Experian Health, Inc.

Mckesson Financial Holdings

Sean Daniel Reisz

John Taylor Dennis

Dimuthu Wijetilleke

Matthew McCawley

Karly Marie Rowe

Christopher Wavil Moody

Elizabeth S Kaye

Jessica Landisman Williams

Jeffrey Scott Sauers

Charles David Smith

David Liu

Thomas A Picard

Kenneth Basheer Halaby

Reed Liggin

Generating and providing match justifications in association with record matching results for improved record matching is provided. An entity resolution system generates a set of deterministic rules based on patterns of attribute comparison outcomes of known matched and unmatched records. Each rule includes matching conditions and an action instruction including a message of a justification for a match/non-match. The system receives a request to determine a match between two records. A matching engine compares various attributes of the records using probabilistic matching technologies to determine a match outcome. A justification engine compares attribute comparison outcomes to the stored rules. When the matching conditions of a rule are satisfied, the rule is activated and a human-readable justification is linked to the match output and provided in a match response. The justification provides insights into the match outcome, and improves the speed and accuracy of a verification process of the match output.

Methods and systems for improved entity recognition and insights

Entity matching is provided. A request to determine a second entity matching with a first entity may be received. Demographic fields to be compared to determine the match may be determined. Comparison weights including an agreement weight, a disagreement weight, and a null weight may be assigned to each of the plurality of demographic fields. The received request may be parsed to determine demographic fields data related to the second entity. The demographic fields data may be compared with indexed demographic data which may include a plurality of records. The demographic fields data may be compared for the determined plurality of demographic fields. A comparison weight for each of the plurality of demographic fields may be determined based on the comparison. The first entity matching with the second entity may be determined from the indexed demographic data based on determined comparison weights for the plurality of demographic fields.

Methods and system for entity matching

A request for a most reliable demographic fields data for a selected demographic field associated with an entity may be received at an entity matching system. A plurality of records associated with the entity may be received at the entity matching system. Each of the plurality of records may include at least one demographic field. A demographic field corresponding to the selected demographic field may be identified from the at least one demographic field for each of the plurality of records. A reliability score may be determined for the corresponding demographic field for each of the plurality of records. The corresponding demographic field with the highest reliability score may be determined as the most reliable demographic fields data.

Methods and system for determining a most reliable record

Expanded data processing for entity matching is provided. A request for determining a matching entity is received. The received request is parsed to determine demographic field data associated with the request. A first look up operation is performed in an indexed demographic data for a first demographic field associated with the request. A first list of entities matching with the first demographic field data is received in response to the first look up operation. A second look up operation is performed in the indexed demographic data to determine a set of demographic attributes associated with each entity on the first list. The demographic attributes correspond to a second demographic field associated with the request. A matching confidence for whether the entity is associated with a given entity in the first list is determined based on a comparison between the demographic field data and the demographic attributes.

Expanded data processing for entity matching

Improvements in data processing to match entities allow for more accurate records to be kept, with less memory storage used and fewer processing resources to be expended when accessing records. When receiving a request for an identifier for an entity, the request is parsed to identify various demographic fields within the request. A probabilistic search is performed to compare the entity to the candidate records that are augmented with expanded demographic data, which improve the reliability in matching the requested entity to its records. As updates are made to the external resources, the requests are rerun to update the internal resource and to eliminate any new identifiers created due to non-updated data, thus reducing data storage overhead.

Expanded data processing for improved entity matching

Systems and methods may be provided for obtaining and providing patient medication history. The systems and methods may include receiving a request for medication history information of a patient, the request including identification information for the patient. At least one memory may be accessed utilizing at least a portion of the identification information, the at least one memory including information associated with pharmacy claims transactions stored in the at least one memory in near real time as the pharmacy claims transactions are processed. Information associated with the medication history of the patient is obtained from the at least one memory and communicated in response to the received request.

Systems and methods for providing patient medication history

Systems and methods may determine prescribing physician activity levels. Information associated with a plurality of healthcare transaction requests that are received during a designated time period from at least one healthcare provider computer for communication to one or more claims processor computers may be collected. A respective prescribing physician for each of the plurality of received healthcare transaction requests may be identified. For each identified physician, a respective activity measure for the designated time period may be calculated based upon a respective number of the healthcare transaction requests identifying the physician.

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Tucker, GA, United States of America

Sean Daniel Reisz