WO2021258004A1

WO2021258004A1 - Systems and methods for remote monitoring and treatment of intraocular pressure disease

Info

Publication number: WO2021258004A1
Application number: PCT/US2021/038109
Authority: WO
Inventors: Mark L. Baum; Andrew E. Livingston
Original assignee: Harrow Ip, Llc
Priority date: 2020-06-19
Filing date: 2021-06-18
Publication date: 2021-12-23

Abstract

Some embodiments of the present disclosure disclose systems and methods for remote monitoring and treatment of intraocular pressure (IOP) disease. In some embodiments, IPO data of an IOP measurement of an eye of a patient taken via an at-home IOP device may be received at an IOP logic server. The IOP logic server may further receive patient auxiliary data from a user device of the patient, after which the IOP logic server may analyze the received IOP data based on the patient auxiliary data to generate a patient health indicator that is dependent on the patient auxiliary data and is also related to an ocular health of the patient. The IOP server data may the provide the patient health indicator to a health care provider. In some embodiments, the health care provider may be selected by the IOP logic server based on the analyses of the IOP data in view of the patient auxiliary data.

Description

SYSTEMS AND METHODS FOR REMOTE MONITORING AND TREATMENT OF

INTRAOCULAR PRESSURE DISEASE

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims priority to and the benefit of the U.S. Provisional Patent Application No. 63/041,344, filed June 19, 2020, titled “Systems and Methods for Remote Monitoring and Treatment of Intraocular Pressure Disease,” which is hereby incorporated by reference in its entirety as if fully set forth below and for all applicable purposes.

TECHNICAL FIELD

[0002] The present disclosure relates generally to systems and methods for remote monitoring and treatment of intraocular pressure disease, and more specifically, to augmenting intraocular pressure (IOP) measurements taken by patients with patient auxiliary data to support the generation of ocular health determinations for use in monitoring, diagnosing and treating the patients.

BACKGROUND

[0003] Measurements of pressure inside an eye, i.e., intraocular pressure (IOP), can indicate the presence of serious eye diseases such as glaucoma, which are associated with increased fluid pressure within the eye due to accumulated fluid that can damage optic nerves. Tonometry is a technique for measuring the pressure in an eye, including by measuring the resistance of the cornea to pressure or indentation, for example, by a tonometer.

BRIEF DESCRIPTION OF THE DRAWINGS

[0004] Figure 1 is an example diagram of a system for remote monitoring, diagnosis and treatment of intraocular pressure disease, according to some embodiments of the present disclosure.

[0005] Figure 2 is an example flowchart of a method for remote monitoring, diagnosis and treatment of intraocular pressure disease, according to some embodiments of the present disclosure.

[0006] Figure 3 is an example block diagram illustrating a computer system for implementing a method for remote monitoring, diagnosis and treatment of intraocular pressure disease, according to some embodiments of the present disclosure.

[0007] In the figures and appendix, elements having the same designations have the same or similar functions. BRIEF SUMMARY OF SOME OF THE EMBODIMENTS

[0008] The following summarizes some aspects of the present disclosure to provide a basic understanding of the discussed technology. This summary is not an extensive overview of all contemplated features of the disclosure, and is intended neither to identify key or critical elements of all aspects of the disclosure nor to delineate the scope of any or all aspects of the disclosure. Its sole purpose is to present some concepts of one or more aspects of the disclosure in summary form as a prelude to the more detailed description that is presented later.

[0009] In some embodiments of the present disclosure, methods and systems for remote monitoring, diagnosis and treatment of intraocular pressure disease are disclosed. In some embodiments, a method comprises receiving, at a processor, intraocular pressure (IOP) data of an IOP measurement of an eye of a patient taken via an at-home IOP device. Further, the method comprises receiving, at the processor and from a user device of the patient, patient auxiliary data including medical data of the patient different from the IOP data, environmental data related to an environment of the patient during the IOP measurement, personal data related to personal information of the patient and/or device data related to the user device of the patient. Further, the method comprises analyzing, via the processor, the received IOP data based on the received patient auxiliary data to generate a patient health indicator that is dependent on the patient auxiliary data and related to an ocular health of the patient. In addition, the method comprises providing, by the processor, the patient health indicator to a health care provider in some embodiments, the method further comprises identifying the health care provider based on results of the analyzing the received IOP data.

[0010] In some embodiments, a system for remote monitoring, diagnosis and treatment of intraocular pressure disease comprises a transceiver and a processor. In some embodiments, the transceiver is configured to receive intraocular pressure (IOP) data of an IOP measurement of an eye of a patient taken via an at-home IOP device. Further, the transceiver is configured to receive, from a user device of the patient, patient auxiliary data including medical data of the patient different from the IOP data, environmental data related to an environment of the patient during the IOP measurement, personal data related to personal information of the patient and/or device data related to the user device of the patient. In some embodiments, the processor is configured to analyze the received IOP data based on the received patient auxiliary data to generate a patient health indicator that is dependent on the patient auxiliary data and related to an ocular health of the patient. In some embodiments, the processor is further configured to provide the patient health indicator to a health care provider. In some embodiments, the processor can be further configured to identify the health care provider based on results of the analyzing the received IOP data. In some embodiments, the system can be an artificial intelligence (AI) neural network system. [0011] Some embodiments of the present disclosure disclose a non-transitory computer- readable medium (CRM) having program code recorded thereon. In some embodiments, the program code comprises code for causing an intraocular pressure (IOP) logic server to receive IOP data of an IOP measurement of an eye of a patient taken via an at-home IOP device. Further, the program code comprises code for causing the IOP logic server to receive, from a user device of the patient, patient auxiliary data including medical data of the patient different from the IOP data, environmental data related to an environment of the patient during the IOP measurement, personal data related to personal information of the patient and/or device data related to the user device of the patient. The program code further comprises code for causing the IOP logic server to analyze the received IOP data based on the received patient auxiliary data to generate a patient health indicator that is dependent on the patient auxiliary data and related to an ocular health of the patient. In addition, the program code comprises code for causing the IOP logic server to provide the patient health indicator to a health care provider. In some embodiments, the code for causing the IOP logic server to analyze the received IOP data based on the received patient auxiliary data can include a code for causing an artificial intelligence (AI) neural network server to analyze the received IOP data based on the received patient auxiliary data.

