Abstract: A medical information navigation engine (“MINE”) is provided. In some embodiments, the system computes a current patient encounter vector for a current patient encounter, and then an optimal patient encounter vector is computed by assuming a best case patient encounter in accordance with the organizational objectives. The system is then able to compute the difference between the best case encounter and the current patient encounter. This difference is used to compute a corresponding payoff using an intelligent matrix.

Abstract: A medical information navigation engine (“MINE”) is provided. In some embodiments, the system computes a current patient encounter vector for a current patient encounter, and then an optimal patient encounter vector is computed by assuming a best case patient encounter in accordance with the organizational objectives. The system is then able to compute the difference between the best case encounter and the current patient encounter. This difference is used to compute a corresponding payoff using an intelligent matrix.

Abstract: A medical information navigation engine (“MINE”) is provided. In some embodiments, the system computes a current patient encounter vector for a current patient encounter, and then an optimal patient encounter vector is computed by assuming a best case patient encounter in accordance with the organizational objectives. The system is then able to compute the difference between the best case encounter and the current patient encounter. This difference is used to compute a corresponding payoff using an intelligent matrix.

Abstract: A medical information navigation engine (“MINE”) is provided. In some embodiments, the system computes a current patient encounter vector for a current patient encounter, and then an optimal patient encounter vector is computed by assuming a best case patient encounter in accordance with the organizational objectives. The system is then able to compute the difference between the best case encounter and the current patient encounter. This difference is used to compute a corresponding payoff using an intelligent matrix.

Abstract: A computerized Medical Information Navigation Engine (“MINE”) extracts clinical knowledge, by identifying coded elements with reimbursement potential contributing to payoff based on clinical history, and subtracting coded elements documented in an encounter from the coded elements, based on business logic. The MINE sorts the remaining coded elements in accordance with one optimization criteria to payoff based on clinical history.

Abstract: A computerized Medical Information Navigation Engine (“MINE”) extracts clinical knowledge, by identifying coded elements with reimbursement potential contributing to payoff based on clinical history, and subtracting coded elements documented in an encounter from the coded elements, based on business logic. The MINE sorts the remaining coded elements in accordance with one optimization criteria to payoff based on clinical history.

Abstract: A medical information navigation engine (MINE) for transacting medical information by receiving medical information from a medical sources, identifying, mapping, and consolidating the received medical information by a back-end medical processor, presenting access to specific relevant data, based on a user's security privileges, within the identified, mapped, and consolidated medical information, based on user-specific functions or roles by a front-end medical processor, and generating user-customized processed medical information to a plurality of users, with at least a portion of the user-customize processed medical information being provided to each of the plurality of users based on its relevancy to each user's specific function or role and each user's associated security privileges.

Abstract: A medical processor computes concept associations by mining aggregated data from patient documents thereby reducing risk of PHI exposure. The processor identifies clinically relevant terms in patient documents, compute associations between pairs of clinically relevant terms using co-occurrences, and filter out random associations. A knowledge provider receives user query concepts, retrieves patient concepts, and extracts relevant apixions from an association matrix. The knowledge provider intersects relevant apixions with patient concepts, ranks and provides the results to the user.

Abstract: A medical information navigation engine (MINE) for transacting medical information by receiving medical information from a medical sources, identifying, mapping, and consolidating the received medical information by a back-end medical processor, presenting access to specific relevant data, based on a user's security privileges, within the identified, mapped, and consolidated medical information, based on user-specific functions or roles by a front-end medical processor, and generating user-customized processed medical information to a plurality of users, with at least a portion of the user-customize processed medical information being provided to each of the plurality of users based on its relevancy to each user's specific function or role and each user's associated security privileges.

Abstract: A method is disclosed to receive health information request (HIR), including health information request query (HIRQ) and health information request data (HIRD), and to metatag the received HIR. The metatagged HIR is reconciled based on a semantic concept and HIRS is returned.

Abstract: A computerized Medical Information Navigation Engine (“MINE”) extracts clinical knowledge, by identifying coded elements with reimbursement potential contributing to payoff based on clinical history, and subtracting coded elements documented in an encounter from the coded elements, based on business logic. The MINE sorts the remaining coded elements in accordance with one optimization criteria to payoff based on clinical history.

Abstract: A medical information navigation engine is useful in association with at least one electronic health record system. The engine decouples identifying information from clinical data from electronic health records. The clinical data includes clinical narrative having discrete data and textual data. The identifying information is stored. Additionally, the identifying information is associated with a token in the clinical data. The clinical data may then be indexed. The discrete data and the textual data in the clinical data may then be mined. Mining includes extracting at least one relevant event from the discrete data and the textual data. Next, the clinical data and identifying information may be reintegrated using the token. The event associated with the mined discrete data and textual data may then be exported. The system may also provide a validation tool for users, including clinicians, to search and view clinical data. The exported event may be used to alter treatment of a patient.

Abstract: A method is disclosed to receive health information request (HIR), including health information request query (HIRQ) and health information request data (HIRD), and to metatag the received HIR. The metatagged HIR is reconciled based on a semantic concept and HIRS is returned.

Abstract: A medical information navigation engine (MINE) for transacting medical information by receiving medical information from a medical sources, identifying, mapping, and consolidating the received medical information by a back-end medical processor, presenting access to specific relevant data, based on a user's security privileges, within the identified, mapped, and consolidated medical information, based on user-specific functions or roles by a front-end medical processor, and generating user-customized processed medical information to a plurality of users, with at least a portion of the user-customize processed medical information being provided to each of the plurality of users based on its relevancy to each user's specific function or role and each user's associated security privileges.

Abstract: A medical information navigation engine (“MINE”) is provided. In some embodiments, the system computes a current patient encounter vector for a current patient encounter, and then an optimal patient encounter vector is computed by assuming a best case patient encounter in accordance with the organizational objectives. The system is then able to compute the difference between the best case encounter and the current patient encounter. This difference is used to compute a corresponding payoff using an intelligent matrix.

Abstract: A medical processor computes concept associations by mining aggregated data from patient documents thereby reducing risk of PHI exposure. The processor identifies clinically relevant terms in patient documents, compute associations between pairs of clinically relevant terms using co-occurrences, and filter out random associations. A knowledge provider receives user query concepts, retrieves patient concepts, and extracts relevant apixions from an association matrix. The knowledge provider intersects relevant apixions with patient concepts, ranks and provides the results to the user.

Abstract: A computerized Medical Information Navigation Engine (“MINE”) extracts clinical knowledge, by identifying coded elements with reimbursement potential contributing to payoff based on clinical history, and subtracting coded elements documented in an encounter from the coded elements, based on business logic. The MINE sorts the remaining coded elements in accordance with one optimization criteria to payoff based on clinical history.

Abstract: A medical information navigation engine is useful in association with at least one electronic health record system. The engine decouples identifying information from clinical data from electronic health records. The clinical data includes clinical narrative having discrete data and textual data. The identifying information is stored. Additionally, the identifying information is associated with a token in the clinical data. The clinical data may then be indexed. The discrete data and the textual data in the clinical data may then be mined. Mining includes extracting at least one relevant event from the discrete data and the textual data. Next, the clinical data and identifying information may be reintegrated using the token. The event associated with the mined discrete data and textual data may then be exported. The system may also provide a validation tool for users, including clinicians, to search and view clinical data. The exported event may be used to alter treatment of a patient.

Abstract: A method is disclosed to receive health information request (HIR), including health information request query (HIRQ) and health information request data (HIRD), and to metatag the received HIR. The metatagged HIR is reconciled based on a semantic concept and HIRS is returned.