Systems and methods for biometric-enabled intelligent medical devices and applications thereof

Abstract

According to an aspect of the present disclosure, a hub device collects a set of healthcare parameters from one or more medical devices, wherein each healthcare parameter is a result of conducting a corresponding medical test using a medical device on a person of a plurality of persons. The hub device also receives a biometric data that is able to uniquely identify each person of the plurality of persons. The hub device then identifies a unique identifier associated with the person based on the biometric data and uploads the set of healthcare parameters associated with the unique identifier to a node in a cloud, wherein the node stores the set of healthcare parameters as part of an electronic health record (EHR) of the person.

Description

SYSTEMS AND METHODS FOR BIOMETRIC-ENABLED INTELLIGENTMEDICAL DEVICES AND APPLICATIONS THEREOFPRIORITY CLAIM

[0001] The instant patent application is related to and claims priority from the US provisional patent application entitled, “SYSTEMS AND METHODS FOR BIOMETRIC-ENABLED INTELLIGENT MEDICAL DEVICES AND APPLICATIONS THEREOF” Application No.: 63 / 569,106, Filed: 23rdMarch 2024, which is incorporated in its entirety herewith.BACKGROUND OF THE DISCLOSURE

[0002] Technical Field

[0003] The present disclosure relates to digital healthcare management and more specifically to a system and method for biometric-enabled intelligent medical devices and applications thereof.

[0004] Related Art

[0005] As medical / diagnostic devices become more connected and data-driven, ensuring the security and privacy of user data is critical. Protecting against data breaches and unauthorized access is an ongoing concern.

[0006] For instance, not only users need accurate data, but users also want to capture the data over a period and develop analytics to better serve. For example, in a primary care setup, a user may wish to have his or her data saved in appropriate database. Some of the problems in this area are addressed below.

[0007] Ensuring that medical / diagnostic devices provide accurate and reliable results is paramount. Challenges include minimizing false positives and false negatives, reducing variability, and improving the consistency of results.

[0008] Ensuring that medical / diagnostic devices can seamlessly integrate with electronic health records (EHRs) and other healthcare systems is challenging. Standardizing data formats and communication protocols is crucial for effective interoperability.

[0009] Also, developing and implementing accurate point-of-care diagnostic tests that can be performed quickly and easily by healthcare professionals, even in resource-limited settings, is a challenge. Specifically, developing medical / diagnostic devices capable of performing multiple tests simultaneously (multiplex testing) to provide a more comprehensive diagnostic profile is an ongoing challenge.

[0010] Encouraging persons (such as patients) to actively participate in diagnostic testing and follow-up care, especially for chronic conditions, is a requirement in improving overall healthcare outcomes.[Oil] Therefore, there is an urgent need of systems and methods for biometric-enabled intelligent medical devices and applications thereof.SUMMARY OF THE DISCLOSURE

[0012] The disclosed embodiments relate to systems and methods for biometric-enabled intelligent medical devices and applications thereof. More particularly, the system is a programmable hub device that is used to provided healthcare service management.

[0013] According to an aspect of the present disclosure, the hub device collects a set of healthcare parameters from one or more medical devices, wherein each healthcare parameter is a result of conducting a corresponding medical test using a medical device on a person of a plurality of persons. The hub device also receives a biometric data that is able to uniquely identify each person of the plurality of persons. The hub device then identifies a unique identifier associated with the person based on the biometric data and uploads the set of healthcare parameters associated with the unique identifier to a node in a cloud, wherein the node stores the set of healthcare parameters as part of an electronic health record (EHR) of the person.

[0014] According to another aspect of the present disclosure, the hub device obtains a plan data indicating a corresponding set of medical tests to be conducted for each of a subset of persons contained in the plurality of persons. Upon identifying the unique identifier, the hub device checks whether the unique identifier associated with the person matches any of the identifiers of the subset of persons. The hub device performs the uploading (noted above) if the checking determines that the unique identifier matches the identifier of at least one person in the subset of persons.

[0015] According to one more aspect of the present disclosure, for each first person contained in the subset of persons, and for each healthcare parameter received as the result of conducting a medical test on the first person, the hub device determines whether the medical test is contained in the corresponding set of medical tests indicated in the plan data for the first person. If contained, hub device includes the healthcare parameter in the set of healthcare parameters (uploaded to the node), and excludes the healthcare parameter otherwise.

[0016] According to yet another aspect of the present disclosure, the hub device receives theplan data from another node in the cloud, with the plan data being specified by a user to indicate a desired set of medical tests for each of a desired subset of persons contained in the plurality of persons. Accordingly, the user is enabled to configure the hub device prior to the collecting (noted above).

[0017] According to a further aspect of the present disclosure, the hub device maintains a historical data comprising details of each person, the previous values for the healthcare parameters, and the details of the medical tests conducted on the person including the medical devices used. The hub device then trains, based on said historical data, a machine learning (ML) model to select medical tests for each person. Upon receiving an input data indicating a desired subset of persons of the plurality of persons sought to be tested, the hub device applies the ML model to the input data to generate the plan data indicating a specific set of medical tests selected for each of the desired subset of persons.

[0018] According to an extended aspect of the present disclosure, the biometric data is one of a fingerprint, facial data, or voice data. The biometric data is received from a biometric device forming part of the hub device, a biometric device forming part of the medical device or from a personal device of the person.

[0019] According to another aspect of the present disclosure, each medical device is one of glucometer, oximeter, weighing scale, lipid profile meter, BP monitor, ECG, thermometer, fetal health monitor, spirometer, otoscope, HbAlc monitor, and hemoglobinometer.

