WO2023194788A1

WO2023194788A1 - System and method for managing collection of medical data

Info

Publication number: WO2023194788A1
Application number: PCT/IB2022/055578
Authority: WO
Inventors: Rathnakamal Athuluri; Sunil Babubhai Kapadia
Original assignee: Individual
Current assignee: Individual
Priority date: 2022-04-06
Filing date: 2022-06-16
Publication date: 2023-10-12
Anticipated expiration: 2024-10-06

Abstract

A system for managing a collection of medical data is provided. The system includes a processing subsystem which includes a data collection module (40) which receives first entries corresponding to multiple fields, generates sub-field(s), receives second entries corresponding to the sub-field(s). The processing subsystem also includes a data validation module (50) which stores the medical data corresponding to at least one of the first entries and the second entries on at least one of a distributed ledger by initiating a contract-based process, and a centralized ledger by initiating a data encryption process, validates the corresponding medical data to be authentic. The processing subsystem also includes a data acknowledgment module (60) which generates a unique acknowledgment number linked to the medical data, identifies a comprehension level of the corresponding medical data, generates a data quality score, and generates a predetermined reward for the user, thereby managing the collection of the medical data.

Description

SYSTEM AND METHOD FOR MANAGING COLLECTION OF MEDICAL DATA

EARLIEST PRIORITY DATE

This Application claims priority from a Complete patent application filed in India having Patent Application No. 202241020777, filed on April 06, 2022, and titled SYSTEM AND METHOD FOR MANAGING COLLECTION OF MEDICAL DATA.

FIELD OF INVENTION

Embodiments of a present disclosure relate to a field of healthcare, and more particularly to a system and a method for managing a collection of medical data.

BACKGROUND

Health is described as a state of total physical, mental, social, and spiritual well-being that entails keeping the body as healthy as possible by following daily guidance and taking preventive steps to minimize the risk of disease. A number of factors have an impact on the health of people who take drugs. These factors could include the medication's therapeutic behavior on an individual, the synergistic or antagonistic behavior of other medications taken alongside an existing medication, comorbidities, genetics, and lifestyle behaviors like smoking, weight changes, medication compliance, and mental health situations. However, people are usually unaware of one or more side effects that may be caused by the medicines due to the lack of availability of information about such medicines for the people. Therefore, any treatment suggested by a physician for a patient could be least effective due to a lack of the information about a latest cure, latest medicines, or the like.

Hence, there is a need for an improved system and method for managing a collection of medical data which addresses the aforementioned issues.

BRIEF DESCRIPTION

In accordance with one embodiment of the disclosure, a system for managing a collection of medical data is provided. The system includes a processing subsystem hosted on a server. The processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes a data collection module. The data collection module is configured to receive one or more first entries corresponding to a plurality of fields, from a user upon registration, the one or more first entries are corresponding to one or more first data points associated with the medical data corresponding to the user in one or more first forms. The data collection module is also configured to generate one or more subfields following the corresponding plurality of fields in a predefined order based on predefined criteria, upon analyzing the one or more first entries using a natural language processing technique in real-time. The one or more sub-fields are linked to the corresponding plurality of fields. Further, the data collection module is also configured to receive one or more second entries corresponding to the one or more sub-fields, from the user, upon generating the corresponding one or more sub-fields in real-time. The one or more second entries are corresponding to one or more second data points associated with the medical data in one or more second forms. The processing subsystem also includes a data validation module operatively coupled to the data collection module. The data validation module is configured to store the medical data corresponding to at least one of the one or more first entries and the one or more second entries on at least one of a distributed ledger by initiating a contractbased process, and a centralized ledger by initiating a data encryption process. The data validation module is also configured to validate the corresponding medical data to be authentic by analyzing the medical data using the natural language processing technique, based on a plurality of first historic datasets. Further, the processing subsystem also includes a data acknowledgment module operatively coupled to the data validation module. The data acknowledgment module is configured to generate a unique acknowledgment number linked to the medical data corresponding to the user, upon validating the corresponding medical data to be authentic. The data acknowledgment module is also configured to identify a comprehension level of the corresponding medical data based on a plurality of second historic datasets, using the natural language processing technique, upon generating the unique acknowledgment number. Further, the data acknowledgment module is also configured to generate a data quality score corresponding to a quality of the corresponding medical data based on the comprehension level of the corresponding medical data. Furthermore, the data acknowledgment module is also configured to generate a predetermined reward for the user based on the data quality score corresponding to the quality of the corresponding medical data, thereby managing the collection of the medical data.

