WO2022162421A1 - System and methods for providing an integrated digital pharmacovigilance automation platform with full compliance with real-time analytics and safety alerts - Google Patents
System and methods for providing an integrated digital pharmacovigilance automation platform with full compliance with real-time analytics and safety alerts Download PDFInfo
- Publication number
- WO2022162421A1 WO2022162421A1 PCT/IB2021/050671 IB2021050671W WO2022162421A1 WO 2022162421 A1 WO2022162421 A1 WO 2022162421A1 IB 2021050671 W IB2021050671 W IB 2021050671W WO 2022162421 A1 WO2022162421 A1 WO 2022162421A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- pharmacovigilance
- providing
- user
- module
- integrated digital
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract description 53
- 238000012517 data analytics Methods 0.000 claims abstract description 8
- 238000001514 detection method Methods 0.000 claims abstract description 7
- 239000003814 drug Substances 0.000 claims description 37
- 229940079593 drug Drugs 0.000 claims description 31
- 230000001105 regulatory effect Effects 0.000 claims description 13
- 238000004801 process automation Methods 0.000 claims description 9
- 238000010801 machine learning Methods 0.000 claims description 8
- 229960005486 vaccine Drugs 0.000 claims description 5
- 230000002411 adverse Effects 0.000 claims description 4
- 238000013473 artificial intelligence Methods 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 claims description 3
- 239000002537 cosmetic Substances 0.000 claims description 2
- 102000004190 Enzymes Human genes 0.000 claims 1
- 108090000790 Enzymes Proteins 0.000 claims 1
- 238000003326 Quality management system Methods 0.000 claims 1
- 231100000279 safety data Toxicity 0.000 claims 1
- 230000008569 process Effects 0.000 description 12
- 230000000694 effects Effects 0.000 description 8
- 238000012545 processing Methods 0.000 description 8
- 238000004590 computer program Methods 0.000 description 7
- 230000036541 health Effects 0.000 description 7
- 230000006870 function Effects 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 4
- 230000008901 benefit Effects 0.000 description 4
- 238000004891 communication Methods 0.000 description 4
- 230000010354 integration Effects 0.000 description 4
- 230000033228 biological regulation Effects 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 3
- 239000003795 chemical substances by application Substances 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000011835 investigation Methods 0.000 description 3
- 238000007726 management method Methods 0.000 description 3
- 238000012552 review Methods 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- 206010067484 Adverse reaction Diseases 0.000 description 2
- 229940022962 COVID-19 vaccine Drugs 0.000 description 2
- 230000006838 adverse reaction Effects 0.000 description 2
- 230000034994 death Effects 0.000 description 2
- 231100000517 death Toxicity 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000012946 outsourcing Methods 0.000 description 2
- 230000002265 prevention Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 208000030453 Drug-Related Side Effects and Adverse reaction Diseases 0.000 description 1
- 241000282412 Homo Species 0.000 description 1
- 206010061218 Inflammation Diseases 0.000 description 1
- 241001465754 Metazoa Species 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000013475 authorization Methods 0.000 description 1
- 230000001364 causal effect Effects 0.000 description 1
- 230000008094 contradictory effect Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 230000000857 drug effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000036039 immunity Effects 0.000 description 1
- 230000004054 inflammatory process Effects 0.000 description 1
- 230000002045 lasting effect Effects 0.000 description 1
- 230000000670 limiting effect Effects 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000002483 medication Methods 0.000 description 1
- 238000003058 natural language processing Methods 0.000 description 1
- 239000002547 new drug Substances 0.000 description 1
- 239000000825 pharmaceutical preparation Substances 0.000 description 1
- 229940127557 pharmaceutical product Drugs 0.000 description 1
- 230000000144 pharmacologic effect Effects 0.000 description 1
- 230000005180 public health Effects 0.000 description 1
- 230000002829 reductive effect Effects 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
- 238000012502 risk assessment Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 230000036962 time dependent Effects 0.000 description 1
- 231100000027 toxicology Toxicity 0.000 description 1
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06395—Quality analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/22—Social work
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H70/00—ICT specially adapted for the handling or processing of medical references
- G16H70/40—ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Abstract
A method for providing an integrated digital pharmacovigilance automation platform with full compliance includes the steps of, providing a single click and a single login access to a user to access the integrated digital pharmacovigilance automation platform with real-time data analytics based on integrating multiple data sources for signal detection and risk management; providing a single click pharmacovigilance compliance option to the user via the integrated digital pharmacovigilance automation platform; providing a multiple integrated data sets to the user for safety; and, providing an automated workflow to the user for accessing and using a multiple features of the integrated digital pharmacovigilance automation platform.