[0012] In some embodiments, the at-home IOP device can be a tonometer. In some embodiments, the user device can be a portable device including a smartphone, a tablet or a computer. In some embodiments, the IOP data can be received at the processor after the user device transmits the IOP data to the processor in response to receiving the IOP data from the at-home IOP device. In some embodiments, the IOP data can be transmitted to the processor directly by the at-home IOP device. In some embodiments, the patient health indicator can include information on health care treatments to at least partially restore the ocular health of the patient. In some embodiments, the analyzing the received IOP data based on the received patient auxiliary data can be performed by an artificial intelligence (AI) neural network comprising the processor.

DETAILED DESCRIPTION

[0013] Abnormal pressure within the eye may be a symptom of serious eye diseases, such as glaucoma which is associated with increased intraocular pressure (IOP) as a result of fluid build-up that may damage optic nerves. Tonometers are devices that are capable of measuring IOP in a patient’s eye, for example, by measuring cornea resistance to indentation or pressure by the tonometer. Health care providers such as physicians, nurses, etc., can use tonometers to measure the IOP in a patient’s eyes to diagnose and treat IOP-related diseases. These measurements, however, are performed when the patient visits the health care provider (in most cases about four times a year or so), and as such fail to capture fluctuations in the measurements that may occur over a shorter time period and can provide critical information relevant to the diagnosis and treatment IOP diseases. In some cases, such fluctuations may be related to changes in medications, a patient’s adherence to instructions provided by health care providers, or lack thereof, etc. IOP devices such as portable tonometers or tonometers designed for at-home monitoring can be used by patients themselves to collect IOP measurements, which can facilitate more frequent collection of IOP measurements. The IOP measurements can then be transmitted to a logic server (e.g., a logic server of the health care provider which may include a logic engine) via a user device that is coupled to or in communication with the IOP device, as discussed below. In some embodiments, the collected IOP measurements or any data related to health information of a patient may be transmitted to a health care provider for monitoring, diagnosis and/or treatment purposes in a manner that is compliant with legal regulations such as Health Insurance Portability and Accountability Act (HIPAA) that lay out strict requirements for the handling of sensitive health care data.

[0014] In some embodiments, the IOP measurements may further be augmented with patient auxiliary data that can be used by health care providers to generate ocular health determinations for diagnosing and treating the patients. The patient auxiliary data may include medical data related to the non-IOP health information of the patient (i.e., health information other than the IOP measurements), device data related to the device(s) used in collecting and/or transmitting the IOP, environmental data related to the environment of the patient (e.g., location, time, etc.) during the IOP measurements, personal data (e.g., location or residency, age, etc.) and/or the like. For example, the data related to the health information of the patient may include information about medications that are being taken by patients, such as medication type and the frequency with which the patients are taking the medications, etc. As another example, the device data may include information related to the settings of the IOP device during the collection of the IOP measurements, identifying information of the IOP device and/or user devices such as but not limited to the name, age, type, serial number, make, model, etc., of the IOP device and/or devices, respectively. In some aspects, the IOP measurements and the patient auxiliary data of a patient may be transmitted, via the user device, to the server of the health care provider, which may then analyze the IOP measurements in combination with the patient auxiliary data for further diagnosis and treatment of the patient. In some embodiments, the occurrence, or lack thereof, of transmission of the IOP measurements from a patient to a health care provider may be used by the health care provider as an indication of the patient’s compliance, or lack thereof, with the health care provider’s instructions to take the measurements. [0015] In some embodiments, the present disclosure discloses systems and methods configured to facilitate remote monitoring, diagnosis and treatment of intraocular pressure diseases, and in particular augmenting IOP measurements of a patient with patient auxiliary data to support the generation of ocular health determinations for use in monitoring, diagnosing and treating the patient. In some embodiments, the system may include an IOP device (e.g., tonometer) that is configured to measure IOP of an eye of a patient. The IOP device may include a component that is designed to put pressure onto the cornea of an eye, and the IOP device may then determine the IOP measurements of the eye based on the cornea’s resistance to the pressure. The system may also include a user device that is configured to retrieve the IOP measurements from the IOP device and transmit the same to a server of the health care provider. The user device may also transmit patient auxiliary data to the server to augment the IOP measurements and allow the health care provider to make ocular health determinations for monitoring, diagnosing and treating the patient. The user device can be a standalone communicator device, a personal device such as but not limited to a smartphone, a tablet, a computer, etc. The user device may be configured to retrieve or receive the IOP measurements from the IOP device either wirelessly or when wired or plugged into the IOP device. Further, the user device may include a user input interface (e.g., such as a touchscreen, voice recorder, etc.) to allow the patient to input data (e.g., the patient auxiliary data) into the user device. In some embodiments, the IOP device and the user device can be separate devices (e.g., or coupled but separable devices), and in other embodiments, the IOP device and the user device may be integrated into a single device.