In accordance with another embodiment, a method for managing a collection of medical data is provided. The method includes receiving one or more first entries corresponding to a plurality of fields, from a user upon registration, wherein the one or more first entries are corresponding to one or more first data points associated with the medical data corresponding to the user in one or more first forms. The method also includes generating one or more sub-fields following the corresponding plurality of fields in a predefined order based on predefined criteria, upon analyzing the one or more first entries using a natural language processing technique in real-time, wherein the one or more sub-fields are linked to the corresponding plurality of fields. Further, the method also includes receiving one or more second entries corresponding to the one or more sub-fields, from the user, upon generating the corresponding one or more sub-fields in real-time, wherein the one or more second entries are corresponding to one or more second data points associated with the medical data in one or more second forms. Furthermore, the method also includes storing the medical data corresponding to at least one of the one or more first entries and the one or more second entries on at least one of a distributed ledger by initiating a contract-based process, and a centralized ledger by initiating a data encryption process. Furthermore, the method also includes validating the corresponding medical data to be authentic by analyzing the medical data using the natural language processing technique, based on a plurality of first historic datasets. Furthermore, the method also includes generating a unique acknowledgment number linked to the medical data corresponding to the user, upon validating the corresponding medical data to be authentic. Furthermore, the method also includes identifying a comprehension level of the corresponding medical data based on a plurality of second historic datasets, using the natural language processing technique, upon generating the unique acknowledgment number. Furthermore, the method also includes generating a data quality score corresponding to a quality of the corresponding medical data based on the comprehension level of the corresponding medical data. Furthermore, the method also includes generating a predetermined reward for the user based on the data quality score corresponding to the quality of the corresponding medical data, thereby managing the collection of the medical data. To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:

FIG. 1 is a block diagram representation of a system for managing a collection of medical data in accordance with an embodiment of the present disclosure;

FIG. 2 is a block diagram representation of an exemplary embodiment of the system for managing a collection of medical data of FIG. 1 in accordance with an embodiment of the present disclosure;

FIG. 3 is a block diagram of a data collection management computer or a data collection management server in accordance with an embodiment of the present disclosure;

FIG. 4 (a) is a flow chart representing steps involved in a method for managing a collection of medical data in accordance with an embodiment of the present disclosure; and

FIG. 4 (b) is a flow chart representing continued steps involved in the method of FIG. 4 (a) in accordance with an embodiment of the present disclosure.

Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.

DETAILED DESCRIPTION

For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.

The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or sub-systems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.

In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.

Embodiments of the present disclosure relate to a system for managing a collection of medical data. As used herein, the term “medical data” refers to data that contains information on a person's state of health and the medical treatment that they have received. Further, the system described hereafter in FIG. 1 is the system for managing the collection of the medical data.

FIG. 1 is a block diagram representation of a system (10) for managing a collection of medical data in accordance with an embodiment of the present disclosure. The system (10) includes a processing subsystem (20) hosted on a server (30). In one embodiment, the server (30) may include a cloud server. In another embodiment, the server (30) may include a local server. The processing subsystem (20) is configured to execute on a network (not shown in FIG. 1) to control bidirectional communications among a plurality of modules. In one embodiment, the network may include a wired network such as a local area network (LAN). In another embodiment, the network may include a wireless network such as wireless fidelity (Wi-Fi), Bluetooth, Zigbee, near field communication (NFC), infrared communication, or the like.

Basically, for a user to be able to use the system (10), the user may have to register with the system (10). Therefore, in an embodiment, the processing subsystem (20) may include a registration module (as shown in FIG. 2). The registration module may be configured to register the user with the system (10) upon receiving the plurality of user details via a user device. In one embodiment, the plurality of user details may be stored in a database (as shown in FIG. 2) of the system (10). In one exemplary embodiment, the database may include a local database or a cloud database. Also, in an embodiment, the database may be a distributed ledger or a centralized ledger.

Further, in one exemplary embodiment, the plurality of user details may include a username as per on a unique identity card, a unique identity card, contact details as per the unique identity card, marital status, age, gender, location, one or more social media platform links, or the like corresponding to the user. In an embodiment, the unique identity card may correspond to an Aadhaar card, Permanent Account Number (PAN) card, Passport, or the like. Furthermore, in an embodiment, the user device may include a mobile phone, a tablet, a laptop, or the like belonging to the corresponding user.

Upon registration, the user may have to provide details corresponding to a health of the user. Therefore, the processing subsystem (20) includes a data collection module (40). The data collection module (40) may be operatively coupled to the registration module. The data collection module (40) is configured to receive one or more first entries corresponding to a plurality of fields, from the user upon registration. The one or more first entries are corresponding to one or more first data points associated with the medical data corresponding to the user in one or more first forms.

Basically, upon registration, the plurality of fields may appear on a user interface of the system (10), appearing on the user device. The user may have to provide the details alongside the corresponding plurality of fields. In one exemplary embodiment, the plurality of fields may include at least one of a medicine field to receive information about one or more medicines been taken by the user currently and in past, a vaccine field to receive information about one or more vaccines been taken by the user currently and in past, a medical device field to receive information about one or more medical devices been used by the user currently and in past, one or more adverse events observed field, a field to receive physician details to whom the user may have consulted, prescription details receiving field, a field to receive medical details corresponding to a related person of the user, and the like. Moreover, in an embodiment, upon reaching the field to receive the medical details corresponding to the related person of the user, all of the plurality of fields filled earlier for the user may have to be filled now for the corresponding related person. In an embodiment, the related person may include a family member, a friend, a relative, or the like.