Description
SYSTEM AND METHODS FOR PROVIDING AN INTEGRATED DIGITAL
PHARMACOVIGILANCE AUTOMATION PLATFORM WITH FULL COMPLIANCE WITH REAL-TIME ANALYTICS AND SAFETY ALERTS
FIELD OF THE INVENTION
Embodiments of the present invention generally relate to the field of pharmacovigilance, and, more particularly, to systems and methods for providing an integrated digital pharmacovigilance automation platform with full compliance in real-time as well as detailed safety analytics and alerts in real time.
BACKGROUND OF THE INVENTION
Generally, it is well known that when it comes to pharmaceutical industry, the safety of drugs, devices, vaccines, cosmetics, veterinary and consumer products are regulated under FDA and similar health agencies globally. Considering the fact that the stakes are high across the entire spectrum of this industry, the pharmaceutical sector is a highly regulated space to ensure safety of humans and animals as they might get exposed. Consequently, every pharma company needs to comply with the regulations to ensure strict compliance, so patients are safe.
However, as it is also well known, the complete compliance in pharmaceutical industry get affected due to a large number of problems, such as, for example, data from multiple sources, data integration, cumbersome process to adhere to regulation which increases the risk of compliance, manage safety profile of the drug and risk management process, time bound activities, redundancy due to
outsourcing and complexity managing multiple global regulatory requirements, thereby resulting in inefficiencies and cost burden. Additionally, it is reported that adverse drug reactions was the fourth leading cause of death. In US alone there are 2 million adverse events reported with 100,000 deaths every year reported by FDA.
At present, various stakeholders of the pharmaceutical industry opt for existing solutions to solve such problems, which primarily include outsourcing and consulting services. However, such solutions which aren’t scalable as the current tools and data are in silos where in there are applications for each function adding to the complexity, inefficiency, risk of non compliance and cost. This keeps the data in silos making the analysis cumbersome causing delay in risk assessment and resultant communication the safety information. This is not acceptable for the current situation with millions of healthy people who will be receiving multiple Covid-19 vaccines as they get approved.
Generally, Pharmacovigilance (PV) is also termed as Drug/vaccine Safety Surveillance. PV is a pharmacologic science associated with the collection, detection, assessment, monitoring, and prevention of pharmaceutical products and their adverse effects. This process ensures that the regulatory authorities consider the benefits and risks throughout the life cycle of a drug. It also allows detecting other various serious effects and to identify new drug safety signals. This process of collection, detection, assessment, monitoring, and prevention involves medical information that can be collected from the patients, healthcare providers, medical literature, physicians, pharmaceutical company’s sales team, pharmacists, etc. The information collected from different sources must be processed in a defined consistent way. It is done for the electronic
submission to the regulatory authorities like FDA (Food and Drug Authority), WHO (World Health Organisation), MHRA (Medicines and Health Regulatory Agency), EMA (European Medicines Agency), and other local authorities across the globe based on the marketing authorization. It is also important for pharmaceutical companies to engage in pharmacovigilance to attend to public health and to gain trust from their patients who use the drug/vaccines. It further ensures that the product is not withdrawn from the market due to safety issues. Presently, the pharmacovigilance system is based on consistent and accurate acquisition, integration, and analysis of adverse event data. It has been observed from studies that around 30% of all drug reactions occur from simultaneous use. Another observation was made where around 29.4% of elderly patients are on 6 or more drugs. Many drug safety papers came to a conclusion where the adverse effects of drugs were detected too late when the majority of patients have already been exposed to the drugs and their effects. For a long time, researchers have been seeking and waiting for the opportunity to obtain unambiguous drug reaction relationships to automate the narrative generation process. Although, it can be a time-consuming process to acquire numerous medical records of a single patient. Maintaining vigilance over a drug's effect on patients is a big challenge. It is further compounded by the fact that the drug may be given to the majority of patients at the time of trial and in the market. Moreover, the government agencies like FDA, need quick and comprehensive reporting of this information.