[0016] In some embodiments, the system configured to facilitate remote monitoring, diagnosis and treatment of intraocular pressure diseases may also include an IOP logic server or servers including a logic engine configured to receive the IOP measurements and/or the patient auxiliary data from the user device. In some cases, the IOP logic server may be a cloud server that is HIPAA compliant, i.e., a cloud server that is configured to handle health care data such as the IOP measurements in a manner compliant with the requirements of HIPAA. The IOP logic server may also include an engine (e.g., logic engine) configured to analyze the received patient IOP measurements and/or patient auxiliary data and generate health outputs that can aid the health care provider in monitoring, diagnosing and/or treating the patient. In some aspects, the IOP logic server may be configured to intake and perform analyses on the IOP data and/or the auxiliary patient data to generate patient related outputs such as but not limited to status updates on availability of new the IOP measurements, patient health warnings if the analyses indicate potential patient health complications or danger, adherence indicators on whether the patient is adhering to health care provider instructions, treatment suggestions in view of the IOP measurements/patient auxiliary data, and/or the like. In some cases, the IOP logic server may generate adherence indicators without necessarily receiving the IOP measurements and/or the patient auxiliary data, as the IOP logic server may determine that the patient has failed to take instructed IOP measurements and/or patient auxiliary data if the IOP measurements and/or the patient auxiliary data are not received at the IOP logic server at pre-determined IOP measurements and/or the patient auxiliary data transmission times.

[0017] In some embodiments, the logic engine may include an artificial intelligence (AI) engine (e.g., neural newtork) that is trained on a training set of IOP measurements and patient auxiliary data to later make predictions when analyzing a test data of IOP measurements and patient auxiliary data. Artificial intelligence, implemented with neural networks and deep learning models, has demonstrated great promise as a technique for automatically analyzing real-world information with human-like accuracy. In general, such neural network and deep learning models receive input information and make predictions based on the same. Whereas other approaches to analyzing real- world information may involve hard-coded processes, statistical analysis, and/or the like, neural networks learn to make predictions gradually, by a process of trial and error, using a machine learning process. A given neural network model may be trained using a large number of training examples, proceeding iteratively until the neural network model begins to consistently make similar inferences from the training examples that a human might make. For example, the above -noted logic engine may be trained using a large number of IOP measurements and/or patient auxiliary data until the engine starts to make consistent predictions based on same or similar training data. The AI logic engine may then be used to intake and perform analyses of IOP measurements and/or patient auxiliary data to generate outputs that a health care provider can use to monitor, diagnose and treat a patient. In some cases, neural network models have been shown to outperform and/or have the potential to outperform other computing techniques in a number of applications.

[0018] In some embodiments, the method configured to facilitate remote monitoring, diagnosis and treatment of intraocular pressure diseases further includes the use of the above -noted system to perform IOP measurements of a patient and augment the IOP measurements with patient auxiliary data to support the generation of ocular health determinations by a logic server of the health care provider for use in monitoring, diagnosing and treating the patient. A patient with a portable or in-home IOP device such as a tonometer may perform IOP measurements using the IOP device, for example, as instructed by a health care provider. In some embodiments, the IOP device may automatically send to a user device or the user device may query the IOP device and download the IOP measurements wirelessly. In yet some embodiments, the user device may receive, retrieve or download the IOP measurements from the IOP device when the IOP device and the user device are connected or wired. For example, the user device may download the IOP measurements from the IOP device via a cable. In some cases, the patient may input the IOP measurements (e.g., as voice data, text data, etc.) into the user device via a user interface of the user device, etc.

[0019] In some embodiments, the user device may identify the medical data, the device data, the environmental data, the personal data and/or the like, that may be included in the patient auxiliary data for augmenting the IOP measurements gathered or taken by the IOP devices. In some embodiments, the patient auxiliary data may augment the IOP measurements by allowing the health care provider to properly interpret, via the logic server, the IOP measurements in view of or based on the patient auxiliary data. In other words, the analyses of the IOP measurements by the logic server may depend on the patient auxiliary data, as discussed below. The user device may obtain the patient auxiliary data from external devices coupled to the user device and/or from internal applications or sensors configured to gather such data. For example, the user device may obtain the patient’s medical data such as information related to the patient’s vital signs from external devices coupled to the user device that are configured to measure the vital signs. As another example, the user device may obtain device data such as location, operating system, browser type/version used by the user data to transmit the IOP measurements/patient auxiliary data, etc., by querying internal applications for the relevant or related data. For instance, the user device may query location applications executing on the user device to obtain the location of the user device itself.

[0020] In some embodiments, upon receiving the IOP measurements from the IOP device and/or identifying and compiling the patient auxiliary data, the user device may transmit to an IOP logic server (e.g., a cloud server including a logic engine) the received IOP measurements and/or patient auxiliary data. For example, the user device can be a standalone communicator device, a mobile device, a smartphone, etc., equipped with wireless communications capabilities and the user device may transmit the received IOP measurements and/or patient auxiliary data to the IOP logic server wirelessly. For instance, the IOP measurements and/or patient auxiliary data may be transmitted to the IOP logic server via a web browser executing on the user device. In some embodiments, the patient auxiliary data may include information related to the processes of the IOP measurements and patient auxiliary data gathering, as well as transmission thereof. For example, the patient auxiliary data may include information related to the date/time of the IOP measurements, the date/time of the transmission of the IOP measurements, and as mentioned above information related to the IOP device and/or the user device such as but not limited to device identification serial number, device type, device operating system, device browser version, and/or the like.