[0020] According to one more aspect of the present disclosure, the hub device and the one or more medical devices are part of an Internet of Things (loT) network. The hub device connects to the one or more medical devices through wired or wireless technologies. Encryption is used in the transmission of the set of healthcare parameters from the one or more medical devices to the hub device and in the transmission from the hub device to the node in the cloud.

[0021] Several aspects of the disclosure are described below with reference to examples for illustration. However, one skilled in the relevant art will recognize that the disclosure can be practiced without one or more of the specific details or with other methods, components, materials and so forth. In other instances, well-known structures, materials, or operations are not shown in detail to avoid obscuring the features of the disclosure. Furthermore, the features / aspects described can be practiced in various combinations, though only some of the combinations are described herein for conciseness.BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Example embodiments of the present disclosure will be described with reference to the accompanying drawings briefly described below.

[0023] FIG. 1 is a block diagram illustrating an example computing system in which various aspects of the present disclosure can be implemented.

[0024] FIG. 2 is a flow chart illustrating the manner in which a hub device providing healthcare service management is provided according to aspects of the present disclosure.

[0025] FIG. 3 is a block diagram illustrating the manner in which a hub device is implemented in one embodiment.

[0026] FIG.s 4A and 4B illustrate user interfaces provided by a hub device in one embodiment.

[0027] FIG. 5 is a block diagram illustrating the details of a digital processing system in which various aspects of the present disclosure are operative by execution of appropriate execution modules.

[0028] In the drawings, like reference numbers generally indicate identical, functionally similar, and / or structurally similar elements. The drawing in which an element first appears is indicated by the leftmost digit(s) in the corresponding reference number.DETAILED DESCRIPTION OF THE DISCLOSURE

[0029] It is to be understood that the present disclosure is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The present disclosure is capable of other embodiments and of being practiced or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.

[0030] The use of "including", "comprising", or "having" and variations there of herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. The terms "a" and "an" herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items. Further, the use of terms "first", "second", and "third", and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another.

[0031] As used herein, the singular forms "a", "an", and "the" include both singular and plural referents unless the context clearly dictates otherwise. By way of example, "a dosage" refers to one or more than one dosage. The terms "comprising", "comprises" and "comprised of" asused herein are synonymous with "including", "includes" or "containing", "contains", and are inclusive or open-ended and do not exclude additional, non-recited members, elements, or method steps.

[0032] Example embodiments of the present disclosure are described with reference to the accompanying figures.

[0033] 1. Definitions

[0034] “Biometric” refers to a method of identifying or verifying an individual's identity based on their unique physiological or behavioral characteristics. Biometrics is used in various applications for security, access control, and identification purposes.

[0035] “Chips” refers to microchips or integrated circuits that serve as essential components responsible for various functions within the device, from processing and memory storage to connectivity and control.

[0036] “Al” refers to artificial intelligence and include both machine learning (ML) and deep learning (DL). In medicine, the term refers to the use of artificial intelligence techniques and technologies to assist healthcare professionals in various aspects of medical practice, research, and administration. Al has the potential to transform healthcare by improving diagnosis, treatment, user care, and the overall efficiency of healthcare systems.

[0037] “Electronic Health Record” (EHR) refers to a comprehensive and centralized record of an individual's medical history and health-related information. This record is typically created, managed, and maintained by the user or their authorized representatives

[0038] “Hub” refers to a device or component that serves as a central point for connecting multiple devices or networks together.

[0039] “Hospices” refers to specialized facilities, programs, or services that provide palliative care and support to individuals who are facing life-limiting illnesses, particularly in the advanced stages of their diseases.

[0040] "Internet of Things" (loT) relates to a network of interconnected devices and the technology enabling communication between these devices and the cloud, as well as among the devices themselves.

[0041] “HIPAA Compliant” refers to adhering to the regulations and requirements set forth by the Health Insurance Portability and Accountability Act (HIPAA), a U.S. law enacted in 1996. HIPAA establishes standards to protect sensitive patient health information from being disclosed without the patient's consent or knowledge.

[0042] 2. Example Environment

[0043] FIG. 1 is a block diagram illustrating an example computing system (100) in whichvarious aspects of the present disclosure can be implemented. The block diagram is shown containing network 110, cloud 120 (which in turn is shown containing a number of nodes such as node 130a and 130b, collection server 140 and data store 160), hub device 150, medical devices 170a-170d, end user systems 180a-180b, and healthcare servers 190a-190b.

[0044] Merely for illustration, only representative number / type of systems is shown in FIG. 1. Many computing systems often contain many more systems, both in number and type, depending on the purpose for which the computing system is designed. Each system / device of FIG. 1 is described below in further detail.

[0045] Network 110 provides connectivity between hub device 150, end user systems 180a- 180b, healthcare servers 190a-190b, and nodes of cloud 120 (such as node 130a / 130b, collection server 140). Network 110 may represent Wireless / LAN networks implemented using protocols such as Transport Control Protocol / Internet Protocol (TCP / IP), or circuit switched network implemented using protocols such as Global System for Mobile Communications (GSM), Code-Division Multiple Access (CDMA), etc. as is well known in the relevant arts.

[0046] In general, network 110 provides transport of packets, with each packet containing a source address (as assigned to the specific system from which the packet originates) and a destination address, equaling the specific address assigned to the specific system to which a packet is destined / targeted. The packets would generally contain the requests and responses between the various systems connected via network 110 as described in detail in the below sections.