To analyze drug protection incidents and to develop descriptions in the pharmacovigilance method are based on manual examination of case reports from patients, consumers, and healthcare professionals. These methods
further consist of literature searching, case screening, case processing, narrative generation, and medical review. Due to the complexity of data and the need for ensuring reduced costs and quality of reporting. These methods are not well suited and are time-consuming and expensive. Case processing and narrative generation can be time-consuming and the medical review process can be repetitive. One of the major drawbacks is the lack of availability of medical personnel to perform the task. Also, the meaningful analysis of the data requires the identification of complex relationships which is not readily apparent to the trained professionals.
Computer-based systems have been developed to handle this problem but already existing computer-based systems can only perform natural language processing in a limited capacity. These systems unable to investigate the relationships between the drugs, diseases, and reactions in a sufficiently robust and complex automated fashion.
The traditional computer-based systems are limited in their ability to identify relationships. This is because the architecture of these systems and processes fail to account for the underlying clinical knowledge databases being dissimilar in their structure and management. Even though there is a need for a collaborative knowledge framework to automate the pharmacovigilance process through semantic integration of the databases, there has not been a successful effort within the enterprise to solidify an alliance between different databases to promote the pharmacovigilance strategy.
Given the limitations in traditional computer-based systems, substantial manual effort is needed after processing the clinical text. Even though medical reviewers are only familiar with a limited number of data sources and are prone
to human error. Therefore, the manual identification of relationships in the clinical text is not always accurate.
Most of the text analytics-related task in the pharmacovigilance space has been inhibited to theoretical and investigation objectives only. It addresses only a few of the sub-processes involved in the complete process chain. The complete end-to-end processing of Adverse Event (AE) is not supported by the systems. Human intervention can cause issues like inconsistency, inaccuracy, and incomplete report generation. This has led to losing the trust of end-users and the regulatory authorities. Solutions like typical machine learning processes applying hidden Markov models that have proven to be inadequate. Such failure is due to the lack of suitable and required data, time, and monetary expenses. In addition to the above, with the abrupt rise in healthcare data production, the capacity to reliably stock substantial medical data has come to a growing intricacy in composing, scanning, and unearthing data characteristics within massive data storages. One outcome is that conventional methods for scanning health and medical data for required components, like keyword searching, Boolean operators, and advanced search are inadequate to gather desired data from huge storages because just a minor mismatch between, for instance, a keyword and data incorporated in a report, may ensue in it being excluded from the investigation results.
Also, the existence of a keyword in too many reports within a data surge may stem from an over-inclusive investigation, generating outcomes that are too voluminous for a person to survey in a reasonable amount of time. Moreover, a keyword link can need intelligence and elicit data examination outcomes that
merge reports simply on the rationale of having a word, even though that word has considerable different connotations in the reports.
Also, individuals may have a strong intuitive sense of what information is valuable within a set of results, but may not be competent to formulate keywords that suitably indicate that reasoning. Thus, a shortage exists for reporting and data spotting techniques in pharma industry.
Moreover, the clinical examinations must be supervised before a medication is retailed. Regulatory agents like the US Food and Drug Administration (FDA) agree to authorize medications founded on tests of both the safety and the potency of the medication, as assessed in clinical examinations. After the medicines are retailed, a specific quantity of supplementary “post-market” security examination is mandated. Relying on the extent of threat associated with the medicine, this may range from reasonable supervision to particular additional surveys, as inferred by the regulatory agencies. Post-market safety surveys are needed as it is not credible to completely verify the immunity of a medicine in a controlled clinical examination.
Clinical examinations commonly eliminate customers with severe illnesses other than the one under review, since such customers would largely confuse the insights of the findings. Susceptible groups like youngsters and expectant mothers may similarly be omitted. Nonetheless, all of these aspects of customers may be revealed to the medicine after trials are over and it has been authorized and is on the market. Also, there is the likelihood of advancing exchange with original drugs, or Also lifestyle components, which may not subsist during the clinical trials. Drug exchanges in general, and exchanges
between medicines and co-morbid circumstances, are of tremendous interest in the area of pharmacovigilance.