[0021] In some embodiments, the IOP logic server may then intake and analyze (for example, using an AI engine) the IOP measurements and/or patient auxiliary data to generate patient health output including but not limited to notifications, diagnosis, treatment options, and/or the like. For example, the notifications may include a status update to the health care provider that the patient has taken and uploaded new IOP measurements. As another example, the notifications may include alerts or warnings to the health care provider showing that the patient’ s IOP readings are outside a pre -defined range indicating healthy or acceptable IOP. In some cases, the notifications may include adherence notices indicating whether the patient is adhering to the health care provider’ s instructions to take IOP measurements. In some instances, the notifications may also be sent to the patient (for example, to inform the patient that the IOP data has been received at the cloud server (i.e., logic server) and can be accessed by the patient and/or the health care provider). In some embodiments, such adherence notices may be generated without IOP measurements being received at the IOP logic server, and the IOP logic server may generate the adherence notices when IOP measurements are not received at the IOP logic server despite a scheduled transmission from the user device to the IOP logic server. In some embodiments, the logic server may receive and use input from the health care provider in generating the patient health output. In some embodiments, the term health care provider includes persons and/or entities involved in providing health care services to the patient, including physicians, health care advocates, nurses, or even family members of the patient.

[0022] Figure 1 is a simplified diagram of a system for remote monitoring, diagnosis and treatment of intraocular pressure (IOP) diseases, and in particular a system configured for augmenting IOP measurements of a patient 180 with patient auxiliary data to support the generation of ocular health determinations for use in monitoring, diagnosing and treating the patient 180, according to some embodiments. The system includes an IOP device 160 configured to measure IOP of an eye of a patient 180, a user device 17 configured to receive the IPO measurements from the IOP device 160, augment the IOP measurements with patient auxiliary data and transmit the IOP measurements and the patient auxiliary data to an IOP logic server 100 that that includes a processor 110 coupled to memory 120. Operation of IOP logic server 100 is controlled by processor 110. And although IOP logic server 100 is shown with only one processor 110, it is understood that processor 110 may be representative of one or more central processing units, multi-core processors, microprocessors, microcontrollers, digital signal processors, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), graphics processing units (GPUs) and/or the like in IOP logic server 100. IOP logic server 100 may be implemented as a stand-alone subsystem, as a board added to a computing device, and/or as a virtual machine.

[0023] Memory 120 may be used to store software executed by IOP logic server 100 and/or one or more data structures used during operation of IOP logic server 100. Memory 120 may include one or more types of machine readable media. Some common forms of machine readable media may include floppy disk, flexible disk, hard disk, magnetic tape, any other magnetic medium, CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, and/or any other medium from which a processor or computer is adapted to read.

[0024] Processor 110 and/or memory 120 may be arranged in any suitable physical arrangement. In some embodiments, processor 110 and/or memory 120 may be implemented on a same board, in a same package (e.g., system-in-package), on a same chip (e.g., system-on-chip), and/or the like. In some embodiments, processor 110 and/or memory 120 may include distributed, virtualized, and/or containerized computing resources. Consistent with such embodiments, processor 110 and/or memory 120 may be located in one or more data centers and/or cloud computing facilities.

[0025] As shown, memory 120 includes an IOP logic engine 130 that may be used to implement and/or emulate the software (e.g., automated software) and neural network systems and models described further herein and/or to implement any of the methods described further herein, such as but not limited to the method described with reference to FIG. 2. The IOP logic engine 130 may be used, in some examples, for analyzing the input 140 that includes the IOP measurements and patient auxiliary data received by the IOP logic server 100 to produce the output 150 that includes notifications, alerts, diagnosis/treatment determinations, and/or the like. For instance, the IOP logic engine 130 may include an artificial intelligence (AI) engine (e.g., neural network) that is trained on a training set of IOP measurements and patient auxiliary data to later generate the output 150, which may include notifications, alerts, diagnosis/treatment determinations, and/or the like as noted above, when provided with the input 140 of IOP measurements and patient auxiliary data as a test data. As another example, the IOP logic engine 130 may include a software that is configured to intake the input 140 and generate the output 150. For instance, the software may include machine-readable instructions that are executable to cause a machine to perform the steps of method 200 discussed below.

[0026] In some examples, memory 120 may include non-transitory, tangible, machine readable media that includes executable code that when run by one or more processors (e.g., processor 110) may cause the one or more processors to perform the methods described in further detail herein. In some examples, IOP logic engine 130 may be implemented using hardware, software, and/or a combination of hardware and software. As shown, IOP logic server 100 receives input 140, which is provided to IOP logic engine 130, which then may generate output 150.

[0027] In some embodiments, the input 140 may include the IOP measurements 192 performed or taken by the IOP device 160 and transmitted to the user device 170 for further transmission to the IOP logic server 100. In some cases, the user device 170 may further augment the received IOP measurements 192 with a patient auxiliary data and may transmit the IOP measurements 192 augmented with the patient auxiliary data 190 to the IOP logic server 100. In other words, the input 140 may include the IOP measurements 192 from the IOP device 160 and the patient auxiliary data from the user device 170 (e.g., both the IOP measurements 192 and the patient auxiliary data received via the user device 170). In some embodiments, instead of or in addition to transmitting the IOP measurements 192 to the user device 170, the IOP device 160 may directly transmit the IOP measurements 194 to the IOP logic server 100. In such cases, the input 140 may include the IOP measurements 194 directly from the IOP device 16 and the patient auxiliary data 190 directly from the user device 170.

[0028] In some embodiments, the output 150 can include results of analyses of the received input 140 (which may include the IOP measurements performed by the IOP device 160 and the patient auxiliary data obtained by the user device 170) by the IOP logic engine 130. For example, the output 150 may include status notifications about availability of IOP measurements and/or results of the analyses by the IOP logic engine 130, warnings/alerts about health conditions of the patient 180, adherence metrics measuring the adherence of the patient 180 to instructions from a health care provider, etc.