[0047] Cloud 120 is a collection of nodes (such as node 130a / 130b) that may include processing nodes, connectivity infrastructure, data storages, administration systems, etc., which are engineered to together host software applications. Cloud 120 may be provided on a public cloud infrastructure (such as Amazon Web Services (AWS) available from Amazon.com, Inc., Google Cloud Platform (GCP) available from Google LLC, etc.) that provides a virtual computing infrastructure for various customers, with the scale of such computing infrastructure being specified often on demand. Alternatively, cloud 120 may be provided on an enterprise system (or a part thereof) on the premises of the business organizations. Cloud 160 may also be a "hybrid" infrastructure containing some nodes of a public cloud infrastructure and other nodes of an enterprise system.

[0048] It may be appreciated that each of collection server 140 and data store 160 are implemented on corresponding nodes of cloud 120. Some of the other nodes (such as node 130a / 130b) of cloud 120 may be implemented as corresponding data stores similar to datastore 160, while other nodes of the cloud 120 may be implemented as corresponding server systems, similar to collection server 140.

[0049] Data store 160 represents a non-volatile storage, facilitating storage and retrieval of a collection of data by applications executing in collection server 140. In one embodiment, data store 160 is implemented using relational database technologies where the data is maintained in the form of databases containing tables and columns and provides storage and retrieval of data using structured queries such as SQL (Structured Query Language), as is well known in the relevant arts. Alternatively, data store 160 may be implemented as a file server and store data in the form of one or more files organized in the form of a hierarchy of directories, as is well known in the relevant arts.

[0050] Collection server 140 represents a system, such as a web and / or application server, executing various applications designed to perform one or more tasks requested from other systems such as nodes 130a / 130b, hub device 150, end user systems 180a-180b or healthcare servers 190a-190b. Collection server 140 may perform the tasks using data maintained internally in the server, on external data (e.g., maintained in data store 160) or on data received as part of the requests (e.g., data received from hub device 150). Collection server 140 may also send the results of performance of the tasks to the requesting systems. Furthermore, each server may maintain some of the received information and the result of performance of the tasks in data store 160.

[0051] Each of healthcare servers 190a- 190b represents a system, such as a web and / or application server similar to collection server 140. Each healthcare server 190a- 190b typically belongs to a corresponding healthcare service provider (e.g., hospital, clinic, diagnostic center, etc.) and maintains information / data on one or more persons / patients. Such patient data is typically maintained locally as electronic health records (EHR) in local data stores, not shown, associated with the respective healthcare server 190a-190b.

[0052] Each of end user systems 180a- 180b represents a system such as a personal computer, workstation, mobile phone (e.g., iPhone available from Apple Corporation), tablet, portable device (also referred to as “smart” devices”) that operate with a generic operating system such as Android operating system available from Google Corporation, etc., used by users to send (user) requests to other systems such as nodes of cloud 120 or hub device 150. In addition, each of end user systems 180a- 180b may include various hardware (and corresponding software) sensors such as camera, microphone, accelerometers, etc. In general, an end user system enables a user to send user requests for performing desired tasks to other systems and to receive corresponding responses containing the results of performance of the requestedtasks.

[0053] Each of medical devices 170a-170d is a healthcare product / instrument that is intended for use in the diagnosis, targeting, monitoring, or prevention of diseases or medical conditions. These devices can vary significantly in complexity and purpose, ranging from simple instruments like thermometers and blood pressure monitors to complex machinery such as MRI machines and pacemakers. Medical devices encompass a wide range of instruments and equipment used in healthcare settings for various purposes. In the following disclosure, medical devices 170a-170d are used for conducting tests on persons (such as patients) to measure one or more of their relevant healthcare parameters.

[0054] Examples of medical devices 170a- 170d include, but are not limited to thermometers (e.g., digital, infrared), blood pressure monitors (sphygmomanometers), stethoscopes, x-ray machines, ultrasound machines, glucometers (for measuring healthcare parameters such as blood glucose levels), pulse oximeters (for measuring healthcare parameters such as blood oxygen saturation), spirometers (for measuring healthcare parameters such as lung function), insulin pumps (for managing diabetes), weighing scale, lipid profile meter, fetal health monitor, otoscope, HbAlc monitor, and hemoglobinometer.

[0055] In one embodiment, hub device 150 and medical devices 170a-170d are part of an Internet of Things (loT) network, with hub device 150 connecting to medical devices 170a- 170d through wired or wireless technologies (as indicated by the dashed arrows). In addition, encryption is used in the transmissions from medical devices 170a-170d to hub device 150 and in the transmissions from hub device 150 to nodes in cloud 120 (such as collection server 140).

[0056] As noted in the Background section, there are several challenges to using medical devices 170a-170d such as ensuring the security and privacy of personal healthcare data (such as the healthcare parameters node above), ability to capture the healthcare data over a period, ensuring that medical devices provide accurate and reliable results, ensuring that medical devices can seamlessly integrate with electronic health records (EHRs), implementing accurate point-of-care diagnostic tests that can be performed quickly and easily by healthcare professionals, developing medical devices capable of performing multiple tests simultaneously, and encouraging end users (such as patients) to actively participate in diagnostic testing and follow-up care.

[0057] Hub device 150, provided according to aspects of the present disclosure, is a programmable hub device that is used to provided healthcare service management while overcoming several drawbacks noted above. The manner in which hub device 150 facilitatessuch healthcare service management is described below with examples.

[0058] 3. General Flow

[0059] FIG. 2 is a flow chart illustrating the manner in which a hub device providing healthcare service management is provided according to aspects of the present disclosure. The flowchart is described with respect to FIG. 1, in particular, hub device 150, merely for illustration. However, various features can be implemented in other systems and / or other environments also without departing from the scope of various aspects of the present disclosure, as will be apparent to one skilled in the relevant arts by reading the disclosure provided herein.