Furthermore, various strategies for post-market pharmacovigilance are placed to recognize safety indications from traded medicines. A “safety signal” relates to a question about a surplus of negative episodes contradicting what would be predicted if the incident of such circumstances were unrelated to a medicine's practice. Regulatory agents like the FDA have organized projects by which health care agents and customers can document negative circumstances to drug manufacturers and to the FDA itself. In a few examples, these statements may be very comprehensive, and may even comprise empirical re-challenge of a customer with medicine to test reoccurrence of non-serious negative effects, like an inflammation.
In this kind of circumstance, a causal connection between the medicine and the negative effect is needed for statistical methods. Still, in several examples, limited information is accessible. Statements are recorded into a chart of Individual Safety Reports (ISRs), which normally contain an estimated date of the incident, the medicines that the customer was taking, and conditions for the negative effects which arose. These ISRs are most frequently collected into a matrix that demonstrates the preponderance of various feasible drug-event mixtures. Note that a complicating characteristic in utilizing such information for pharmacovigilance is an absence of data about the quantity of the susceptible population.
With this kind of data, pharmacovigilance techniques depend on the detection of disproportionality of a drug-event mixture correlated to the ratio at which the incident transpires with other medicines. Data examination techniques that
have been employed with such data include the Multi-Item Gamma Poisson Shrinker (MGPS), the Proportional Reporting Ratio (PRR) method, and the Bayesian Confidence Propagation Neural Network (BCPNN). Consequently, developing and tracing tendencies in pharmacovigilance data is a lasting issue because of the enormous quantity of data that requires to be evaluated together with the lengthy timeframes over which much of this data is compiled.
Accordingly, there remains a need in the art for innovative, novel, efficient solutions for providing an integrated digital pharmacovigilance automation platform with full compliance.
SUMMARY OF THE INVENTION
The embodiments of the present disclosure have several features, no single one of which is solely responsible for their desirable attributes. Without limiting the scope of the present embodiments as expressed by the claims that follow, their more prominent features will now be discussed briefly. After considering this discussion, and particularly after reading the section entitled “Detailed Description”, one will understand how the features of the present embodiments provide advantages.
In one embodiment, a method for providing an integrated digital pharmacovigilance automation platform with real-time data analytics full compliance includes the steps of, providing a single login and single click access to a user to access the integrated digital pharmacovigilance automation platform; providing a single click pharmacovigilance compliance option to the user via the integrated digital pharmacovigilance automation platform; providing multiple integrated data sets to the user for safety; and, providing an automated
workflow to the user for accessing and using a multiple features of the integrated digital pharmacovigilance automation platform. Our platform also provides simple voice-based search/query options which will enable a user to get to the desired analytics report by simply asking the question in his own words. This technology interface is also an important part of our intellectual property
BRIEF DESCRIPTION OF THE DRAWINGS
Fig. 1 and Fig. 2 illustrate flow diagrams of the method for integrated digital PV automation platform, in accordance with embodiments of the present invention.
DETAILED DESCRIPTION
Various embodiments of the present invention are aimed at providing novel and inventive solutions for providing an integrated digital pharmacovigilance automation platform with full compliance. Specifically, the systems and methods as disclosed herein are aimed at providing the users with an integrated digital platform to operate pharmacovigilance with enhanced compliance and efficiency as per regulations ensuring patient safety.
The instant invention is aimed at solving the problems associated with the prior art, and to provide various novel and inventive aspects to the users, which have significant advantages, such as, for example, but not limited to, cost benefits, complete compliance, more accurate analytics and predictions and, faster communication to prevent the lives of the patients who are taking multiple drugs. With Covid-19 vaccines coming out in an accelerated manner, the systems and methods as disclosed herein are aimed at disclosed a platform for real-time data analysis and communication to prevent mass adverse reactions in healthy population across the globe as a result of vaccines. More particularly,
the systems and methods of the present invention are novel and inventive as the present disclosure provides seamless integration of the data that is required for performing the analysis to determine any reported adverse event is an adverse reaction or not. Additionally, regular reports are generated that might be required to satisfy regulatory requirements. Accordingly, the instant invention solves the issues associated with the prior art, which mostly includes a manual process involving multiple teams within a pharma company.