[0029] In some embodiments, the output 150 may be generated on-demand, i.e., a requesting entity such as one of the patient 180, the health care provider or other authorized entities (e.g., health insurance providers) may request for an output 150 (e.g., status notifications, results of the analyses by the IOP logic engine 130, warnings/alerts about health conditions of the patient 180, adherence metrics, etc.) and the IOP logic engine 130 may generate the output 150 as discussed above and provide the same to a device of the requesting entity. In some cases, the IOP logic engine 130 may provide the output 150 automatically without any request (e.g., based on a pre-determined arrangement where a requesting entity is entitled to receive some or all of the output 150 that the IOP logic engine 130 generates). In some embodiments, the IOP logic engine 130 may provide the output 150 to the patient 180, the health care provider or the other authorized entities, etc., on real-time basis, i.e., as the IOP measurements 194 and/or the patient auxiliary data 190 are transmitted to and received by the IOP logic engine 100.

[0030] Figure 2 is an example flow chart of a method 200 for remote monitoring, diagnosis and treatment of intraocular pressure disease. Steps of the method 200 can be executed by a computing device (e.g., a processor, processing circuit, and/or other suitable component) of a logic server or other suitable means for performing the steps. For example, a logic server, such as the IOP logic server 100, may utilize one or more components, such as the processor 110, the memory 120, the IOP logic engine 130 to execute the steps of method 200. As illustrated, the method 200 includes a number of enumerated steps, but embodiments of the method 200 may include additional steps before, after, and in between the enumerated steps. In some embodiments, one or more of the enumerated steps may be omitted or performed in a different order.

[0031] At step 210, the IOP logic server receives, at its processor, IOP data of an IOP measurement of a patient’s eye taken via an at-home IOP device. In some embodiments, the at-home IOP device can be a portable IOP device (e.g., portable tonometer) and the IOP measurement may be taken by the patient or a non-health care professional away from a health care facility using the portable IOP device. The IOP measurement may be made by applying pressure to the patient’s eye and measuring the resistance by the cornea to indentation or the pressure by the tonometer. The IOP measurements as such include data related to the pressure within the eye which, when analyzed, may provide indications or information about IOP diseases and ailments in the eye. For example, IOP measurements showing increased IOP in an eye may indicate the presence of serious eye diseases such as glaucoma, which is associated with increased fluid pressure within the eye due to accumulated fluid that can damage optic nerves. In some embodiments, the IOP device may not be portable, but may still be an at-home IOP device that a patient can use to perform an IOP measurement.

[0032] In some embodiments, the IOP logic server may receive the IPO measurement directly from the IOP device. For example, the IOP device may be equipped with a communications system (e.g., wireless communications system) that is in communication with a network that in turn is in communication with the IOP logic server, and as such the IOP device may transmit the IOP measurement to the IOP logic server via the network using the communications system. The network can be the Internet, representing a worldwide collection of networks and gateways to support communications between devices connected to the Internet. The network may include, for example, wired, wireless or fiber optic connections. The network can also be implemented as an intranet, a Bluetooth network, a local area network (LAN), or a wide area network (WAN). In general, the network can be any combination of connections and protocols that will support communications between computing devices, such as between IOP device 160, the user device 170 and the IOP logic server 140.

[0033] In some embodiments, in addition to or instead of transmitting the IOP measurement directly to the IOP logic server, the IOP device may transmit the IOP measurement to the user device for further transmission to the IOP logic server. For example, the user device may be equipped with a communications system (e.g., wireless communications system) that is in communication with the afore-mentioned network and the IOP device may transmit the IOP measurement to the user device via the network, which the user device may then in turn transmit to the IOP logic server with the patient auxiliary data, as discussed below. In some embodiments, the user device may be wired with the IPO device and may download or retrieve the IOP measurement from the IOP device via the wired connection. In some cases, the user device can be a standalone communicator device, a personal device such as but not limited to a smartphone, a tablet, a computer, etc.

[0034] At step 220, the IOP logic server receives, at its processor and from the user device of the patient, patient auxiliary data including medical data of the patient, environmental data related to an environment of the patient, personal data related to personal information of the patient and/or device data related to the user device of the patient. In some embodiments, the medical data may be medical information of the patient that may be different from the IOP measurement of the patient (e.g., although the medical data may be related to the ocular health of the patient). For example, the medical data may include information about medications the patient is taking, including medications for any ocular conditions, such information including information about type of medications and the frequency with which the patient is instructed to take the medications.

[0035] In some embodiments, the environmental data includes information about the environment of the patient during the IOP measurement. For example, such environmental data includes the location of the patient and/or the date/time when the IOP measurement is being administered. In some cases, the environmental data may include information environmental conditions that may affect the ocular health of a patient such as but not limited to various weather conditions of the location of the IOP measurement. Examples of such environmental data include temperature, humidity, ultraviolet (UV) index, elevation of the location, weather pressures, and/or the like.

[0036] In some embodiments, the personal data of the patient may include personal information of the patient such as age, gender, weight, height, race, etc., of the person that may be relevant in interpreting or analyzing the IOP measurements as discussed below. Further, the personal data may include historical information of the patient such as locations the patient has resided in for a significant period. In some cases, such information can be useful in interpreting the IOP measurements, and hence in understanding a patient’s ocular health. For example, a health care provider reviewing the IOP measurements may arrive at different conclusions regarding the ocular health of a patient if the personal information of the patient indicates that the patient has resided for a significant period of the patient’s life in the southwestern U.S. as opposed to, say, a region with tropical climate.

[0037] In some embodiments, the device data may include information about the user device and/or the IOP device such as but not limited to information about the device type, serial number, phone number or other identifying parameter associated with the device, device name, device settings, operating system executing on the device, location of the device, timing of the transmission of data from the user device (e.g., patient auxiliary data and/or the IOP measurements) to the IOP logic server, the type of the data (e.g., voice data, text data, etc.), signatures or identifiers of the connection between the user device and the network (e.g., IP address, etc.), and/or the like. In some cases, the device data may be configured to allow the health care provider or others with access to the device data to identify the identity of the patient (e.g., when the patient auxiliary data fails to include the patient personal data) from a database of patients.