[0060] In addition, some of the steps may be performed in a different sequence than that depicted below, as suited in the specific environment, as will be apparent to one skilled in the relevant arts. Many of such implementations are contemplated to be covered by several aspects of the present disclosure.

[0061] In step 211, hub device 150 obtains a plan data indicating a corresponding set of medical tests to be conducted for each of a specific set of persons. According to an aspect, hub device 150 receives the plan data from collection server 140 in cloud 120, with the plan data being specified by a user (for example, a healthcare professional). The user may specify a desired set of medical tests for each of a desired set of persons, thereby configuring hub device 150 prior to start of collecting the healthcare parameters as described below.

[0062] According to another aspect, hub device 150 maintains a historical data comprising details of each person, the previous values for the healthcare parameters, and the details of the medical tests conducted on the person including the medical devices used. Hub device 150 then trains, based on said historical data, a machine learning (ML) model to select medical tests for each person. Upon receiving (from collection server 140) an input data indicating a desired set of persons sought to be tested, hub device 150 applies the ML model to the input data to generate the plan data indicating a specific set of medical tests selected for each of the desired subset of persons.

[0063] In step 212, hub device 150 collects a set of healthcare parameters from one or more medical devices (170a- 170d), each healthcare parameter being a result of conducting a medical test using a medical device on a person. Different healthcare parameters may be received from different medical devices, as will be apparent to one skilled in the relevant arts.

[0064] In step 213, hub device 150 receives a biometric data that is able to uniquely identify the person. The biometric data may be one of a fingerprint, facial data, or voice data (or any other type of data as described in below sections). The biometric data may be received from abiometric device forming part of (embedded in) hub device 150, a biometric device forming part of (embedded in) the medical device 170a-170d or from a personal device of the person (such as end user systems 180a- 180b).

[0065] In step 214, hub device 150 identifies a unique identifier associated with the person based on the biometric data. Such identification may be performed in a known way. For example, the unique identifier may be generated based on the biometric data. Alternatively, a mapping of different biometric data against respective unique identifiers may be maintained, with hub device 150 then performing a lookup of the received biometric data in the mapping.

[0066] In step 215, hub device 150 validates the set of healthcare parameters and the unique identifier against the plan data. The validation ensures that only the corresponding set of medical tests (as specified in the plan data) are conducted for each of the specific set of persons (also specified in the plan data).

[0067] According to aspects, hub device 150 checks whether the unique identifier associated with the person matches any of the identifiers of the specific set of persons. Hub device 150 performs the uploading (noted below) if the checking determines that the unique identifier matches the identifier of at least one person in the specific set of persons.

[0068] In addition, for each person contained in the specific set of persons, and for each healthcare parameter received as the result of conducting a medical test on the person, hub device 150 determines whether the medical test is contained in the corresponding set of medical tests indicated in the plan data for the person. If contained, hub device 150 includes the healthcare parameter in the set of healthcare parameters (uploaded to the node), and excludes the healthcare parameter otherwise.

[0069] In step 216, hub device 150 uploads the set of healthcare parameters associated with the unique identifier to a node (here, collection server 140) in a cloud (120). As such, collection server 140 is facilitated to store the set of healthcare parameters as part of an electronic health record (EHR) of the person. The stored data may thereafter be shared by collection server 140 to healthcare servers 190a- 190b.

[0070] Thus, aspects of the disclosure facilitate healthcare service management. It should be noted that the steps of 211-216 may be performed multiple times in any sequence. In general, the multiple uploads ensures that collection server 140 has the latest data and also the historical information on the healthcare parameters of the person, while also ensuring that such latest data (along with the historical information) is provided to healthcare providers as well as persons.

[0071] The manner in which hub device 150 may be implemented to provide aspects of thepresent disclosure according to the operation of FIG. 2 is described below with examples.

[0072] 4. Example Implementation

[0073] FIG. 3 is a block diagram illustrating the manner in which a hub device (150) is implemented in one embodiment. The block diagram is shown containing data repository 310, plan data processor 320, Al (artificial intelligence) manager 330, biometric authenticator 340, biometric sensor 350, medical device handler 360, data validator 370 and cloud uploader 380. Each of the blocks is described in detail below.

[0074] Data repository 310 is used for long-term storage and management of data, including raw, processed, structured, and unstructured data that is required by other blocks of hub device 150. Data repository 310 may be implemented similar to data store 160, either as a database server using relational database technologies or as a file server.

[0075] Specifically, data repository 310 employs a data management engine to effectively organize, store, edit, maintain, format, and process data originating from both personal medical records and medical information sources. The disclosed embodiment includes the source of personal medical data, but not limited to physician records, vaccine records, dental records, cardiac records, pharmaceutical records, laboratory records, radiological records, scanned medical records, indexed medical information, and textual medical record details. It also includes image information, streamed data, video data, audio data, optical character recognition (OCR) data; recognized data; voice recognition data; and captured data. Handwritten notes, dictated records, and insurance information are seamlessly incorporated into the data management process.

[0076] Plan data processor 320 is designed to obtain a plan data and to store the plan data in data repository 310 in any convenient format. In one embodiment, plan data processor 320 receives (via path 115) the plan data from collection server 140. In another embodiment, plan data processor 320 receives (via path 115) a desired set of persons sought to be tested from collection server 140, and forwards the received desired set to Al manager 330, which in turn generates and returns the plan data to be stored.