In one exemplary embodiment, in a case of the medical field, the one or more first data points associated with the medical data may include information about at least one of tablets, capsules, transdermal patches, ointments, injections, saline, a drug injected to the saline, and the like. Similarly, in a case of the vaccine field, the one or more first data points associated with the medical data may include information about flu, pox, polio, severe acute respiratory syndrome coronavirus 2 (SARS-CoV), Tetanus Toxoid (TT), Diphtheria-pertussis-tetanus (DPT), Haemophilus Influenzae Type b (Hib), Japanese Encephalitis (JE), swine flu (H1N1), Typhoid, Hepatitis (Hep-A, B, C, D, m, b), Oral poliovirus vaccines (OPV), and the like. Further, in a case of the medical device field, the one or more first data points associated with the medical data may include information about at least one of a bloop pressure (BP) device, an Angio device, an X-ray device, a Computed tomography (CT) scanner, a Magnetic resonance imaging (MRI) scanner, and the like. Moreover, in an embodiment, the one or more first entries may be provided in the one or more first forms, wherein the one or more first forms may include at least one of in a text form, in an image form, in a document form, and the like.

Upon receiving the one or more first entries for the plurality of fields, every time, one or more sub-fields may be needed to appear to receive further in-depth details corresponding to the medical data of the user. Therefore, the data collection module (40) is also configured to generate the one or more sub-fields following the corresponding plurality of fields in a predefined order based on predefined criteria, upon analyzing the one or more first entries using a natural language processing (NLP) technique in real-time. The one or more sub-fields are linked to the corresponding plurality of fields.

In one embodiment, the predefined criteria may include generating a new field related to a previous field upon analyzing the previous field using the NLP technique one after the other being the predefined order. As used herein, the term “natural language processing” is defined as a branch of artificial intelligence (Al) that helps computers understand, interpret and manipulate human language. Here, predefined historic data may be used to train a model that may be used for an operation of the NLP, wherein the predefined historic data may include a word dictionary of a plurality of words with meaning.

Later, the data collection module (40) is also configured to receive one or more second entries corresponding to the one or more sub-fields, from the user, upon generating the corresponding one or more sub-fields in real-time. The one or more second entries are corresponding to one or more second data points associated with the medical data in one or more second forms. In one embodiment, for the medicine field, the one or more sub-fields may include a category field of a medicine, a prescription details field, an alternative prescription field, and the like. Similarly, the one or more sub-fields for all of the plurality of fields may be generated in real-time.

Further, in an embodiment, the one or more second data points for the corresponding one or more sub-fields under the medicine field may include a medicine for headache, cold, fever, and the like. The one or more second data points may also include a prescription of taking the tablets twice daily for two weeks, consulting a physician after every two weeks for two months, avoiding climbing stairs, and the like. Moreover, in an embodiment, the one or more second entries may be provided in the one or more second forms, wherein the one or more second forms may include at least one of in a text form, in an image form, in a document form, and the like.

Upon collecting the medical data, an authenticity of the same may have to be checked, because at first, the user may fail to provide appropriate and authentic information. Therefore, the processing subsystem (20) also includes a data validation module (50) operatively coupled to the data collection module (40). Prior to checking the authenticity of the medical data, a security of the medical data may have to be taken care of so that the user may be assured that the medical data belonging to the user would be safe with the system (10), and the user can trust the system (10). The data validation module (50) is configured to store the medical data corresponding to at least one of the one or more first entries and the one or more second entries on at least one of a distributed ledger by initiating a contract-based process, and a centralized ledger by initiating a data encryption process.

As used herein, the term “distributed ledger” refers to a consensus of replicated, shared, and synchronized digital data geographically spread across multiple sites, countries, or institutions. Unlike with a centralized database, there is no central administrator. In one embodiment, the distributed ledger may be a blockchain. As used herein, the term “blockchain” is a type of distributed ledger that is a system of recording information in a way that makes it difficult or impossible to change, hack, or cheat the system. A blockchain is essentially a digital ledger of transactions that is duplicated and distributed across the entire network of computer systems on the blockchain. Furthermore, in one embodiment, the contract-based process may include a smart contract. As used herein, the term, “smart contract” refers to a transaction protocol that is intended to automatically execute, control, or document legally relevant events and actions according to the terms of a contract or an agreement. Also, the medical data stored using the smart contract may be completely encrypted using user’s private keys, which may be completely hackproof.

Moreover, as used herein, the term “centralized ledger” refers to a general ledger that contains all the accounts for recording transactions relating to a company’s assets, liabilities, owners’ equity, revenue, and expenses, and is having a central administrator. Further, the centralized ledger may not be as safe as the distributed ledger, therefore, encryption may be needed. Thus, as used herein, the term “data encryption process” refers to a process of encrypting data which means translating data from plaintext (unencrypted) to ciphertext (encrypted). Users can access encrypted data with an encryption key and decrypted data with a decryption key.