[0038] In some embodiments, the patient auxiliary data may be provided or input into the user device by the patient, for example, via a user interface of the user device configured for receiving the patient auxiliary data. For instance, the patient may speak into a voice recorder of the user device to input the patient auxiliary data as a voice data. As another example, the patient may input the patient auxiliary data as text data into a web browser or web portal executing on the user device. That is, the patient may input into the user interface of the user device one or more of medical data of the patient, environmental data related to an environment of the patient, personal data related to personal information of the patient or device data related to the user device of the patient.

[0039] In some embodiments, the user device may obtain the patient auxiliary data from external devices coupled to or in communication with the user device and/or by querying an application executing on the user device. For example, the user device may be in communication with an external medical device that senses or measures a physiological parameter of the patient (e.g., an electronic thermometer configured to measure the temperature of the patient) and the user device may receive the measured physiological parameter as medical data from the external medical device. In some cases, the external medical device may be wearable electronics (e.g., smart watches, wearable electronic clothing, etc.) configured to gather biometric/physiologic data of a patient, and the user device may receive the gathered biometric/physiologic data as medical data from the wearable electronics. It is to be understood from the above discussion that the data from the external device may not be limited to medical data of the patient and can include at least environmental data related to an environment of the patient, personal data related to personal information of the patient and device data related to the user device of the patient.

[0040] In some embodiments, as noted above, the user device may obtain the patient auxiliary data by querying an application executing on the user device. For example, in situations where the user device is at least in close proximity to the patient or the IOP device during the IOP measurement, the user device may query a location application executing on the user device itself for the location of the user device to determine the location of the IOP measurement. In some cases, the user device may access external resources to retrieve some or all of the patient auxiliary data. For example, the user device may access an application executing on the user device or a link to an external resource (e.g., website) to access environmental data such as information related to the temperature, humidity, UV index, etc., of the location of the IOP measurement. As another example, the user device may retrieve the patient’s medical data from a portal storing the patient’s medical records (e.g., a medical records database of the patient’s physician). It is to be understood from the above discussion that the data that the user device can obtain by querying applications executing on the user device or links to external resources is not limited to medical data of the patient or environmental data related to an environment of the patient, and can include at least personal data related to personal information of the patient and device data related to the user device of the patient.

[0041] At step 230, the IOP logic server may analyze, via the processor, the received IOP data based on the received patient auxiliary data to generate a patient health indicator that is dependent on the patient auxiliary data and related to an ocular health of the patient. In some embodiments, the patient health indicator may include a status notice to the health care provider of the patient about the availability of the IOP measurement. That is, upon receiving the data from IOP device and/or the user device, the IOP logic server may generate a notice to inform the health care provider about the availability of the data. In some embodiments, the patient health indicator may include a health warning to the patient and/or the health care provider about the health condition of the patient. For example, the IOP logic server may determine, based on an analysis of the received IOP data and/or patient auxiliary data, that the patient’s ocular health may be at risk, and as such generate a warning to the health care provider (and in some cases, the patient) with results of the analysis.

[0042] In some embodiments, the patient health indicator may also include an adherence parameter or measure indicating or quantifying the patient’s adherence to instructions by the health care provider. For example, the patient auxiliary data may indicate that the patient has not been taking prescribed medications as instructed by the health care provider, and in such cases, the IOP logic server may generate an adherence metric to inform the health care provider about the patient’ s adherence, or lack thereof, to the instructions of the health care provider. In some embodiments, the IOP logic server may also determine, as part of the patient health indicator and based on an analysis of the received IOP data and/or patient auxiliary data, the patient’ s IOP diseases or conditions and treatments for the same. In such cases also, the IOP logic server generate and provide to the health care provider (and in some cases, the patient) a treatment plan along with results of the analysis.

[0043] In some embodiments, the public health indicator that may be generated by analyzing the IOP measurement or data may depend on the patient auxiliary data based on which the IOP measurement is analyzed. For example, an analysis of the IOP measurement may indicate that the IOP pressure is in a certain range of pressures which may be deemed to be risky or not based on the age of the patient. That is, when the IOP measurement is analyzed based on or with respect to the personal data (which the IOP logic server receives as part of the patient auxiliary data), the patient health indicator that includes a warning about the health of the patient may depend on the personal data (e.g., the value of the IOP pressure may be deemed to be safe if the patient is younger than a certain age, but risky if over that certain age). As another example, an analysis of the IOP measurements may indicate that the patient has developed IOP diseases or conditions, and the patient health indicator that includes a treatment plan for treating the IOP disease or conditions may depend on one or more of the patient auxiliary data, including but not limited to medical data related to current medications of the patient, personal data related to patient age, and/or the like. It is to be understood from the above discussion that the analysis of the IOP measurement can depend or may be based on not only medical data or personal data of the patient auxiliary data, but can also depend on other components of the patient auxiliary data such as the device data and the environment data.

[0044] At step 240, the IOP logic server may avail or provide the patient health indicator to a health care provider of the patient. In some cases, the health care provider may be pre-determined and the IOP logic server may provide the health care provider access to the results of the analysis. In yet other cases, the IOP logic server may identify, from a database of health care providers, a suitable health care provider for the patient based on an analysis of the IOP measurements and/or the patient auxiliary data. For example, an analysis of the IOP measurements may indicate a rare ocular disease and, based on the location of the patient as determined from the environmental data, the IOP logic server may generate a list of health care providers within a certain distance of the patient and can treat the rare ocular disease of the patient.