[0077] Al manager 320 represents a module implemented using Al technologies such as machine learning (ML) and deep learning (DL). Al manager 320 may encompass any combination of fuzzy logic, a knowledge base (KB), a neural network, a decision support system (DSS), an agent, a software agent, or an expert system.

[0078] According to an aspect, Al manager 330 maintains (in data repository 310) a historical data comprising details of each person (as noted above), the previous values for the healthcare parameters, and the details of the medical tests conducted on the person including the medicaldevices used. Al manager 330 then trains, based on said historical data, a machine learning (ML) model to select medical tests for each person. Upon receiving from plan data processor 320, a desired set of persons sought to be tested as an input data, Al manager 330 applies the ML model to the input data to generate a plan data indicating a specific set of medical tests selected for each of the desired set of persons. Al manager 330 then sends the generated plan data to plan data processor 320.

[0079] According to another aspect, Al manager 330 uses advanced algorithms to perform biometric authentication (and according assist biometric authenticator 340). It supports various biometric methods, such as fingerprint recognition, facial recognition, voice recognition, or iris scanning. This ensures secure and seamless user identification.

[0080] According to one more aspect, Al manager 330 implements ML / DL models that enable the identification of false positive or false negative values received for healthcare parameters. As such, Al manager 330 operates in combination with data validator 370 to cause prompting for more measurements of the identified healthcare parameters.

[0081] Biometric authenticator 340 is designed to authentic received biometric data. The biometric data may be face recognition data, voice recognition data, touch data or any other biometric that uniquely identifies each person. Authentication may entail identifying a unique identifier associated with the received biometric data based on data (e.g., the mapping noted above) maintained in data repository 310. Such authentication enables the hub device to understand who the person is whose testing is being conducted.

[0082] The biometric data may be received (via path 175) from any of medical devices 170a- 170d or received (via path 185) from a personal device of the person such as end user systems 180a- 180b. In one embodiment, a biometric sensor (350) is seamlessly integrated into hub device 150 as described in detail below.

[0083] Biometric sensor 350 is a biometric device that is designed to capture and analyze unique physical or behavioral characteristics of the user. The disclosed embodiment involves the utilization of various types of biometrics, including:

[0084] 1. Fingerprint Sensor: These sensors capture the unique patterns of ridges and valleys on an individual's fingertip. Fingerprint sensors are widely used for access control and device unlocking.

[0085] 2. Facial Recognition Sensor: These sensors analyze facial features, such as the arrangement of eyes, nose, and mouth, to identify individuals. Facial recognition is used in security systems and smartphone authentication.

[0086] 3. Iris Scanner: These sensors capture the unique patterns in the colored part of theeye (iris). Iris recognition is highly accurate and is used in applications like border control and secure access systems.

[0087] 4. Retina Scanner: These sensors capture the unique patterns of blood vessels in the back of the eye (retina). They are used in high-security environments.

[0088] 5. Voice Recognition Sensor: These sensors analyze vocal characteristics, such as pitch and tone, of an individual to verify identity. They are used in voice-controlled systems and phone authentication.

[0089] 6. Hand Geometry Sensor: These sensors measure the size and shape of a person's hand. They are often used in physical access control systems.

[0090] 7. Vein Scanner: These sensors use infrared light to capture the unique vein patterns beneath the skin's surface. They are used in secure access and healthcare applications.

[0091] 8. Palmprint Sensor: These sensors analyze the unique patterns on the palm of the hand. They are used in access control and authentication systems.

[0092] 9. Gait Analysis Sensor: These sensors measure the way a person walks or moves. This can be used for continuous authentication in surveillance and healthcare.

[0093] 10. Signature Recognition Sensor: These sensors capture and analyze an individual's handwriting style. They are used for document verification and secure transactions.

[0094] 11. Keystroke Dynamics Sensor: These sensors analyze the typing rhythm and timing of keystrokes on a keyboard. They can be used for continuous user authentication.

[0095] 12. Ear Recognition Sensor: These sensors analyze the shape and features of a person's ear. They are used in security applications and access control.

[0096] 13. Palm Vein Scanner: These sensors, similar to vein scanners, use infrared technology to capture vein patterns in the palm. They offer high accuracy and security.

[0097] 14. Heartbeat Sensor: These sensors measure the unique electrical activity of the heart. They are used for continuous authentication and health monitoring.

[0098] 15. Body Odor Sensor: These sensors analyze the unique chemical composition of an individual's body odor. They are used experimentally for identification.

[0099] Medical device handler 360 is designed to communicate (wired or wirelessly) with the various medical devices 170a- 170d, receive (via path 175) healthcare parameters, and forwards the received values to data validator 370. Medical device handler 360 may also send a timestamp (indicating a date and time) of receipt of each of the healthcare parameter, associated details such as the specific medical device from which the healthcare parameter was received, etc. as required for the specific implementation. In one embodiment the transmission between medical devices 170a-170d and medical device handler 360 is protectedby encryption techniques well known in the arts.

[0100] Data validator 370 is designed to receive a set of healthcare parameters from medical device handler 360 and the unique identifier of the person from biometric authenticator 340 and then perform validations as required by any plan data stored in data repository 310 (earlier by plan data processor 320). If no plan data is present, data validator 370 forwards the received set of healthcare parameters and unique identifier to cloud uploader 380.

[0101] If a plan data is present, data validator 370 checks whether the unique identifier associated with the person matches any of the identifiers of the specific set of persons specified in the plan data. Data validator 370 then marks the person as having a match if the checking determines that the unique identifier matches the identifier of at least one person in the specific set of persons.