Upon taking care of the security of the medical data, the authenticity of the medical data may be checked. Therefore, the data validation module (50) is also configured to validate the corresponding medical data to be authentic by analyzing the medical data using the NLP technique, based on a plurality of first historic datasets. In one exemplary embodiment, the plurality of first historic datasets may include a drug dictionary such as a Medical Dictionary for Regulatory Activities (MedDRA), a World Health Organization Drug Dictionary (WHODD), and the like. Basically, when the NLP technique may be used, a model may be trained with the plurality of first historic datasets along with the predefined historic data using Al, wherein the model may be used by the data validation module (50) to generate one or more insights about the medical data shared by the user being authentic or no.

Upon checking the validity, the user may be acknowledged for the same, based on a validation result. In one embodiment, the validation result may include a positive validation result or a negative validation result. In one exemplary embodiment, the positive validation result may correspond to the medical data provided by the user being true and authentic. In another exemplary embodiment, the negative validation result may correspond to the medical data being not true, or partially true, and hence being invalidated from being authentic.

Therefore, the processing subsystem (20) also includes a data acknowledgment module (60) operatively coupled to the data validation module (50). The data acknowledgment module (60) is configured to generate a unique acknowledgment number linked to the medical data corresponding to the user, upon validating the corresponding medical data to be authentic. As used herein, the term “unique acknowledgment number” refers to a series of numbers generated randomly and having a random arrangement of the corresponding series of numbers. Basically, along with the medical data, the unique acknowledgment number may also be stored. Therefore, whenever the user or an authorized person may be willing to get access to the medical data, and if the user or the authorized person possesses the corresponding unique acknowledgment number, then the corresponding medical data can be accessed easily.

Usually, people tend to provide incomplete data. Therefore, apart from checking the authenticity of the medical data, a quality of the medical data may have to be checked. Therefore, the data acknowledgment module (60) is also configured to identify a comprehension level of the corresponding medical data based on a plurality of second historic datasets, using the NLP technique, upon generating the unique acknowledgment number. The data acknowledgment module (60) is also configured to generate a data quality score corresponding to the quality of the corresponding medical data based on the comprehension level of the corresponding medical data. In one exemplary embodiment, the plurality of second historic datasets may include the plurality of first historic datasets. In another exemplary embodiment, the plurality of second historic datasets may include the plurality of first historic datasets, a plurality of details corresponding to a plurality of diseases, a plurality of suggestions from a plurality of physicians, and the like.

Therefore, when the NLP technique may be used, a model may be trained with the plurality of second historic datasets along with the predefined historic data using Al, wherein the model may be used by the data acknowledgment module (60) to generate one or more insights about the comprehension level of the medical data shared by the user. Further, based on the comprehension level, the data quality score may be decided.

Subsequently, the data acknowledgment module (60) is also configured to generate a predetermined reward for the user based on the data quality score corresponding to the quality of the corresponding medical data, thereby managing the collection of the medical data. In one embodiment, the predetermined reward generated may be a good reward only when the data quality score is greater than or matches a threshold quality value. The predetermined reward may be a bad reward, or a poor reward, when the data quality score deviates from the threshold quality value. Basically, a facility of rewarding the user for providing the medical data of certain quality may encourage the user to provide the medical data of a better quality to receive a good reward. In one embodiment, the predetermined reward may include a discount on a certain pharmacy store on a purchase of a certain medicine, a consultation discount at a certain healthcare center, and the like.

In addition, upon identifying that the user is receiving a bad reward, the system (10) may have to identify and suggest one or more ways for the user to provide the medical data with a better quality. Therefore, in an embodiment, the data acknowledgment module (60) may also be configured to generate one or more reference data points as a recommendation for the user, to improve the quality of the corresponding medical data when the data quality score deviates from the threshold quality score. Suppose under the medical device field, the one or more sub-fields may be asking to provide a variety name of a medical device, information of information may be provided by the user. Suppose the user didn’t understand or may be the user is unaware of the variety of the corresponding medical device. Then, the one or more reference data points generated as the recommendation may include a list of names of varieties of the corresponding medical device. Later, upon looking at the list, the user may remember the variety name of the corresponding medical device, and may enter that particular name.

In addition, the user may be willing to receive assistance in understanding on an exact meaning of the medical data being authentic. Therefore, in an embodiment, the processing subsystem (20) may include a data recommendation module (as shown in FIG. 2). The data recommendation module may be operatively coupled to the data validation module (50). The data recommendation module may be configured to generate one or more recommendations for the user based on the plurality of first historic datasets, upon invalidating the corresponding medical data from being authentic. The one or more recommendations may be corresponding to one or more sample entries to provide the user an indication about one or more third data points corresponding to the medical data to be provided, to get the corresponding medical data validated.

In one exemplary embodiment, the medical data may be invalidated because of one or more reasons, wherein the one or more reasons may include spelling mistakes, a mismatch between a medicine taken corresponding to a disease mentioned by the user, a mismatch of symptoms, or the like. Therefore, in an embodiment, the one or more sample entries may include a medicine name with corrected spelling, one or more medicine names that are supposed to be taken of the disease mentioned by the user, actual symptoms that can be caused by the corresponding disease, or the like.