[0045] Computer Implemented System

[0046] In various embodiments, the methods for remote monitoring, diagnosis and treatment of intraocular pressure disease can be implemented via computer software or hardware. That is, as depicted in FIG. 1, the methods (e.g., 200 in FIG. 2) disclosed herein can be implemented on a computing device or server 100 that includes a processor 110 and an IOP logic engine 130 that receive input 140 and generate output 150. In various embodiments, the computing device or server 100 can be communicatively connected to a data store or memory 120 and a display device (not shown) via a direct connection or through an internet connection.

[0047] It should be appreciated that the various engines depicted in FIG. 1 can be combined or collapsed into a single engine, component or module, depending on the requirements of the particular application or system architecture. Moreover, in various embodiments, the memory 120 and the IOP logic engine 130 can comprise additional engines or components as needed by the particular application or system architecture. [0048] FIG. 3 is a block diagram illustrating a computer system 300 upon which embodiments of the present teachings may be implemented. In various embodiments of the present teachings, computer system 300 can include a bus 302 or other communication mechanism for communicating information and a processor 304 coupled with bus 302 for processing information. In various embodiments, computer system 300 can also include a memory, which can be a random- access memory (RAM) 306 or other dynamic storage device, coupled to bus 302 for determining instructions to be executed by processor 304. Memory can also be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 304. In various embodiments, computer system 300 can further include a read only memory (ROM) 308 or other static storage device coupled to bus 302 for storing static information and instructions for processor 304. A storage device 310, such as a magnetic disk or optical disk, can be provided and coupled to bus 302 for storing information and instructions.

[0049] In various embodiments, computer system 300 can be coupled via bus 302 to a display 312, such as a cathode ray tube (CRT) or liquid crystal display (LCD), for displaying information to a computer user. An input device 314, including alphanumeric and other keys, can be coupled to bus 302 for communication of information and command selections to processor 304. Another type of user input device is a cursor control 316, such as a mouse, a trackball or cursor direction keys for communicating direction information and command selections to processor 304 and for controlling cursor movement on display 312. This input device 314 typically has two degrees of freedom in two axes, a first axis (i.e., x) and a second axis (i.e., y), that allows the device to specify positions in a plane. However, it should be understood that input devices 314 allowing for 3- dimensional (x, y and z) cursor movement are also contemplated herein.

[0050] Consistent with certain implementations of the present teachings, results can be provided by computer system 300 in response to processor 304 executing one or more sequences of one or more instructions contained in memory 306. Such instructions can be read into memory 306 from another computer-readable medium or computer-readable storage medium, such as storage device 310. Execution of the sequences of instructions contained in memory 306 can cause processor 304 to perform the processes described herein. Alternatively, hard-wired circuitry can be used in place of or in combination with software instructions to implement the present teachings. Thus, implementations of the present teachings are not limited to any specific combination of hardware circuitry and software.

[0051] The term “computer-readable medium” (e.g., data store, data storage, etc.) or “computer-readable storage medium” as used herein refers to any media that participates in providing instructions to processor 304 for execution. Such a medium can take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Examples of non-volatile media can include, but are not limited to, dynamic memory, such as memory 306. Examples of transmission media can include, but are not limited to, coaxial cables, copper wire, and fiber optics, including the wires that comprise bus 302.

[0052] Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, PROM, and EPROM, a FLASH-EPROM, another memory chip or cartridge, or any other tangible medium from which a computer can read.

[0053] In addition to computer-readable medium, instructions or data can be provided as signals on transmission media included in a communications apparatus or system to provide sequences of one or more instructions to processor 304 of computer system 300 for execution. For example, a communication apparatus may include a transceiver having signals indicative of instructions and data. The instructions and data are configured to cause one or more processors to implement the functions outlined in the disclosure herein. Representative examples of data communications transmission connections can include, but are not limited to, telephone modem connections, wide area networks (WAN), local area networks (LAN), infrared data connections, NFC connections, etc.

[0054] It should be appreciated that the methodologies described herein, flow charts, diagrams and accompanying disclosure can be implemented using computer system 300 as a standalone device or on a distributed network or shared computer processing resources such as a cloud computing network.

[0055] The methodologies described herein may be implemented by various means depending upon the application. For example, these methodologies may be implemented in hardware, firmware, software, or any combination thereof. For a hardware implementation, the processing unit may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, electronic devices, other electronic units designed to perform the functions described herein, or a combination thereof.

[0056] In various embodiments, the methods of the present teachings may be implemented as firmware and/or a software program and applications written in conventional programming languages such as C, C++, Python, etc. If implemented as firmware and/or software, the embodiments described herein can be implemented on a non-transitory computer-readable medium in which a program is stored for causing a computer to perform the methods described above. It should be understood that the various engines described herein can be provided on a computer system, such as computer system 300, whereby processor 304 would execute the analyses and determinations provided by these engines, subject to instructions provided by any one of, or a combination of, memory components 306/308/310 and user input provided via input device 314.

[0057] RECITATIONS OF SOME EMBODIMENTS OF THE PRESENT DISCLOSURE

[0058] Embodiment 1: A method, comprising: receiving, at a processor, intraocular pressure (IOP) data of an IOP measurement of an eye of a patient taken via an at-home IOP device; receiving, at the processor and from a user device of the patient, patient auxiliary data including medical data of the patient different from the IOP data, environmental data related to an environment of the patient during the IOP measurement, personal data related to personal information of the patient and/or device data related to the user device of the patient; analyzing, via the processor, the received IOP data based on the received patient auxiliary data to generate a patient health indicator that is dependent on the patient auxiliary data and related to an ocular health of the patient; and providing, by the processor, the patient health indicator to a health care provider.

[0059] Embodiment 2: The method of embodiment 1, wherein the at-home IOP device is a tonometer.

[0060] Embodiment 3: The method of embodiment 1 or 2, wherein the user device is a portable device including a smartphone, a tablet or a computer.

