Express Scripts Strategic Development, Inc.

Express Scripts, Inc.

David Alfredo Tomala

Robert F Nease

Seda Aydin Follis

Pritesh J Shah

Christopher G Lehmuth

Christopher R Markson

Amit K Bothra

Logan R Meltabarger

Dhanur S Balagere

Reethi N Iyengar

Bose S Daggubati

Tina L Bauman

Tina L Bauman Butcher

Suman Katragadda

A content analysis system includes a processor executing instructions from memory. The instructions include, in response to receiving a request signal from a user device, obtaining feedback items, each having a source indicator; identifying unique source indicators; and, for each source indicator, aggregating corresponding ones of the feedback items. A set of filtered feedback items is generated according to either first or second access levels associated with a user of the user device. A subset of filtered feedback items is selected according to a date range specified by the request signal, a set of automated rules is applied, and natural language processing is performed based on frequency of presence of salient terms to identify themes. A control signal is transmitted to a user interface of the user device instructing display of a representation that indicates a change in the frequency of the identified themes over the specified date range.

Medical processing systems and methods

A method includes obtaining source data containing feedback information, identifying different phrases of interest in the feedback information, generating count data that indicates how often the different phrases of interest appear in the feedback information, determining counts of how often the phrases of interest appear in the feedback information, and modifying a count that is representative of how often at least one phrase of interest appears in the feedback data. The count is modified by reducing the count by a count of how often another, shorter phrase of interest also appears in the feedback data. The method also includes generating at least one interface respectively reflecting the count data for the different phrases of interest and the count that has been modified.

Methods and systems for predicting adherence to Multiple Sclerosis treatment are described. In one embodiment, a member undergoing a Multiple Sclerosis treatment is identified. Member data associated with the member undergoing the Multiple Sclerosis treatment is accessed. Pre-prediction time period adherence data associated with the member, member prescription data associated with the member, member family data associated with the member, and member demographic data associated with the member are determined based on the member data associated with the member. A likelihood that the member will be adherent to the Multiple Sclerosis treatment over a prediction time period is determined based on the pre-prediction time period adherence data, member prescription data, member family data, and member demographic data. Other methods and systems are described.

Methods and systems for predicting adherence to Multiple Sclerosis treatment

Methods and systems for pharmacy modeling are described. The risk adjusted pharmacy predictive model is created from member data, claims data, and population data. This model can be used to compare the actual pharmacy performance to an expected actual pharmacy performance value, which can be used to identify pharmacies at risk or not performing to an acceptable level. The model can be used for adherence and generic drug utilization ratings of pharmacies. The pharmacy can be judged on a therapy class by therapy class basis with factors that reflect the demographic, socio-economic, location, benefits attributes, etc. that actually affect the performance of the pharmacy and may assist in determining the quality of care by a pharmacy.

Methods and systems for pharmacy modeling

A system and method for improving adherence to therapy programs, in particular to adherence to medication therapy and/or to healthcare therapy, that includes medication therapy. Methods of the present invention may also be used to improve compliance with and/or adherence to other wellness and/or health care programs. Methods and systems of the invention may include methods of identifying patients who are at risk of non-adherence, non-compliance, or likelihood of cessation with a therapy program, predicting a basis for such noncompliance, and targeting interventions directed to patients who have been identified as likely to be non-compliant, wherein interventions take into consideration the predicted basis for noncompliance.

Methods and systems for improving therapy adherence

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Chesterfield, MO, United States of America

David Alfredo Tomala