Generally, people are unaware of medicines that may have been recently launched in the market, a newly introduced remedy to several diseases, precautionary measures to be taken to avoid several diseases, and the like. Therefore, to provide such a facility to the user, the processing subsystem (20) may also include a knowledge distribution module (as shown in FIG. 2). The knowledge distribution module may be operatively coupled to the data collection module (40). The knowledge distribution module may be configured to extract a plurality of third historic datasets from a medical database based on a location of the user, upon registration. The medical database may be linked with the distributed ledger and the centralized ledger of the system (10). The medical database may be authorized to be accessed by one or more medical authorities. In one exemplary embodiment, the plurality of third datasets may include information about a plurality of medicines newly launched in the location of the user, a plurality of new remedies for several diseases available in one or more healthcare centers in and around the location of the user, a plurality of banned medicines, and the like.

Also, people are unaware of medicines which may be banned by the government, unethical to use, and the like. Therefore, the knowledge distribution module may also be configured to generate a list of medicines belonging to a predefined category based on the plurality of third historic datasets. The predefined category may include a banned category, a generic category, an approved primitive category, an approved latest category, and the like. Further, the knowledge distribution module may also be configured to distribute knowledge about a plurality of medicines from the medical database, by generating one or more notifications for the user in real-time upon at least one of the generation of the corresponding list of medicines and detection of an update in the corresponding list of medicines. In one embodiment, the one or more notifications may be in a text message form, an email form, a podcast form, a video form, an audio form, or the like.

FIG. 2 is a block diagram representation of an exemplary embodiment of the system (10) for managing the collection of medical data of FIG. 1 in accordance with an embodiment of the present disclosure. For example, say a person ‘X’ (70) is very much health conscious and registers with the system (10) to remain updated with latest information in the healthcare sector. The system (10) includes the processing subsystem (20) hosted on the cloud server (80). The person ‘X’ (70) registers oneself and family members with the system (10) via the registration module (90) by providing a plurality of personal details and a plurality of family -related details via a personal mobile phone (100) of the person ‘X’ (70). The plurality of personal details and the plurality of family -related details are stored in the distributed ledger (110).

Upon registration, the person ‘X’ (70) then starts to fill the plurality of fields and the one or more sub-fields in real-time with the one or more first entries and the one or more second entries respectively via the data collection module (40). Suppose dosage details provided by the person ‘X’ (70) about a family member who is suffering from diabetes is misleading. This is detected via the data validation module (50) based on the plurality of first historic datasets, and hence the dosage details are invalidated from being authenticated. Upon invalidation, the one or more recommendations including one or more appropriate dosages to be taken by diabetes patience is generated via the data recommendation module (120). Upon receiving the one or more recommendations, the person ‘X’ (70) can update the dosage details by cross-checking previously entered dosage details. Then, the medical data provided by the person ‘X’ (70) is now validated to be authentic. Later, the medical data is linked with a unique number say 111012 via the data acknowledgment module (60), wherein the unique number can be used as an identity for accessing the corresponding medical data.

Further, a quality of the medical data is identified to be lower or non-standard via the data acknowledgment module (60), as the medical data provided by the person ‘X’ (70) was not comprehensive. Therefore, the predetermined reward generated for the person ‘X’ (70) by the data acknowledgment module (60) was also of a lower value. To get a better reward, the person ‘X’ (70) can improve the quality of the medical data upon receiving a recommendation to improve from the data acknowledgment module (60). Upon receiving the recommendation, the person ‘X’ (70) provides further details to improve the comprehension level of the corresponding medical data, thereby improving the quality of the same.

Furthermore, on a daily basis, the person ‘X’ (70) keeps on receiving the one or more notifications corresponding to distributing the knowledge about a plurality of medicines from the medical database (125), via the knowledge distribution module (130). The medical database (125) is linked with the distributed ledger (110).

FIG. 3 is a block diagram of a data collection management computer or a data collection management server (140) in accordance with an embodiment of the present disclosure. The data collection management server (140) includes processor(s) (150), and a memory (160) operatively coupled to a bus (170). The processor(s) (150), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.

Computer memory elements may include any suitable memory device(s) for storing data and executable program, such as read only memory, random access memory, erasable programmable read only memory, electrically erasable programmable read only memory, hard drive, removable media drive for handling memory cards and the like. Embodiments of the present subject matter may be implemented in conjunction with program modules, including functions, procedures, data structures, and application programs, for performing tasks, or defining abstract data types or low-level hardware contexts. Executable program stored on any of the above-mentioned storage media may be executable by the processor(s) (150).

The memory (160) includes a plurality of subsystems stored in the form of executable program which instructs the processor(s) (150) to perform method steps illustrated in FIG. 1. The memory (160) includes a processing subsystem (20) of FIG 1. The processing subsystem (20) further has following modules: a data collection module (40), a data validation module (50), and a data acknowledgment module (60).

