14 results about "Prior authorization" patented technology

A method for adapting a user interface includes accessing a data package. The method includes determining whether execution of an action based on the data package requires secondary authorization. The method includes, in response to a determination that the execution requires secondary authorization, identifying a set of inquiries and determining whether user data includes information associated with the set of inquiries. The method includes, in response to a determination that the user data includes information associated with the set of inquiries, determining a set of responses. The method includes transmitting a request for secondary authorization including the set of responses and a first subset of the user data. The set of responses is based on the first subset of the user data. The method includes transforming the user interface to display the set of responses, the set of inquiries, and the first subset of the user data.

Systems and methods for providing an intelligent network to onboard patients to specialty medications are described herein. A server system for managing communication is configured to receive from a user at a healthcare practice, an electronic request to obtain a medical prior authorization (medPA) for a specialty drug to administer for a patient of the healthcare practice; select, using a rules service, a communication pathway to submit the medPA to a healthcare payer from a plurality of communication pathways; and initiate a connection with the healthcare payer using the selected communication pathway.

A computerized method for analyzing prior authorization documentation includes receiving a first set of information including a set of pages and determining a set of relevant pages. The method includes determining a set of document types associated with the set of relevant pages and performing optical character recognition. The method includes prompting a machine learning model to extract a set of request data. The method includes prompting the machine learning model with a set of prompts associated with a set of authorization criteria to determine a set of authorization data. The method includes, in response to a determination that the set of authorization data has met the set of authorization criteria, approving an authorization request. The method includes, in response to a determination that set of authorization data has not met the set of authorization criteria, rejecting the authorization request.

Described herein are pharmacy claim transaction processing systems and methods. A method for pre-approval of a prior authorization includes receiving a claim transaction message associated with a pharmacy claim transaction for a prescription that satisfies a prior authorization (PA) trigger condition, the message including a plan identifier and a prescriber identifier. The method includes querying a database with the prescriber identifier and plan identifier to access prescriber reference data, healthcare plan provisions, and jurisdictional regulations, as well as applying pre-approval rules to the prescriber reference data, healthcare plan provisions, and jurisdictional regulations, the pre-approval rules defining pre-approval conditions for pharmacy PAs. The method includes, when the pre-approval conditions are met, automatically appending an approval data element to the message to generate a PA approval message and transmitting the PA approval message. The method further includes writing a record to the database including whether the transaction satisfied the pre-approval conditions.

System and methods for automating preauthorization of genetic efficacy testing with prescription dispensation are provided. Coverage requests for medicines are received from pharmacy systems. Databases are queried to retrieve policy rules for the coverage requests. A coverage decision analysis is performed and where an associated medication is otherwise approved for coverage and where genetic efficacy testing is available for the associated medication, a prior authorization for such genetic efficacy testing is automatically generated and included with which is transmitted together with an approval of coverage decision to a requesting one of said number of pharmacy systems.

This is a monitoring approach that respects privacy (no routine biometric recognition) to continuously extract multimodal markers from individuals / patients. The system can use various modules, including cameras, microphones, connected objects, wearables, sensors, etc . . .. This allows for the early detection of ANY pathology whatsoever that can be detected in the continuous extraction of multimodal markers from individuals / patients and the rapid transmission of alerts to emergency medical services. The multimodal monitoring supported by AI algorithms will not only detect the QUALIFICATION of the onset of a pathology, but especially the QUANTIFICATION of this onset of pathology, and even more so to provide a KINETIC MEASURE OF THIS QUANTIFICATION over a significant period of time.ONLY in cases where a detected pathology requires urgent and absolute medical intervention, and if the precision levels of the methods employed have been validated by medical professionals, the approval of the protocol's performance by the medical authority can trigger the use of biometric recognition. The use of biometric recognition allows for access to the patient's medical history, which can strengthen the diagnosis to send an alarm AND / OR accelerate patient care by already having knowledge of their medical files upon arrival in the emergency room. THIS IS A CASE OF FORCE MAJEURE THAT REQUIRES URGENT TREATMENT, GOVERNED BY MEDICAL ETHICS, AND THEREFORE EXEMPT FROM ANY PRIOR AUTHORISATION.The use of biometric recognition may also be employed without prior approval from an authority ( MEDICAL OR OTHERWISE ), if and only if the state reliably detected by multimodal monitoring falls within the framework of the "OBLIGATION TO ASSIST A PERSON IN DANGER" according to the LAW, biometric recognition can then be used without prior authorization if it provides assistance in the diagnosis. Outside of the two cases mentioned above, "FORCE MAJEURE" AND / OR "OBLIGATION TO ASSIST A PERSON IN DANGER", the "Life Tracking Medicine", (LTM) which gives citizens the ability to create their own data heritage, allows for the CONSIDERATION OF THE PERSON’S PRIOR CONSENT at the time of the device's activation, ONLY IN CASE OF SUSPICION OF THE ARRIVAL / DETECTION OF ANY PATHLOGY, and without prior approval from an authority (MEDICAL OR OTHERWISE), for the use of biometric recognition.This case also includes persons who, being minors or lacking the capacity to make decisions, are represented by a Parent or legal guardian. In this context, the initial consent of the legal guardian will be taken into account, only in case of suspicion of the arrival / detection of any pathology.