In accordance with an embodiment of the present invention, a method for providing an integrated digital pharmacovigilance automation platform with full compliance includes the steps of, providing a single click and a single login access to a user to access the integrated digital pharmacovigilance automation platform; providing a single click pharmacovigilance compliance option to the user via the integrated digital pharmacovigilance automation platform; providing a multiple integrated data sets to the user for safety; and, providing an automated workflow to the user for accessing and using a multiple features of the integrated digital pharmacovigilance automation platform.
In accordance with an embodiment of the present invention, the method further includes the step of providing a pharmacovigilance database for storing data in aggregated form.
In accordance with an embodiment of the present invention, the method further includes the step of executing one or more Al (artificial intelligence) algorithms being trained for data analytics and process automation as required by the user. In use, the method further includes the step of executing one or more ML (machine learning) models for data analytics and process automation as required by the user.
In accordance with an embodiment of the present invention, the systems and methods as disclosed herein are aimed at providing artificial intelligence (Al) algorithms and / or machine learning (ML) modules as discussed herein. The AI/ML modules of the instant invention may analyze data from customers and/or third party platforms. Such analyzing of data may include analyzing historical data and / or sample (or reference data) along with other information that may be provided by the user.
In accordance with an embodiment of the present invention, the method further includes the step of providing one or more RPA (robotic process automation) bots for data analytics and process automation as required by the user. In use, the RPA bots are provided by way of one or more systems including one or more processing devices, such as, a server. In use, the server includes one or more computing platforms having a memory and at least one processor in communication with the memory. In operation, the one or more robotic process automation (RPA) bots are stored in the memory, executable by the processor and configured to receive an input command from the user to perform predetermined robotic tasks in response to the input commands as provided by the user, and return outputs in response to performing the predetermined robotic tasks as required by the user.
In accordance with an embodiment of the present invention, the systems and methods as disclosed herein are aimed at providing an integrated PV System covering end to end operations to ensure compliance by just a subscription. The embodiments of the present invention provide an integrated global data sources from all global regulatory agencies, health agencies, literature, Toxicology, Epidemiology, Social Media, Genomics, and the like. In use, the
systems and methods of the instant invention disclose an integrated workflow automation tool for signal detection, risk management and Authoring, reviewing, finalizing aggregate reports, queries from health agencies and PV partners (PV Agreement holders) submission capabilities, along with predictive analytics, voice based interacting system, prediction capabilities, and the like.
In accordance with an embodiment of the present invention, the systems and methods as provided by instant invention are disclosed herein with reference to the accompanying drawings, in which some, but not all, embodiments of the invention are shown. Indeed, the invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements. Like numbers refer to like elements throughout.
Those of ordinary skills in the art will appreciate that the systems and methods of the present invention may be embodied as an apparatus (e.g., a system, computer program product, and/or other device), a method, or a combination of the foregoing. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.), or an embodiment combining software and hardware aspects that may generally be referred to herein as a “system.”
Furthermore, embodiments of the present invention may take the form of a computer program product comprising a computer-usable storage medium having computer-usable program code/computer-readable instructions embodied in the medium. Any suitable computer-usable or computer-readable
medium may be utilized. The computer usable or computer-readable medium may be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device.
More specific examples (e.g., a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection having one or more wires; a tangible medium such as a portable computer diskette, a hard disk, a time-dependent access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a compact disc read-only memory (CD-ROM), or other tangible optical or magnetic storage device.
Computer program code/computer-readable instructions for carrying out operations of embodiments of the present invention may be written in an object oriented, scripted or unscripted programming language such as JAVA, PERL, Python, Angular JS, , C++ and the like. However, the computer program code/computer-readable instructions for carrying out operations of the invention may also be written in conventional procedural programming languages, such as the “C” programming language or similar programming languages.
In addition, the systems and methods as disclosed herein further disclose various aspects that can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a particular machine, such that the instructions, which execute by the processor of the computer or other programmable data processing apparatus, create mechanisms for implementing the functions/acts specified in the flowchart and/or block diagram
block or blocks. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions, which implement the function/act specified in the flowchart and/or block diagram block or blocks.