The data collection module (40) is configured to receive one or more first entries corresponding to a plurality of fields, from a user upon registration. The one or more first entries are corresponding to one or more first data points associated with the medical data corresponding to the user in one or more first forms. The data collection module (40) is also configured to generate one or more sub-fields following the corresponding plurality of fields in a predefined order based on predefined criteria, upon analyzing the one or more first entries using a natural language processing technique in real-time. The one or more sub-fields are linked to the corresponding plurality of fields. The data collection module (40) is also configured to receive one or more second entries corresponding to the one or more sub-fields, from the user, upon generating the corresponding one or more sub-fields in real-time. The one or more second entries are corresponding to one or more second data points associated with the medical data in one or more second forms.

The data validation module (50) is configured to store the medical data corresponding to at least one of the one or more first entries and the one or more second entries on at least one of a distributed ledger (110) by initiating a contract-based process, and a centralized ledger by initiating a data encryption process. The data validation module (50) is also configured to validate the corresponding medical data to be authentic by analyzing the medical data using the natural language processing technique, based on a plurality of first historic datasets

The data acknowledgment module (60) is configured to generate a unique acknowledgment number linked to the medical data corresponding to the user, upon validating the corresponding medical data to be authentic. The data acknowledgment module (60) is also configured to identify a comprehension level of the corresponding medical data based on a plurality of second historic datasets, using the natural language processing technique, upon generating the unique acknowledgment number. The data acknowledgment module (60) is also configured to generate a data quality score corresponding to a quality of the corresponding medical data based on the comprehension level of the corresponding medical data. The data acknowledgment module (60) is also configured to generate a predetermined reward for the user based on the data quality score corresponding to the quality of the corresponding medical data, thereby managing the collection of the medical data.

The bus (170) as used herein refers to be internal memory channel or computer network that is used to connect computer components and transfer data between them. The bus (170) includes a serial bus or a parallel bus, wherein the serial bus transmits data in a bit-serial format and the parallel bus transmits data across multiple wires. The bus (170) as used herein, may include but not limited to, a system bus, an internal bus, an external bus, an expansion bus, a frontside bus, a backside bus, and the like.

FIG. 4 (a) is a flow chart representing steps involved in a method (180) for managing a collection of medical data in accordance with an embodiment of the present disclosure. FIG. 4 (b) is a flow chart representing continued steps involved in the method (180) of FIG. 4 (a) in accordance with an embodiment of the present disclosure. The method (180) includes receiving one or more first entries corresponding to a plurality of fields, from a user upon registration, wherein the one or more first entries are corresponding to one or more first data points associated with the medical data corresponding to the user in one or more first forms in step 190. In one embodiment, receiving the one or more first entries may include receiving the one or more first entries by a data collection module (40).

The method (180) also includes generating one or more sub-fields following the corresponding plurality of fields in a predefined order based on predefined criteria, upon analyzing the one or more first entries using a natural language processing technique in real-time, wherein the one or more sub-fields are linked to the corresponding plurality of fields in step 200. In one embodiment, generating the one or more sub-fields may include generating the one or more sub-fields by the data collection module (40).

Furthermore, the method (180) includes receiving one or more second entries corresponding to the one or more sub-fields, from the user, upon generating the corresponding one or more sub-fields in real-time, wherein the one or more second entries are corresponding to one or more second data points associated with the medical data in one or more second forms in step 210. In one embodiment, receiving the one or more second entries may include receiving the one or more second entries by the data collection module (40).

Furthermore, the method (180) also includes storing the medical data corresponding to at least one of the one or more first entries and the one or more second entries on at least one of a distributed ledger by initiating a contract-based process, and a centralized ledger by initiating a data encryption process in step 220. In one embodiment, storing the medical data may include storing the medical data by a data validation module (50).

Furthermore, the method (180) also includes validating the corresponding medical data to be authentic by analyzing the medical data using the natural language processing technique, based on a plurality of first historic datasets in step 230. In one embodiment, validating the corresponding medical data to be authentic may include validating the corresponding medical data to be authentic by the data validation module (50).

Furthermore, the method (180) also includes generating a unique acknowledgment number linked to the medical data corresponding to the user, upon validating the corresponding medical data to be authentic in step 240. In one embodiment, generating the unique acknowledgment number may include generating the unique acknowledgment number by a data acknowledgment module (60).

Furthermore, the method (180) also includes identifying a comprehension level of the corresponding medical data based on a plurality of second historic datasets, using the natural language processing technique, upon generating the unique acknowledgment number in step 250. In one embodiment, identifying the comprehension level of the corresponding medical data may include identifying the comprehension level of the corresponding medical data by the data acknowledgment module (60).

Furthermore, the method (180) also includes generating a data quality score corresponding to a quality of the corresponding medical data based on the comprehension level of the corresponding medical data in step 260. In one embodiment, generating the data quality score may include generating the data quality score by the data acknowledgment module (60).