[0102] In addition, for each healthcare parameter received, data validator 370 determines whether the medical test is contained in the corresponding set of medical tests indicated in the plan data for the person. If contained, data validator 370 includes the healthcare parameter in the set of healthcare parameters, and excludes the healthcare parameter otherwise.

[0103] After the above validations are performed, data validator 370 forwards the determined set of healthcare parameters and unique identifier to cloud uploader 380 only if the person is marked to have a match.

[0104] Cloud uploader 380 is designed to communicate with nodes of cloud 120 (such as collection server 140) to upload the set of healthcare parameters and unique identifier (received from data validator 370) to the cloud.

[0105] It may be appreciated that the above described implemented of hub device 150 has several advantages. For example, unique identification and data stealing can be prevented using the biometric enabled devices. In a primary care setup, if a device has biometric identification, a user can uniquely identify his or her data which could be saved in appropriate database under his / her name. The artificial intelligence (Al) enabled device can quickly pick up the false positive or false negative and prompt for more measurements.

[0106] The biometric enabled devices along with Al driven software system around the device will ensure seamless integration with EHR system. With biometric enabled hub-device users can use the devices once they are authenticated by biometric. Biometric enabled hubdevice solves the problem of performing multiple tests simultaneously (multiplex testing) to provide a more comprehensive diagnostic profile.

[0107] Hub device 150 (implemented as a hardware, a software, or a combination thereof) acts as a central point from where data is collected from individual loT / medical devices andsent to the EHR system of the person / patient. Also, the hub device acts as the central point from where certain tests can be planned for a particular patient, etc. The hub device can be used in a family setting where many people can use it or by a healthcare providing institute such as hospital where it can be used for many patients. The hub device can be preprogrammed by care givers for the specific tests for specific patients thereby it can save them time later. The data transmitted wirelessly from the loT / medical devices to the hub device is done through encryption to ensure data security. Some sample user interfaces that may be provided by hub device 150 (for example, when implemented as software executing on a mobile phone / tablet) is described below with examples.

[0108] 5. Sample User Interfaces

[0109] FIG.s 4A and 4B illustrate user interfaces provided by a hub device (150) in one embodiment. Referring to FIG. 4A, display portion 410 depicts a welcome screen that is displayed to a user (healthcare profession, patient, etc.) of the hub device. It may be observed that the user is required to provide a mobile number for self-authentication (using OTP well known in the arts).

[0110] Display portion 420 depicts a dashboard displayed to the user after successful authentication. The dashboard shows the number of tests assigned to the user “John”. The user may select the button labeled “Upcoming Test” to view the list of tests.

[0111] Display portion 430 depicts a list of persons (such as “John Doe”, “Jean Doe”) and a corresponding set of medical tests (that are planned) to be performed for each person. The user may select the button labeled “Proceed to test” to start the testing process.

[0112] Display portion 440 depicts a list of medical devices that may be used to conduct the one or more medical tests planned for a single person. The status of communication between the hub device and each of the medical devices is shown via the corresponding red / green dots (with three red dots indicating that there is no connection and three green dots indicating that there is excellent connection).

[0113] Referring to FIG. 4B, display portion 450 depicts the details of a single medical test (here “Eipid Profile Test”) to be conducted for the person. The user may select the button labeled “Start test” to start the specific test.

[0114] Display portion 460 displays the status of various medical tests conducted for the person using corresponding medical devices. Here, the status of the tests for the person named “Aman Sharma” is shown.

[0115] Display portion 470 displays the various healthcare parameters (“Total Choloesterol”, “Glucose”, etc.) collected after the performance of a medical test (here “Eipid Profile Test”).The user may select the button labeled “Save” to cause upload of the healthcare parameters along with the unique identifier of the person to the cloud.

[0116] It may be appreciated that aspects of the present disclosure may be used in public health setups, hospitals, primary care, hospices, old age asylums, etc. Even in a family, the instant disclosure may be very helpful by identifying, securing, and analyzing user data accurately and consistently. The result will help better care for the person (patient) and provide valuable information to caregivers to optimize care delivery. The embodiment may also help reduce overall costs for healthcare providers.

[0117] Some of the benefits of the instant disclosure are (A) Reduced Workload: Automating test execution and data entry minimizes the manual effort required by healthcare professionals; (B) Improved Accuracy: The system eliminates human errors by leveraging loT-enabled automation; and (C) Enhanced Privacy: Adherence to HIPAA-compliant standards ensures the protection of sensitive health information. A best mode to practice the disclosed embodiments is by selling to hospitals, primary care, even to families. This innovation could create B2B, B2C and B2G business. Working with traders and healthcare consultants and sales channels, marketing may also be done.

[0118] It should be appreciated that the above noted features can be implemented in various embodiments as a desired combination of one or more of hardware, execution modules and firmware. The description is continued with respect to one embodiment in which various features are operative when execution modules are executed.

[0119] 6. Digital Processing System

[0120] FIG. 5 is a block diagram illustrating the details of digital processing system (500) in which various aspects of the present disclosure are operative by execution of appropriate execution modules. Digital processing system 500 may correspond to hub device 150, or any system implementing the features of hub device 150 described herein.

[0121] Digital processing system 500 may contain one or more processors (such as a central processing unit (CPU) 501), random access memory (RAM) 502, secondary memory 503, graphics controller 506, display unit 507, network interface 508, and input interface 509. All the components except display unit 507 may communicate with each other over communication path 505 which may contain several buses as is well known in the relevant arts. The components of FIG. 5 are described below in further detail.

