Description:[0042] Some of the embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words "comprising," "having," "containing," and "including," and other forms thereof, are intended to be equivalent in meaning and be open-ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise.

[0043] The following description includes the preferred best mode of one embodiment of the present invention. It shall be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting.

[0044] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art are able to recognize that the invention is not limited to the embodiments of drawings or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in a certain figure, for ease of illustration, and such omissions do not limit the embodiment outlined in any way. It should be understood that the drawings and details thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claim. It is not suggested or represented that any or all of these matters form any part of the prior art base or were common general knowledge in the field relevant to the present invention.

[0045] The present invention is described hereinafter by various embodiments with reference to the accompanying drawings, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure is thorough and complete and conveys fully the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, a number of materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary and are not intended to limit the scope of the invention.

[0046] In an embodiment of the present invention, it includes a real-time analytics-driven health insurance adjudication system, including a database integrated with universally accepted Standard Treatment Guidelines (STGs), International Classification of Diseases (ICD)codes, symptoms, laboratory investigations, and imaging modalities. Embodiments may also include a real-time analytics and scoring engine configured with rule-based algorithms, predictive models, and fraud detection mechanisms to enable dynamic adjudication and actionable insights.

[0047] In another embodiment of the present invention, it also includes a rule-based claims assessment module, configurable with 28 core variables, allowing insurer-specific adjustments to adjudication parameters. Embodiments may also include a customizable slider feature for real-time adjustment of Straight-Through Processing (STP)rates, enabling optimized processing efficiency. Embodiments may also include an end-to-end pathway integration module, incorporating fraud detection mechanisms to ensure integrity in claims adjudication. Embodiments may also include a secure, multi-tenant Software-as-a-Service (SaaS)architecture, built on a Model-View-Controller (MVC)framework, supporting responsive web interfaces and secure communication via RESTful APIs.

[0048] In another embodiment of the present invention, the rule-based algorithms include statistical and predictive analytic tools for dynamic scoring of claims based on live data inputs. The fraud detection mechanisms analyze clinical, financial, and behavioral patterns to identify potentially fraudulent activities. In some embodiments, the analytics engine may be configured to comply with regional healthcare regulations and insurer-specific requirements.

[0049] In another embodiment of the present invention, the claims decision report provides a detailed breakdown of variables influencing the adjudication decision. User inputs are validated against predefined clinical standards before analysis. The method dynamically adjusts scoring thresholds based on historical adjudication performance metrics, enabling consistent and optimized claim assessments.

[0050] In another embodiment of the present invention, the scoring engine benchmarks claims performance against industry-standard adjudication data. Rejected claims are flagged for manual review with automated detailed justifications. The method generates analytics dashboards to monitor claims trends, providing insurers with actionable insights for informed decision-making.

[0051] In another embodiment of the present invention, approved claims are automatically processed for payment upon meeting predefined criteria. Claims data and decisions are securely transmitted through encrypted RESTful APIs. Claims are scored based on parameters including fraud risk, clinical relevance, and financial viability, ensuring comprehensive and accurate adjudication.

[0052] In another embodiment of the present invention, the method integrates external datasets to enhance claims adjudication accuracy. The system provides actionable recommendations for improving policy compliance based on adjudication trends. Users are notified of adjudication outcomes through automated alerts, streamlining communication and decision transparency.

[0053] In another embodiment of the present invention, claims scoring incorporates real-time data from wearable health devices. The system supports simulated claim scenarios for user training and testing purposes. Fraud detection algorithms are periodically updated using global fraud datasets, and the system audits adjudication decisions against predefined benchmarks for accuracy and consistency.

[0054] In another embodiment of the present invention, the method for real-time analytics-driven health insurance claims adjudication includes receiving claim data from a secure interface, encompassing policyholder details, treatment data, and claim-specific documentation. The received claim data is validated against integrated Standard Treatment Guidelines (STGs), International Classification of Diseases (ICD) codes, and region-specific datasets to ensure compliance with medical and policy standards.

[0055] In another embodiment of the present invention, the method processes validated claim data using a real-time analytics engine configured with rule-based algorithms to assess claim validity and alignment with predefined policy parameters. Predictive models forecast claim outcomes based on historical trends, while fraud detection triggers identify anomalous patterns indicative of potential fraud.

[0056] In another embodiment of the present invention, the method generates a dynamic claim risk score and adjudication decision using real-time inputs and multi-criteria scoring thresholds. Claims are categorized into approval, rejection, or manual review categories based on adjudication results. Detailed adjudication reports provide insights into decision parameters, fraud risk levels, and recommendations for further actions.