Furthermore, the method (180) also includes generating a predetermined reward for the user based on the data quality score corresponding to the quality of the corresponding medical data, thereby managing the collection of the medical data in step 270. In one embodiment, generating the predetermined reward may include generating the predetermined reward by the data acknowledgment module (60). In one exemplary embodiment, the method (180) may further include generating one or more reference data points as a recommendation for the user to improve the quality of the corresponding medical data, when the data quality score deviates from a threshold quality score. In such an embodiment, generating the one or more reference data points may include generating the one or more reference data points by the data acknowledgment module (60).

Further, in one exemplary embodiment, the method (180) may further include generating one or more recommendations for the user based on the plurality of first historic datasets, upon invalidating the corresponding medical data from being authentic, wherein the one or more recommendations are corresponding to an indication for the user to provide the medical data, wherein the corresponding medical data is authentic. In such an embodiment, generating the one or more recommendations may include generating the one or more recommendations by a data recommendation module (120).

Furthermore, in one exemplary embodiment, the method (180) may further include extracting a plurality of third datasets from a medical database based on a location of the user, upon registration, wherein the medical database is authorized to be accessed by one or more medical authorities, wherein the medical database is linked with the distributed ledger and the centralized ledger of the system. In such an embodiment, extracting the plurality of third datasets may include extracting the plurality of third datasets by a knowledge distribution module (130).

Moreover, the method (180) may also include generating a list of medicines belonging to a predefined category based on the plurality of third datasets, wherein the predefined category comprises a banned category, a generic category, an approved primitive category, and an approved latest category. In such embodiment, generating the list of medicines may include generating the list of medicines by the knowledge distribution module (130).

In addition, the method (180) may also include distributing knowledge about a plurality of medicines from the medical database, by generating one or more notifications for the user in real-time upon at least one of the generation of the corresponding list of medicines and detection of an update in the corresponding list of medicines. In such embodiment, distributing the knowledge about the plurality of medicines may include distributing the knowledge about the plurality of medicines by the knowledge distribution module (130).

Further, from a technical effect point of view, the implementation time required to perform the method steps included in the present disclosure by the one or more processors of the system is very minimal, thereby the system maintains very minimal operational latency and requires very minimal processing requirements.

Various embodiments of the present disclosure enable managing the collection of the medical data of the user much easier, and more efficiently, as the authenticity and the quality of the medical data can be improved using the system. Also, the user is encouraged to provide comprehensive and genuine medical data by providing the user with rewards, thereby assuring the collection of genuine medical data.

Further, the medical data is collected in a triaging form and is stored in Blockchain or in encrypted form, thereby ensuring confidentiality and immutability of the corresponding medical data. This also helps not only pharma companies to plugin to the system for their Pharmacovigilance or regulatory works but also Country government Health Authorities (HA) such as a Central Drugs Standard Control Organization of India (CDSCO) and that Country specific HA.

Moreover, the system also assists in spreading awareness about banned medicines or vaccines and educating people about latest medicines and vaccines, by reading the of the people accessing the system. This enables the people to be prepared for future pandemic diseases to cater to better health for future generations, thereby making the system more efficient and more reliable.

Furthermore, mandating the people to provide evidence about the medications they are taking, makes the people responsible, and the people avoid going for home-based usages of Allopathy medicines and understand that the medications should be taken under a proper health care practitioner. Overall, the system is user-friendly and welcoming to really make use of the system not only to report adverse events but as well to enrich one’s own medical knowledge to sustain a healthy life. While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein. The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.

Claims

WE CLAIM:

1. A system (10) for managing a collection of medical data comprising: a processing subsystem (20) hosted on a server (30), and configured to execute on a network to control bidirectional communications among a plurality of modules comprising: a data collection module (40) configured to: receive one or more first entries corresponding to a plurality of fields, from a user upon registration, wherein the one or more first entries are corresponding to one or more first data points associated with the medical data corresponding to the user in one or more first forms; generate one or more sub-fields following the corresponding plurality of fields in a predefined order based on predefined criteria, upon analyzing the one or more first entries using a natural language processing technique in real-time, wherein the one or more sub-fields are linked to the corresponding plurality of fields; and receive one or more second entries corresponding to the one or more sub-fields, from the user, upon generating the corresponding one or more sub-fields in real-time, wherein the one or more second entries are corresponding to one or more second data points associated with the medical data in one or more second forms; a data validation module (50) operatively coupled to the data collection module (40), wherein the data validation module (50) is configured to: store the medical data corresponding to at least one of the one or more first entries and the one or more second entries on at least one of a distributed ledger (110) by initiating a contract-based process, and a centralized ledger by initiating a data encryption process; and validate the corresponding medical data to be authentic by analyzing the medical data using the natural language processing technique, based on a plurality of first historic datasets; and a data acknowledgment module (60) operatively coupled to the data validation module (50), wherein the data acknowledgment module (60) is configured to: generate a unique acknowledgment number linked to the medical data corresponding to the user, upon validating the corresponding medical data to be authentic; identify a comprehension level of the corresponding medical data based on a plurality of second historic datasets, using the natural language processing technique, upon generating the unique acknowledgment number; generate a data quality score corresponding to a quality of the corresponding medical data based on the comprehension level of the corresponding medical data; and generate a predetermined reward for the user based on the data quality score corresponding to the quality of the corresponding medical data, thereby managing the collection of the medical data.