[0122] CPU 501 may execute instructions stored in RAM 502 to provide several features of the present disclosure. CPU 501 may contain multiple processing units, with each processing unit potentially being designed for a specific task. Alternatively, CPU 501 may contain only asingle general-purpose processing unit. RAM 502 may receive instructions from secondary memory 503 using communication path 505.

[0123] Graphics controller 506 generates display signals (e.g., in RGB format) to display unit 507 based on data / instructions received from CPU 501. Display unit 507 contains a display screen to display the images defined by the display signals (e.g., portions of the user interfaces shown in FIG.s 4A and 4B). Input interface 509 may correspond to a keyboard and a pointing device (e.g., touch-pad, mouse), which enable the various inputs to be provided (e.g., inputs for the user interfaces shown in FIG.s 4A and 4B).

[0124] Network interface 508 provides connectivity to a network (e.g., using Internet Protocol), and may be used to communicate with other connected systems. Network interface 508 may provide such connectivity over a wire (in the case of TCP / IP based communication) or wirelessly (in the case of WIFI, Bluetooth based communication).

[0125] Secondary memory 503 may contain hard drive 503a, flash memory 503b, and removable storage drive 503c. Secondary memory 503 may store the data (e.g., portions of the data described above with respect to FIG. 3) and software instructions (e.g., for implementing the steps of FIG.s 2A-2C, the blocks of FIG. 3), which enable digital processing system 500 to provide several features in accordance with the present disclosure.

[0126] Some or all of the data and instructions may be provided on removable storage unit 504, and the data and instructions may be read and provided by removable storage drive 503c to CPU 501. Floppy drive, magnetic tape drive, CD-ROM drive, DVD Drive, Flash memory, removable memory chip (PCMCIA Card, EPROM) are examples of such removable storage drive 503c.

[0127] Removable storage unit 504 may be implemented using storage format compatible with removable storage drive 503c such that removable storage drive 503c can read the data and instructions. Thus, removable storage unit 504 includes a computer readable storage medium having stored therein computer software (in the form of execution modules) and / or data.

[0128] However, the computer (or machine, in general) readable storage medium can be in other forms (e.g., non-removable, random access, etc.). These “computer program products” are means for providing execution modules to digital processing system 500. CPU 501 may retrieve the software instructions (forming the execution modules) and execute the instructions to provide various features of the present disclosure described above.

[0129] While specific embodiments of the disclosure have been shown and described in detail to illustrate the inventive principles, it will be understood that the disclosure may beembodied otherwise without departing from such principles.

[0130] Reference throughout this specification to “one embodiment”, “an embodiment”, or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosed embodiment. Thus, appearances of the phrases “in one embodiment”, “in an embodiment” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0131] Merely for illustration, only representative number / type of graph, chart, block, and sub-block diagrams were shown. Many environments often contain many more block and sub-block diagrams or systems and sub-systems, both in number and type, depending on the purpose for which the environment is designed.

[0132] It should be understood that the figures and / or screen shots illustrated in the attachments highlighting the functionality and advantages of the disclosed embodiment are presented for example purposes only. The disclosed embodiment is sufficiently flexible and configurable, such that it may be utilized in ways other than that shown in the accompanying figures.

[0133] All publications, patents, and patent applications cited in the present specification are hereby incorporated by reference in their totality. In particular, the teachings of all documents herein specifically referred to are incorporated by reference.

[0134] It should also be understood that the examples and embodiments described herein are for illustrative purposes only and that various modifications or changes in light thereof will be suggested to persons skilled in the art and are to be included within the spirit and purview of this application and scope of the appended claims.

Claims

What is Claimed is:

1. A method for providing healthcare service management, the method being performed at a hub device, the method comprising: collecting a set of healthcare parameters from one or more medical devices, wherein each healthcare parameter is a result of conducting a corresponding medical test using a medical device on a person of a plurality of persons; receiving a biometric data that is able to uniquely identify each person of the plurality of persons; identifying a unique identifier associated with the person based on the biometric data; and uploading the set of healthcare parameters associated with the unique identifier to a node in a cloud, wherein the node stores the set of healthcare parameters as part of an electronic health record (EHR) of the person.

2. The method of claim 1, further comprising: obtaining a plan data indicating a corresponding set of medical tests to be conducted for each of a subset of persons contained in the plurality of persons; upon identifying the unique identifier, checking whether the unique identifier associated with the person matches any of the identifiers of the subset of persons; and performing the uploading if the checking determines that the unique identifier matches the identifier of at least one person in the subset of persons.

3. The method of claim 2, wherein for each first person contained in the subset of persons, the method further comprises: for each healthcare parameter received as the result of conducting a medical test on the first person: determining whether the medical test is contained in the corresponding set of medical tests indicated in the plan data for the first person; and if contained, including the healthcare parameter in the set of healthcare parameters, and excluding the healthcare parameter otherwise.

4. The method of claim 3, wherein the obtaining comprises receiving the plan data from another node in the cloud, wherein the plan data is specified by a user to indicate a desired set of medical tests for each of a desired subset of persons contained in the plurality of persons,wherein the user is enabled to configure the hub device prior to the collecting.

5. The method of claim 3, wherein the obtaining comprises: maintaining a historical data comprising details of each person, the previous values for the healthcare parameters, and the details of the medical tests conducted on the person including the medical devices used; training, based on said historical data, a machine learning (ML) model to select medical tests for each person; receiving an input data indicating a desired subset of persons of the plurality of persons sought to be tested; and applying the ML model to the input data to generate the plan data indicating a specific set of medical tests selected for each of the desired subset of persons.