[0057] In another embodiment of the present invention, rule-based algorithms are dynamically updated to accommodate changes in healthcare regulations, insurer policies, and fraud detection standards. Fraud detection triggers incorporate live feedback from external datasets and machine learning models to continuously enhance detection accuracy. Simulation tools test hypothetical claim scenarios, enabling insurers to train users and optimize adjudication workflows.

[0058] In another embodiment of the present invention, the user interface provides dynamic visualizations for real-time tracking of claims processing metrics. The system supports accessibility through a responsive web interface, allowing multi-device compatibility. The architecture is scalable to handle high claim volumes while maintaining consistent performance and low latency.

[0059] In another embodiment of the present invention, the system integrates IoT-enabled devices, such as wearable health trackers, to enrich claim data with real-time patient health metrics. This capability ensures that adjudication decisions incorporate comprehensive and up-to-date information, enhancing the relevance and accuracy of claim outcomes.

[0060] In another embodiment of the present invention, periodic audits of adjudication decisions are conducted against predefined benchmarks to ensure accuracy and compliance. The system provides insurers with detailed compliance reports, highlighting areas for improvement and ensuring alignment with industry standards.

[0061] In another embodiment of the present invention, the system incorporates features for regional and global fraud monitoring by integrating data from international fraud detection databases. This enhances its ability to detect and mitigate cross-border fraudulent activities, thereby ensuring more secure adjudication processes.

[0062] In another embodiment of the present invention, the system supports predictive simulations for insurers to forecast claim trends based on historical data. This functionality enables insurers to anticipate future workload demands, optimize resource allocation, and adjust operational strategies proactively.

[0063] In another embodiment of the present invention, advanced document recognition and processing tools are integrated into the system to streamline the handling of claim-related documentation. This minimizes errors during data extraction and accelerates the adjudication process by automating repetitive tasks.

[0064] FIG. 1 is a block diagram that describes a real-time analytics-driven health insurance adjudication system 100, according to some embodiments of the present disclosure. In some embodiments, the real-time analytics-driven health insurance adjudication system 100 may include real-time analytics 120. The real-time analytics-driven health insurance adjudication system 100 may also include a database 110 integrated with universally accepted Standard Treatment Guidelines (STGs), International Classification of Diseases (ICD)codes, symptoms, laboratory investigations, and imaging modalities.

[0065] In some embodiments, the real-time analytics-driven health insurance adjudication system 100 may also include a scoring engine 130 configured with rule-based algorithms, predictive models, and fraud detection mechanisms to enable dynamic adjudication and actionable insights. The real-time analytics-driven health insurance adjudication system 100 may also include a rule-based claims assessment module 140, configurable with 28 core variables, allowing insurer-specific adjustments to adjudication parameters. The real-time analytics-driven health insurance adjudication system 100 may also include a customizable slider feature 150 for real-time adjustment of Straight-Through Processing (STP)rates, enabling optimized processing efficiency. An end-to-end pathway integration module, incorporating fraud detection mechanisms to ensure integrity in claims adjudication. A secure, multi-tenant Software-as-a-Service (SaaS)architecture, built on a Model-View-Controller (MVC)framework, supporting responsive web interfaces and secure communication via RESTful APIs.

[0066] In some embodiments, rule-based algorithms may include statistical and predictive analytic tools for dynamic scoring of claims based on live data inputs. In some embodiments, the fraud detection mechanisms may analyze clinical, financial, and behavioral patterns to identify potentially fraudulent activities. In some embodiments, the analytics engine may be configured to comply with regional healthcare regulations and insurer-specific requirements.

[0067] In some embodiments, the scoring engine 130 may incorporate machine learning models to enhance decision-making accuracy based on historical claims data. In some embodiments, the real-time analytics-driven health insurance adjudication system 100 may include an integrated document management system (DMS)for efficient storage and retrieval of case-related documents. In some embodiments, the database 110 may also include at least twelve integrated datasets, with at least four being country specific.

[0068] In some embodiments, the customizable slider feature facilitates real-time adjustments to maintain a balance between adjudication efficiency and accuracy. In some embodiments, the analytics engine may provide a real-time fraud risk score for each claim processed. In some embodiments, system 100 may support interoperability through API integration with third-party healthcare data providers. In some embodiments, the scoring engine 130 may generate insights to optimize claims processing time and turnaround efficiency.