Those of ordinary skills in the art will further appreciate that “bot” as used herein may refer to a software application that performs automated tasks. In particular, a plurality of bots may be used by an entity to perform various functions for high-volume applications that relate to the entity's objectives. Typically, a bot will be configured to repeatedly perform a specific task. Each bot may be configured to utilize particular protocols and be compatible with particular platforms and applications. In some embodiments, a bot may be configured to execute its tasks by interacting with other applications within the entity's systems at the interface level (i.e., by providing inputs to the interfaces of the other applications).
Those of ordinary skills in the art will further appreciate that a suffix such as “module” and “unit” may be used to refer to elements or components. Use of such a suffix herein is merely intended to facilitate description of the specification, and the suffix itself is not intended to give any special meaning or function. In the present disclosure, that which is well-known to one of ordinary skill in the relevant art has generally been omitted for the sake of brevity. The accompanying drawings are used to help easily understand various technical features and it should be understood that the embodiments presented herein are not limited by the accompanying drawings. As such, the present disclosure
should be construed to extend to any alterations, equivalents and substitutes in addition to those which are particularly set out in the accompanying drawings.
The conditional language used herein, such as, among others, “can,” “could,” “might,” “may,” “e.g.,” and the like, unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without author input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular embodiment. The terms "comprising," "including," 'having," and the like are synonymous and are used inclusively, in an open-ended fashion, and do not exclude additional elements, features, acts, operations, and so forth. Also, the term "or" is used in its inclusive sense (and not in its exclusive sense) so that when used, for example, to connect a list of elements, the term "or" means one, some, or all of the elements in the list.
While there has been shown and described the preferred embodiment of the instant invention it is to be appreciated that the invention may be embodied otherwise than is herein specifically shown and described and that, within said embodiment, certain changes may be made in the form and arrangement of the parts without departing from the underlying ideas or principles of this invention as outlined in the Claims appended herewith. Therefore, the appended claims are to be construed to cover all equivalents falling within the true scope and spirit of the invention.
Claims
1. A method for providing an integrated digital pharmacovigilance automation platform with full compliance, said method comprising the steps of: providing a single click and a single login access to a user to access said integrated digital pharmacovigilance automation platform; providing a single click pharmacovigilance compliance option to said user via said integrated digital pharmacovigilance automation platform; providing a plurality of integrated data sets to said user for safety; and, providing an automated workflow to said user for accessing and using a plurality of features of said integrated digital pharmacovigilance automation platform.
2. The method as claimed in Claim 1 , wherein said method further comprises the step of providing a pharmacovigilance database for storing data in aggregated form with real-time analytics including Qualitative, Quantitative and Cohort analytics with the help of Al to arrive at a causality assessment to the extent of identifying the mechanistic, genomic or enzyme level changes as an impact of the drug, vaccine, devices, cosmetics and consumer products for signal detection and risk management.
3. The method as claimed in Claim 1 , wherein said method further comprises the step of executing at least one Al (artificial intelligence) algorithm being trained for data analytics and process automation as required by said user.
4. The method as claimed in Claim 1, wherein said method further comprises the step of executing at least one ML (machine learning) model for data analytics and process automation as required by said user.
5. The method as claimed in Claim 1, wherein said method further comprises the step of providing at least one RPA (robotic process automation) bot for data analytics and process automation as required by said user.