6. The method of claim 1, wherein the biometric data is one of a fingerprint, facial data, or voice data, wherein the biometric data is received from a biometric device forming part of the hub device, a biometric device forming part of the medical device or from a personal device of the person.

7. The method of claim 6, wherein each medical device is one of glucometer, oximeter, weighing scale, lipid profile meter, BP monitor, ECG, thermometer, fetal health monitor, spirometer, otoscope, HbAlc monitor, and hemoglobinometer.

8. The method of claim 7, wherein the hub device and the one or more medical devices are part of an Internet of Things (loT) network, wherein the hub device connects to the one or more medical devices through wired or wireless technologies, wherein encryption is used in the transmission of the set of healthcare parameters from the one or more medical devices to the hub device and in the transmission from the hub device to the node in the cloud.

9. A non-transitory machine-readable medium storing one or more sequences of instructions for providing healthcare service management, wherein execution of the one or more instructions by one or more processors contained in a digital processing system causes the digital processing system to perform the actions of:collecting a set of healthcare parameters from one or more medical devices, wherein each healthcare parameter is a result of conducting a corresponding medical test using a medical device on a person of a plurality of persons; receiving a biometric data that is able to uniquely identify each person of the plurality of persons; identifying a unique identifier associated with the person based on the biometric data; and uploading the set of healthcare parameters associated with the unique identifier to a node in a cloud, wherein the node stores the set of healthcare parameters as part of an electronic health record (EHR) of the person.

10. The non-transitory machine-readable medium of claim 9, further comprising one or more instructions for: obtaining a plan data indicating a corresponding set of medical tests to be conducted for each of a subset of persons contained in the plurality of persons; upon identifying the unique identifier, checking whether the unique identifier associated with the person matches any of the identifiers of the subset of persons; and performing the uploading if the checking determines that the unique identifier matches the identifier of at least one person in the subset of persons.

11. The non-transitory machine-readable medium of claim 10, wherein for each first person contained in the subset of persons, further comprises one or more instructions for: for each healthcare parameter received as the result of conducting a medical test on the first person: determining whether the medical test is contained in the corresponding set of medical tests indicated in the plan data for the first person; and if contained, including the healthcare parameter in the set of healthcare parameters, and excluding the healthcare parameter otherwise.

12. The non-transitory machine-readable medium of claim 11, wherein the obtaining comprises one or more instructions for receiving the plan data from another node in the cloud, wherein the plan data is specified by a user to indicate a desired set of medical tests for each of a desired subset of persons contained in the plurality of persons, wherein the user is enabled to configure the hub device prior to the collecting.

13. The non-transitory machine-readable medium of claim 11, wherein the obtaining comprises one or more instructions for: maintaining a historical data comprising details of each person, the previous values for the healthcare parameters, and the details of the medical tests conducted on the person including the medical devices used; training, based on said historical data, a machine learning (ML) model to select medical tests for each person; receiving an input data indicating a desired subset of persons of the plurality of persons sought to be tested; and applying the ML model to the input data to generate the plan data indicating a specific set of medical tests selected for each of the desired subset of persons.

14. The non-transitory machine-readable medium of claim 1, wherein the biometric data is one of a fingerprint, facial data, or voice data, wherein the biometric data is received from a biometric device forming part of the hub device, a biometric device forming part of the medical device or from a personal device of the person.

15. A hub device comprising: a random access memory (RAM) to store instructions for providing healthcare service management; and one or more processors to retrieve and execute the instructions, wherein execution of the instructions causes the hub device to perform the actions of: collecting a set of healthcare parameters from one or more medical devices, wherein each healthcare parameter is a result of conducting a corresponding medical test using a medical device on a person of a plurality of persons; receiving a biometric data that is able to uniquely identify each person of the plurality of persons; identifying a unique identifier associated with the person based on the biometric data; and uploading the set of healthcare parameters associated with the unique identifier to a node in a cloud, wherein the node stores the set of healthcare parameters as part of an electronic health record (EHR) of the person.

16. The hub device of claim 15, further performing the actions of: obtaining a plan data indicating a corresponding set of medical tests to be conducted for eachof a subset of persons contained in the plurality of persons; upon identifying the unique identifier, checking whether the unique identifier associated with the person matches any of the identifiers of the subset of persons; and performing the uploading if the checking determines that the unique identifier matches the identifier of at least one person in the subset of persons.

17. The hub device of claim 16, wherein for each first person contained in the subset of persons, further performing the actions of: for each healthcare parameter received as the result of conducting a medical test on the first person: determining whether the medical test is contained in the corresponding set of medical tests indicated in the plan data for the first person; and if contained, including the healthcare parameter in the set of healthcare parameters, and excluding the healthcare parameter otherwise.

18. The hub device of claim 17, wherein the obtaining comprises the actions of receiving the plan data from another node in the cloud, wherein the plan data is specified by a user to indicate a desired set of medical tests for each of a desired subset of persons contained in the plurality of persons, wherein the user is enabled to configure the hub device prior to the collecting.

19. The hub device of claim 17, wherein the obtaining comprises the actions of: maintaining a historical data comprising details of each person, the previous values for the healthcare parameters, and the details of the medical tests conducted on the person including the medical devices used; training, based on said historical data, a machine learning (ML) model to select medical tests for each person; receiving an input data indicating a desired subset of persons of the plurality of persons sought to be tested; and applying the ML model to the input data to generate the plan data indicating a specific set of medical tests selected for each of the desired subset of persons.

20. The hub device of claim 15, wherein the biometric data is one of a fingerprint, facial data, or voice data,wherein the biometric data is received from a biometric device forming part of the hub device, a biometric device forming part of the medical device or from a personal device of the person.

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