An intelligent dosing platform system for prior authorization and pharmacy benefit management comprising intelligent injection devices with prior authorization modules configured to receive medication release approval from pharmacies, distributing agents, and payor entities. The system analyzes authorization requirements by evaluating individual payor policies, pharmacy benefit manager formulary restrictions, step therapy requirements, medical necessity criteria, seasonality factors, and timing considerations. An automated decision engine determines authorization approval or denial based on real-time policy evaluation and aggregated device usage data from multiple injection devices. An action coordinator triggers downstream processes including preventing device shipment, updating electronic health records, and coordinating billing system updates. A financial reconciliation module ensures one-to-one correspondence between authorization approvals and fulfillment activities, matching approved medications with actually shipped or administered medications while preventing payment for unapproved medications.

System and methods for providing interoperable, automated, real-time genetic efficacy contraindication checks are provided. Coverage requests involving for medications for patients from nodes of an electronic network. A testing results query is made for each request at database(s) with identifying information for the patients to determine if genetic efficacy testing results are available. If so, a testing results query is performed to determine if any of the medications in a respective coverage request are contraindicated for a respective patient based on the genetic efficacy testing results. And, if so, a standardized, encoded message is returned to the originating node for the respective coverage request query indicating each contraindicated medication.

A method is provided for dynamic authorization based on execution path status. The method includes identifying a plurality of execution paths in prior authorization data, in which each execution path contains a plurality of nodes. The identified plurality of execution paths are ranked according to probability. At least part of a most probable execution path from among the ranked plurality of execution paths is determined for a current user based on a current node. Node access authorizations are automatically generated based on the at least part of the determined most probable execution path.

Described herein are pharmacy claim transaction processing systems and methods. A method for pre-approval of a prior authorization includes receiving a pharmacy claim transaction message associated with a pharmacy claim transaction for a prescription that satisfies a prior authorization (PA) trigger condition, the message including a plan identifier and a prescriber identifier. The method includes querying a database with the prescriber identifier and plan identifier to access prescriber reference data, healthcare plan provisions, and jurisdictional regulations, as well as applying pre-approval rules to the prescriber reference data, healthcare plan provisions, and jurisdictional regulations, the pre-approval rules defining pre-approval conditions for pharmacy PAs. The method includes, when the pre-approval conditions are met, automatically appending an approval data element to the message to generate a PA approval message and transmitting the PA approval message. The method further includes writing a record to the database including whether the transaction satisfied the pre-approval conditions.

A method includes processing, by one or more processors, historical claim and claim remittance information to extract prior authorization data; performing, by the one or more processors, a probabilistic analysis on the prior authorization data for a procedure to generate a first metric and a second metric, the first metric comprising a percentage of claims including the procedure in which prior authorization is applied for and the second metric comprising a percentage of claims including the procedure in which prior authorization was not applied for and were denied payment for not applying for prior authorization; determining, by the one or more processors, whether the first metric satisfies a first authorization threshold and the second metric satisfies a second authorization threshold, the first and second thresholds being associated with a prior authorization rule corresponding to the procedure; and generating, by the one or more processors, a recommendation on whether to obtain prior authorization before performing the procedure based on whether the first metric satisfies the first authorization threshold and the second metric satisfies the second authorization threshold.

In some implementations, the device may include receiving a prompt via a graphical user interface of a computing device, where the prompt identifies a target institution of a plurality of institutions, a patient condition, and a treatment. In addition, the device may include providing the prompt as input to ac generative language model, where the generative language model may include a pre-trained machine learning model that was initially trained on a general domain and subsequently trained on a target domain. The device may include receiving a generated pre-authorization letter as output from the generative language model, where the generated pre-authorization letter includes one or more fields identifying information requested from a user of the computing device. Moreover, the device may include presenting the generated pre-authorization letter to the user via the graphical user interface of the computing device.

Systems and methods for providing an intelligent network to onboard patients to specialty medications are described herein. A server system for managing communication is configured to receive from a user at a healthcare practice, an electronic request to obtain a medical prior authorization (medPA) for a specialty drug to administer for a patient of the healthcare practice; select, using a rules service, a communication pathway to submit the medPA to a healthcare payer from a plurality of communication pathways; and initiate a connection with the healthcare payer using the selected communication pathway.

A trained predictive server is provided for determining that a diagnosis and treatment plan is inaccurate. The trained predictive server includes a processor configured to receive a set of prior authorization (PA) data associated with a medical claim for a patient, and determine that the PA data indicates that the medical claim is associated with a qualifying critical illness and control use of medical devices. The processor can extract component data from the set of PA data, and apply the extracted component data to a predictive model associated with the qualifying critical illness to determine whether the medical claim is associated with an inaccurate diagnosis and treatment plan. Upon determining that the medical claim is associated with an inaccurate diagnosis and treatment plan, the processor can generate a request for review of the diagnosis and treatment plan using the set of PA data and stop use of a medical device.