6. The method as claimed in Claim 1, wherein said method further comprises the step of providing a plurality of modules, including, a signal detection module, multiple aggregate reporting module, safety data exchange agreements module, a PSMF (product safety master file) module, compliance metrics module, a PV quality management system, a clinical trial and adverse event monitoring module, risk management plan and trackers module, PV regulatory intelligence management module, predictive analytics of adverse events module, literature and social media surveillance module, and the like.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/IB2021/050671 WO2022162421A1 (en) | 2021-01-28 | 2021-01-28 | System and methods for providing an integrated digital pharmacovigilance automation platform with full compliance with real-time analytics and safety alerts |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/IB2021/050671 WO2022162421A1 (en) | 2021-01-28 | 2021-01-28 | System and methods for providing an integrated digital pharmacovigilance automation platform with full compliance with real-time analytics and safety alerts |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| WO2022162421A1 true WO2022162421A1 (en) | 2022-08-04 |
Family
ID=82654245
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/IB2021/050671 WO2022162421A1 (en) | 2021-01-28 | 2021-01-28 | System and methods for providing an integrated digital pharmacovigilance automation platform with full compliance with real-time analytics and safety alerts |
Country Status (1)
| Country | Link |
|---|---|
| WO (1) | WO2022162421A1 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP4322085A1 (en) * | 2022-08-11 | 2024-02-14 | Selta Square Co., Ltd. | Method and apparatus for sharing data in intelligent pharmacovigilance platform |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160048655A1 (en) * | 2014-08-14 | 2016-02-18 | Accenture Global Services Limited | System for automated analysis of clinical text for pharmacovigilance |
| US20190272725A1 (en) * | 2017-02-15 | 2019-09-05 | New Sun Technologies, Inc. | Pharmacovigilance systems and methods |
-
2021
- 2021-01-28 WO PCT/IB2021/050671 patent/WO2022162421A1/en active Application Filing
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160048655A1 (en) * | 2014-08-14 | 2016-02-18 | Accenture Global Services Limited | System for automated analysis of clinical text for pharmacovigilance |
| US20190272725A1 (en) * | 2017-02-15 | 2019-09-05 | New Sun Technologies, Inc. | Pharmacovigilance systems and methods |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP4322085A1 (en) * | 2022-08-11 | 2024-02-14 | Selta Square Co., Ltd. | Method and apparatus for sharing data in intelligent pharmacovigilance platform |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ventola | Big data and pharmacovigilance: data mining for adverse drug events and interactions | |
| CN111125061A (en) | Method for standardizing and promoting health medical big data | |
| Kahn et al. | Transparent reporting of data quality in distributed data networks | |
| Wang et al. | Beyond a technical perspective: understanding big data capabilities in health care | |
| US7752057B2 (en) | System and method for continuous data analysis of an ongoing clinical trial | |
| Sahama et al. | A data warehouse architecture for clinical data warehousing | |
| Amantea et al. | A simulation-driven approach to decision support in process reorganization: A case study in healthcare | |
| WO2022162421A1 (en) | System and methods for providing an integrated digital pharmacovigilance automation platform with full compliance with real-time analytics and safety alerts | |
| Natsiavas et al. | A Knowledge-Based Platform for Assessing Potential Adverse Drug Reactions at the Point of Care: User Requirements and Design. | |
| Comden et al. | Using probabilistic risk assessment to model medication system failures in long-term care facilities | |
| Greco et al. | Crew performance variability in human error probability quantification: A methodology based on behavioral patterns from simulator data | |
| Meineke et al. | Design and Concept of the SMITH Phenotyping Pipeline. | |
| Ozmen et al. | Feature engineering and process mining to enable hazard detection in health information technology | |
| Machado et al. | Applying Business Intelligence to Clinical and Healthcare Organizations | |
| Touati et al. | Towards a Digital Collaborative Framework for an Efficient Medication Errors Management | |
| Alghamdi | Health data warehouses: reviewing advanced solutions for medical knowledge discovery | |
| Chilunjika et al. | Blockchain Technology for Health Information Management: A case of Zimbabwe | |
| Clayton et al. | Key intelligence topics (KITs) and key intelligence questions (KIQs) in safety signal intelligence | |
| Klasky et al. | Comparative Assessment of Data-driven Process Models in Health Information Technology | |
| Choe et al. | Development and Application of an Active Pharmacovigilance Framework Based on Electronic Healthcare Records from Multiple Centers in Korea | |
| Hovenga et al. | Clinical decision support systems: data quality management and governance | |
| Rodrigues et al. | Implementing guidelines for causality assessment of adverse drug reaction reports: A bayesian network approach | |
| Blinzakov et al. | An integrated software system for medical equipment management | |
| Mukhlas et al. | Process Improvement Model in Pharmaceutical Packaging Design Process Using Process Mining | |
| Alshammari | Pharmacovigilance in the Arab World |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| 121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 21922714 Country of ref document: EP Kind code of ref document: A1 |
|
| NENP | Non-entry into the national phase |
Ref country code: DE |
|
| 122 | Ep: pct application non-entry in european phase |
Ref document number: 21922714 Country of ref document: EP Kind code of ref document: A1 |