[0069] In some embodiments, fraud detection mechanisms may be dynamically updated to reflect evolving fraud patterns. In some embodiments, the user interface may provide dynamic visualizations for real-time monitoring of claims processing metrics. In some embodiments, the System 100 may be accessible through a responsive web interface supporting multiple device types. In some embodiments, the architecture may be scalable to manage high claim volumes without performance degradation.

[0070] FIG. 2 is a flowchart that describes a method, according to some embodiments of the present disclosure. In some embodiments, at 210, the method may include receiving claim data via a secure web interface. At 220, the method may include comparing the claim data against integrated Standard Treatment Guidelines (STGs), ICD codes, laboratory investigations, and imaging modalities. At 230, the comparing may include analyzing the claim data using a real-time analytics engine incorporating rule-based algorithms and predictive models. At 240, the comparing may include generating a dynamic risk score and adjudication decision based on live data inputs. At 250, the comparing may include generating a claims decision report and storing it in an integrated document management system (DMS). Customizing processing parameters using a configurable slider feature.

[0071] In some embodiments, user inputs may be validated against predefined clinical standards prior to analysis. In some embodiments, dynamic adjustment of scoring thresholds is based on historical adjudication performance metrics. In some embodiments, the scoring engine may benchmark claims performance against industry-standard adjudication data. In some embodiments, rejected claims may be flagged for manual review with an automated detailed justification.

[0072] In some embodiments, integration of external datasets to enhance claims adjudication accuracy. In some embodiments, the system may provide actionable recommendations for improving policy compliance based on adjudication trends. In some embodiments, the method may include notifying users of adjudication outcomes through automated alerts. In some embodiments, claims scoring incorporates real-time data from wearable health devices. In some embodiments, the system may support simulated claim scenarios for user training and testing purposes. In some embodiments, periodic updates to fraud detection algorithms using global fraud datasets. In some embodiments, the system may audit adjudication decisions against predefined benchmarks for accuracy and consistency.

[0073] FIG. 3 is a flowchart that describes a method, according to some embodiments of the present disclosure. In some embodiments, at 302, the method may include receiving claim data from a secure interface, including policyholder details, treatment data, and claim-specific documentation. At 304, the method may include validating the received claim data against integrated Standard Treatment Guidelines (STGs), International Classification of Diseases (ICD)codes, and region-specific datasets to ensure compliance with medical and policy standards.

[0074] In some embodiments, at 306, the method may include processing the validated claim data using a real-time analytics engine configured with: At 308, the method may include generating a dynamic claim risk score and adjudication decision using real-time inputs and multi-criteria scoring thresholds. At 310, the method may include automatically categorizing claims into approval, rejection, or manual review categories based on adjudication results.

[0075] In some embodiments, at 312, the method may include providing insurers with detailed adjudication reports, including insights into decision parameters, fraud risk levels, and recommendations for further actions. At 314, the method may include storing adjudication results and associated documentation in an integrated document management system (DMS)for future retrieval, audits, and compliance tracking. At 316, the method may include enabling real-time tracking of claim metrics and adjudication performance through an interactive analytics dashboard. Rule-based algorithms to assess claim validity and alignment with predefined policy parameters. Predictive models to forecast claim outcomes based on historical trends. Fraud detection triggers identifying anomalous patterns indicative of potential fraud.

[0076] In some embodiments, the method may include updating the rule-based algorithms dynamically to accommodate changes in healthcare regulations, insurer policies, and fraud detection standards. In some embodiments, fraud detection triggers may incorporate live feedback from external healthcare datasets and machine learning models to continuously enhance detection accuracy. In some embodiments, the method may include providing simulation tools within the system to test hypothetical claims scenarios, enabling insurers to train users and optimize adjudication workflows.

[0077] What has been described above includes examples of the disclosed innovation. It is, of course, not possible to describe every conceivable combination of components and/or methodologies, but one of ordinary skill in the art may recognize that many further combinations and permutations are possible. Accordingly, the innovation is intended to embrace all such alterations, modifications, and variations that fall within the spirit and scope of the appended claims. Furthermore, to the extent that the term “includes” is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term “comprising” as “comprising” is interpreted when employed as a transitional word in a claim.

[0078] The terms “an embodiment”, “embodiment”, “embodiments”, “the embodiment”, “the embodiments”, “one or more embodiments”, “some embodiments”, and “one embodiment” mean “one or more (but not all) embodiments of the invention(s)” unless expressly specified otherwise.

[0079] The terms “including”, “comprising”, “having” and variations thereof mean “including but not limited to”, unless expressly specified otherwise.

