin level, respiration, blood pressure, SpO2, body movement, exertion and the like.

A remote health monitoring system may include a processor controlled by instructions stored in a memory. For example, the transceiver assembly may include and be controlled by such a processor, and the remote server may be controlled by another such processor. The memory may be random access memory (RAM), read-only memory (ROM), flash memory or any other memory, or combination thereof, suitable for storing control software or other instructions and data.

Some of the functions performed by the remote health monitoring system have been described with reference to flowcharts and/or block diagrams. Those skilled in the art should readily appreciate that functions, operations, decisions, etc. of all or a portion of each block, or a combination of blocks, of the flowcharts or block diagrams may be implemented as computer program instructions, software, hardware, firmware or combinations thereof.

Those skilled in the art should also readily appreciate that instructions or programs defining the functions of the present invention may be delivered to a processor in many forms, including, but not limited to, information permanently stored on non-writable storage media (e.g. read-only memory devices within a computer, such as ROM, or devices readable by a computer I/O attachment, such as CD-ROM or DVD disks), information alterably stored on writable storage media (e.g. floppy disks, removable flash memory and hard drives) or information conveyed to a computer through communication media, including wired or wireless computer network.

In addition, while the invention may be embodied in software, the functions necessary to implement the invention may optionally or alternatively be embodied in part or in whole using firmware and/or hardware components, such as combinatorial logic, Application Specific Integrated Circuits (ASICs), Field- Programmable Gate Arrays (FPGAs) or other hardware or some combination of hardware, software and/or firmware components.

Claim 1:
A method for displaying patient ECG data on at least one display device, the method comprising:
receiving, using an application program (<NUM>), ECG data including an ECG waveform from a database (<NUM>), wherein the application program (<NUM>) and the database (<NUM>) are comprised by a server system (<NUM>);
receiving, using a data analyzer (<NUM>) also comprised by the server system (<NUM>), analyzed ECG data including a detected arrhythmic event;
generating using the application program (<NUM>), an indicia of the detected arrhythmic event;
displaying, on the at least one display device, the indicia of the detected arrhythmic event in relation to the ECG waveform at a position associated with a time of the detected arrhythmic event;
characterised in that the method further comprises:
classifying, using the data analyzer (<NUM>), the patient into one of a plurality of patient groups based on at least a detected recency of the detected arrhythmic event, wherein the plurality of patient groups include at least a first group of patients that have experienced a recent arrhythmic event, and a second group of patients that have not experienced a recent arrhythmic event;
generating group indicia associated with each of the plurality of patient groups, wherein each group indicia includes a visual indication of the detected recency of the detected arrhythmia event; and
displaying, on the at least one display device, the group indicia associated with each of the plurality of patient groups.