[0061] Embodiment 4: The method of any of embodiments 1-3, wherein the IOP data is received at the processor after the user device transmits the IOP data to the processor in response to receiving the IOP data from the at-home IOP device.

[0062] Embodiment 5: The method of any of embodiments 1-4, wherein the IOP data is transmitted to the processor directly by the at-home IOP device.

[0063] Embodiment 6: The method of any of embodiments 1-5, further comprising identifying the health care provider based on results of the analyzing the received IOP data.

[0064] Embodiment 7: The method of any of embodiments 1-6, wherein the patient health indicator includes information on health care treatments to at least partially restore the ocular health of the patient.

[0065] Embodiment 8: The method of any of embodiments 1-7, wherein the analyzing the received IOP data based on the received patient auxiliary data is performed by an artificial intelligence (AI) neural network comprising the processor. [0066] Embodiment 9: A system, comprising: a processor and a transceiver coupled to the processor, the system configured to perform the methods of aspects 1-8.

[0067] Embodiment 10: A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform the methods of embodiments 1-8.

[0068] While the present teachings are described in conjunction with various embodiments, it is not intended that the present teachings be limited to such embodiments. On the contrary, the present teachings encompass various alternatives, modifications, and equivalents, as will be appreciated by those of skill in the art. [0069] In describing the various embodiments, the specification may have presented a method and/or process as a particular sequence of steps. However, to the extent that the method or process does not rely on the particular order of steps set forth herein, the method or process should not be limited to the particular sequence of steps described, and one skilled in the art can readily appreciate that the sequences may be varied and still remain within the spirit and scope of the various embodiments.

[0070] Although illustrative embodiments have been shown and described, a wide range of modification, change and substitution is contemplated in the foregoing disclosure and in some instances, some features of the embodiments may be employed without a corresponding use of other features. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. Thus, the scope of the invention should be limited only by the following claims, and it is appropriate that the claims be construed broadly and in a manner consistent with the scope of the embodiments disclosed herein.

Claims

WHAT IS CLAIMED IS:

1. A method, comprising: receiving, at a processor, intraocular pressure (IOP) data of an IOP measurement of an eye of a patient taken via an at-home IOP device; receiving, at the processor and from a user device of the patient, patient auxiliary data including medical data of the patient different from the IOP data, environmental data related to an environment of the patient during the IOP measurement, personal data related to personal information of the patient and/or device data related to the user device of the patient; analyzing, via the processor, the received IOP data based on the received patient auxiliary data to generate a patient health indicator that is dependent on the patient auxiliary data and related to an ocular health of the patient; and providing, by the processor, the patient health indicator to a health care provider.

2. The method of claim 1, wherein the at-home IOP device is a tonometer.

3. The method of claim 1, wherein the user device is a portable device including a smartphone, a tablet or a computer.

4. The method of claim 1 , wherein the IOP data is received at the processor after the user device transmits the IOP data to the processor in response to receiving the IOP data from the at- home IOP device.

5. The method of claim 1, wherein the IOP data is transmitted to the processor directly by the at-home IOP device.

6. The method of claim 1, further comprising identifying the health care provider based on results of the analyzing the received IOP data.

7. The method of claim 1, wherein the patient health indicator includes information on health care treatments to at least partially restore the ocular health of the patient.

8. The method of claim 1, wherein the analyzing the received IOP data based on the received patient auxiliary data is performed by an artificial intelligence (AI) neural network comprising the processor.

9. A system, comprising: a transceiver configured to: receive intraocular pressure (IOP) data of an IOP measurement of an eye of a patient taken via an at-home IOP device; and receive, from a user device of the patient, patient auxiliary data including medical data of the patient different from the IOP data, environmental data related to an environment of the patient during the IOP measurement, personal data related to personal information of the patient and/or device data related to the user device of the patient; and a processor configured to: analyze the received IOP data based on the received patient auxiliary data to generate a patient health indicator that is dependent on the patient auxiliary data and related to an ocular health of the patient; and provide the patient health indicator to a health care provider.

10. The system of claim 9, wherein the at-home IOP device is a tonometer.

11. The system of claim 9, wherein the user device is a portable device including a smartphone, a tablet or a computer.

12. The system of claim 9, wherein the IOP data is received at the processor after the user device transmits the IOP data to the processor in response to receiving the IOP data from the at- home IOP device.

13. The system of claim 9, wherein the IOP data is transmitted to the processor directly by the at-home IOP device.

14. The system of claim 9, wherein the processor is further configured to identify the health care provider based on results of the analyzing the received IOP data.

15. The system of claim 9, wherein the system is an artificial intelligence (AI) neural network system.

16. A non-transitory computer-readable medium (CRM) having program code recorded thereon, the program code comprising: code for causing an intraocular pressure (IOP) logic server to receive IOP data of an IOP measurement of an eye of a patient taken via an at-home IOP device; code for causing the IOP logic server to receive, from a user device of the patient, patient auxiliary data including medical data of the patient different from the IOP data, environmental data related to an environment of the patient during the IOP measurement, personal data related to personal information of the patient and/or device data related to the user device of the patient; code for causing the IOP logic server to analyze the received IOP data based on the received patient auxiliary data to generate a patient health indicator that is dependent on the patient auxiliary data and related to an ocular health of the patient; and code for causing the IOP logic server to provide the patient health indicator to a health care provider.

17. The non-transitory CRM of claim 16, wherein the at-home IOP device is a tonometer.

18. The non-transitory CRM of claim 16, wherein the IOP data is received at the processor after the user device transmits the IOP data to the processor in response to receiving the IOP data from the at-home IOP device.

19. The non-transitory CRM of claim 16, wherein the IOP data is transmitted to the processor directly by the at-home IOP device.

20. The non-transitory CRM of claim 16, wherein the program code further comprising identifying the health care provider based on results of the analyzing the received IOP data.