[0080] The enumerated listing of items does not imply that any or all of the items are mutually exclusive, unless expressly specified otherwise.

[0081] The terms “a”, “an” and “the” mean “one or more”, unless expressly specified otherwise.

[0082] A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention.

[0083] Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the disclosure of the embodiments of the invention is intended to be illustrative, but not limited, of the scope of the invention, which is set forth in the following claims.

[0084] While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.

[0085] Reference Numerals:
Reference Numeral Description
100

Real-time analytics-driven health insurance adjudication system
110
Database integrated with Standard Treatment Guidelines (STGs), ICD codes, and other medical datasets
120
Real-time analytics engine
130
Scoring engine with rule-based algorithms and predictive models
140
Rule-based claims assessment module
150
Customizable slider feature for Straight-Through Processing (STP) adjustment
210
Receiving claim data via a secure web interface
220
Comparing the claim data against integrated STGs, ICD codes, and datasets
230 Analyzing the claim data using the analytics engine
240
Generating a dynamic risk score and adjudication decision
250 )
Generating a claims decision report and storing it in the document management system (DMS)
302
Receiving claim data, including policyholder details and treatment documentation
304
Validating the claim data against integrated medical and policy standards
306
Processing validated claim data with the analytics engine

308

Generating a dynamic claim risk score using real-time inputs
310

Automatically categorizing claims into approval, rejection, or manual review
312

Providing detailed adjudication reports with insights
314

Storing adjudication results and associated documents in the DMS
316
Enabling real-time tracking of claim metrics via analytics dashboard

[0086] In another embodiment, the above disclosure is a description of the invention and is not intended to limit the scope of the invention. Other variations and modifications of the above-described embodiment shall be apparent to those skilled in the art and are intended to fall within the scope of the invention as defined in the following claims.
, Claims:1. A real-time analytics-driven health insurance adjudication system, comprising:
a. a database integrated with Standard Treatment Guidelines (STGs), International
Classification of Diseases (ICD) codes, symptoms, laboratory investigations, and
imaging modalities;
b. a real-time analytics and scoring engine configured with rule-based algorithms,
predictive models, and fraud detection mechanisms to enable dynamic adjudication
and actionable insights;
c. a rule-based claims assessment module configurable with 28 core variables for
insurer-specific adjustments to adjudication parameters;
d. a customizable slider feature for real-time adjustment of Straight-Through
Processing (STP) rates, optimizing processing efficiency;
e. an end-to-end pathway integration module incorporating fraud detection
mechanisms for maintaining claims integrity; and
f.
a secure, multi-tenant Software-as-a-Service (SaaS) architecture built on a Model
View-Controller (MVC) framework with responsive web interfaces and secure
communication via RESTful APIs,
wherein the system is characterized by its ability to adapt dynamically to insurer
requirements and evolving fraud patterns through rule-based algorithms including
statistical and predictive analytic tools for scoring claims based on live data input for
identification of fraudulent activities.
2. The system of claim 1, wherein the analytics engine is configurable to comply with
regional healthcare regulations and insurer-specific requirements.
3. A method for real-time analytics-driven adjudication of health insurance claims,
comprising:
a. receiving claim data via a secure web interface;
b. validating the claim data against integrated Standard Treatment Guidelines (STGs),
ICD codes, laboratory investigations, and imaging modalities;
c. analyzing the validated data using a real-time analytics engine configured with rule
based algorithms, predictive models, and fraud detection triggers;
d. generating a dynamic risk score and adjudication decision based on live data inputs
and multi-criteria scoring thresholds;
e. automatically categorizing claims into approval, rejection, or manual review
categories; and
f.
generating adjudication reports and storing results in an integrated document
management system (DMS) for audits and compliance tracking,
wherein the method is characterized by its capability to provide real-time fraud
detection, configurable workflows, and actionable insights based on live data
processing.
4. The method of claim 3, further comprises updating rule-based algorithms dynamically
to align with regional healthcare regulations, insurer-specific policies, and evolving
fraud detection standards.
5. The method of claim 3, wherein the fraud detection triggers incorporate feedback from
external healthcare datasets and machine learning models to enhance detection
accuracy.
6. The method of claim 3, further comprising generating analytics dashboards for real
time tracking of claims processing metrics and trends.
7. The method of claim 3, wherein rejected claims are flagged for manual review with
detailed automated justifications.
8. The method of claim 3, further enables insurers to simulate hypothetical claim scenarios
for user training and adjudication optimization.