2. The system (10) as claimed in claim 1, wherein the data acknowledgment module (60) is configured to generate one or more reference data points as a recommendation for the user, to improve the quality of the corresponding medical data when the data quality score deviates from a threshold quality score.

3. The system (10) as claimed in claim 1, wherein the processing subsystem (20) comprises a data recommendation module (120) operatively coupled to the data validation module (50), wherein the data recommendation module (120) is configured to generate one or more recommendations for the user based on the plurality of first historic datasets, upon invalidating the corresponding medical data from being authentic, wherein the one or more recommendations are corresponding to one or more sample entries to provide the user an indication about one or more third data points corresponding to the medical data to be provided, to get the corresponding medical data validated.

4. The system (10) as claimed in claim 1, wherein the processing subsystem (20) comprises a knowledge distribution module (130) operatively coupled to the data collection module (40), wherein the knowledge distribution module (130) is configured to extract a plurality of third historic datasets from a medical database (125) based on a location of the user, upon registration, wherein the medical database (125) is linked with the distributed ledger (110) and the centralized ledger of the system (10).

5. The system (10) as claimed in claim 4, wherein the knowledge distribution module (130) is configured to generate a list of medicines belonging to a predefined category based on the plurality of third historic datasets, wherein the predefined category comprises a banned category, a generic category, an approved primitive category, and an approved latest category.

6. The system (10) as claimed in claim 5, wherein the knowledge distribution module (130) is configured to distribute knowledge about a plurality of medicines from the medical database (125), by generating one or more notifications for the user in real-time upon at least one of the generation of the corresponding list of medicines and detection of an update in the corresponding list of medicines.

7. A method (180) for managing a collection of health data comprising: receiving, by a data collection module (40), one or more first entries corresponding to a plurality of fields, from a user upon registration, wherein the one or more first entries are corresponding to one or more first data points associated with the medical data corresponding to the user in one or more first forms; (190) generating, by the data collection module (40), one or more sub-fields following the corresponding plurality of fields in a predefined order based on predefined criteria, upon analyzing the one or more first entries using a natural language processing technique in real-time, wherein the one or more sub-fields are linked to the corresponding plurality of fields; (200) receiving, by the data collection module (40), one or more second entries corresponding to the one or more sub-fields, from the user, upon generating the corresponding one or more sub-fields in real-time, wherein the one or more second entries are corresponding to one or more second data points associated with the medical data in one or more second forms; (210) storing, by a data validation module (50), the medical data corresponding to at least one of the one or more first entries and the one or more second entries on at least one of a distributed ledger by initiating a contract-based process, and a centralized ledger by initiating a data encryption process; (220) validating, by the data validation module (50), the corresponding medical data to be authentic by analyzing the medical data using the natural language processing technique, based on a plurality of first historic datasets; (230) generating, by a data acknowledgment module (60), a unique acknowledgment number linked to the medical data corresponding to the user, upon validating the corresponding medical data to be authentic; (240) identifying, by the data acknowledgment module (60), a comprehension level of the corresponding medical data based on a plurality of second historic datasets, using the natural language processing technique, upon generating the unique acknowledgment number; (250) generating, by the data acknowledgment module (60), a data quality score corresponding to a quality of the corresponding medical data based on the comprehension level of the corresponding medical data; and (260) generating, by the data acknowledgment module (60), a predetermined reward for the user based on the data quality score corresponding to the quality of the corresponding medical data, thereby managing the collection of the medical data (270).

8. The method (180) as claimed in claim 7, comprising generating, by the data acknowledgment module (60), one or more reference data points as a recommendation for the user to improve the quality of the corresponding medical data, when the data quality score deviates from a threshold quality score.

9. The method (180) as claimed in claim 7, comprising generating, by a data recommendation module (120), one or more recommendations for the user based on the plurality of first historic datasets, upon invalidating the corresponding medical data from being authentic, wherein the one or more recommendations are corresponding to one or more sample entries to provide the user an indication about one or more third data points corresponding to the medical data to be provided, to get the corresponding medical data validated.

10. The method (180) as claimed in claim 7, comprising: extracting, by a knowledge distribution module (130), a plurality of third historic datasets from a medical database based on a location of the user, upon registration, wherein the medical database is linked with the distributed ledger and the centralized ledger of the system; generating, by the knowledge distribution module (130), a list of medicines belonging to a predefined category based on the plurality of third historic datasets, wherein the predefined category comprises a banned category, a generic category, an approved primitive category, and an approved latest category; and distributing, by the knowledge distribution module (130), knowledge about a plurality of medicines from the medical database, by generating one or more notifications for the user in real-time upon at least one of the generation of the corresponding list of medicines and detection of an update in the corresponding list of medicines.