An apparatus for data fault detection and repair is disclosed. The apparatus comprises at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a user profile relating to a user, wherein the user profile comprises at least provider data of a user. The memory instructs the processor to generate practitioner data as a function of the user profile. The memory additionally instructs the processor to retrieve remittance data as a function of the practitioner data. The memory then instructs the processor to identify a data fault in at least one of the practitioner data and the remittance data. The memory instructs the processor to initiate a data correction action based on the identified data fault.

A computer-implemented system that enables a live virtual inspection to transform into a self-guided inspection includes: an inspector platform and at least one field agent platform, the system configured to: establish a data connection between the inspector platform and the at least one field agent platform; receive a download of a guidesheet document at the at least one field agent platform, the guidesheet including information indicative of an inspection type and identification of required artifacts for the inspection type; enable upload of artifacts from the at least one field agent platform to the inspector platform via the data network; detect a loss of the data connection; enable the at least one field agent platform to continue collection of artifacts for local storage during the loss of the data connection; and enable the at least one field agent platform to upload artifacts from local storage after the data connection is restored.

A machine learning based (ML-based) method and system for processing claims in a claim decision readiness ecosystem for first users is disclosed. The ML-based method comprises obtaining data in view of first forms associated with the claims from communication devices associated with first users; categorizing the first forms into claim type and claim characteristics documents, regulatory requirement documents, and insurance carrier business rule documents; generating claim decision readiness scores based on receipt, non-receipt, completeness, and incompleteness, of the categorized first forms and associated data fields, using a claim decision readiness scoring tool; executing automated workflow channels based on the generated claim decision readiness scores with pre-defined business rules; validating data in the data fields using redundant and repetitive questions across the categorized first forms; updating the claim decision readiness scores and the automated workflow channels, to adjudicate the claims.

A system and method for knowledge graph data structure based machine learning capable of processing claim documents to reduce cycle time and improve client experience and return to work outcomes is proposed. The system uses a Large Language Model (LLM), having a name entity recognition model and next best action engine, to identify claim specific data. The claim specific data may include keywords and phrases which are related to a user's claim or history. A pre-trained knowledge graph data structure trained based on a corpus of similar historical treatments is provided to the next best action engine in combination with the user's information for operating in inference mode. The nest best action engine contains triggers which correspond to the identified entities or relationships, and automatically generates an output data structure.

Systems and methods are provided for a medical software platform for enabling authenticated communications between various entities to, for example, expedite a worker compensation process between the entities. For example, the medical software platform can receive healthcare and claim data as it is determined and identify relevant parties and deadlines that need to receive the data and respond to the data. This information may be associated with a patient profile. The medical software platform can provide various end users with instructions and data on how to follow and navigate the care of a worker compensation patient. The medical software platform can reduce the overall time in providing timely treatment to the patients and ensure that all the entities involved in the care of the patient are all on the same page.

The present invention relates to a body fluid management server and a body fluid management method using the server, the server comprising: an interface unit for performing communication interface between user equipment, which is communication-connected to a personal sperm measurement apparatus, and a clinic sperm measurement apparatus; a body fluid analysis unit for acquiring the state information of sperm by analyzing clinic sperm data and personal sperm data received via the interface unit, and providing the state information with respect to the personal sperm data to the user equipment and the clinic sperm measurement apparatus or a clinic terminal to enable monitoring by a user and a clinic authorized person; a body fluid state determination unit for determining the final state of the sperm of the relevant user by means of the state information of the sperm acquired by the body fluid analysis unit; a health management information generation unit for generating health management information suitable for the user in accordance with the level of the management state from among the final state determined by the body fluid state determination unit, and providing the same to the user equipment; a measurement data storage unit for storing clinic sperm data and personal sperm data of respective users; and a health management information storage unit for storing health management information provided to respective users.

Techniques for generating an efficacy index that statistically reflects an efficacy of a treatment used to address a medical condition are disclosed. A service identifies a transition timestamp reflective of when a patient begins the treatment. The service accesses a first set of claims associated with the patient. The service determines a static claim metric for the patient's medical condition using a total number of claims included in the first set of claims and certain timestamp data. The service accesses a second set of claims associated with the patient's medical condition. The service determines dynamic claim metrics for the patient's medical condition using the second set of claims and other timestamp data. The service generates a patient-level index based on the static claim metric and the dynamic claim metrics. The service aggregates the patient-level index with other patient-level indexes to generate an overall efficacy index for the treatment.

Techniques for automatically detecting inactive addresses of providers using unsupervised learning approaches are provided. The techniques include determining an activity trend of an address. Responsive to determining that the address is associated with increasing activity, determining an active metric of the address, which indicates a likelihood of the address being active. Responsive to determining that the address is associated with decreasing activity, determining an inactive metric of the address, which indicates a likelihood of the address being inactive. The techniques further include determining whether the address is active or inactive based on the active metric and/or inactive metric. In some embodiments, the active metric or inactive metric is a weighted sum of z scores determined based on Gaussian distributions generated with respect to various benchmarks or provider features. In some embodiments, the weights used to determine the active metric or inactive metric are determined using RLHF techniques.

The following relates generally to AI-based review of insurance claims complaints. In some embodiments, one or more processors: (1) receive, via a chatbot, an insurance claim complaint; (2) categorize, via the chatbot, the insurance claim complaint by determining a category of the insurance claim complaint, the category comprising a tone category or a policy category; (3) build, via the chatbot, a complaint report including information of the insurance claim complaint and an indication of the category; and/or (5) send, via the chatbot, the complaint report to an insurance complaint administrator computing device.

A medical record system and method are disclosed which enables a SOAP note for a patient encounter to be generated and stored in an electronic medical record for the patient. The content of the SOAP note is initially auto-populated by the system in response to information received from the patient and the practitioner.