SYSTEM AND METHOD FOR PROFESSIONAL CONTINUING EDUCATION DERIVED BUSINESS INTELLIGENCE ANALYTICS

• US 20130198120A1
• Filed: 01/25/2013
• Published: 08/01/2013
• Est. Priority Date: 01/27/2012
• Status: Abandoned Application

• Current Assignee: MedAnalytics, Inc.
• Original Assignee: MedAnalytics, Inc.
• Inventors: Vezina, Daniel P.
• Application Number:
  US13/750,231
• Publication Number:
  US 20130198120A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 50/20 Education
• CPC Class Codes: G06Q 50/2057 Career enhancement or continuing education service

Abstract

The present disclosure relates to non-linear analytics engine derived business intelligence. More particularly, the present disclosure describes methods and systems that use content associated with a medical professional continuing education event as a data source for a non-linear analytics engine. The content, which relates to the content creation, content delivery, follow-ups, evaluations, attendee interactions, and administrative tasks associated with medical professional continuing education event, is extracted from a learning management system and subsequently transmitted to the non-linear analytics engine to create business intelligence.

Claims

1. A system for analyzing unstructured data sources generated by a professional continuing education event, the system comprising:

at least one processor;

a memory in operable communication with the at least one processor; and

an analytics system comprising;

a learning management system to;

retrieve unstructured data from one or more professional continuing education events;

a business intelligence application to;

parse the unstructured data;

assign a relevancy value to the unstructured data based on qualitative and quantitative measurements; and

provide the unstructured data based on the relevancy value to a relevant cluster storage location; and

a data warehouse to;

analyze a first set of relevant connections between a sub-set of the unstructured data located within a cluster location; and

analyze a second set of relevant connections between the sub-set of unstructured data located in different cluster location.

2. A method for creating predictive business intelligence from unstructured data comprising:

retrieving, at at least one processor, unstructured data from one or more professional continuing education events;

parsing, at the processor, the unstructured data;

assigning, at the processor, a relevancy value to the unstructured data based on qualitative and quantitative measures;

providing, at the processor, the unstructured data based the relevancy value to a relevant cluster storage location;

analyzing, at the processor, a first set of relevant connections between a sub-set of the unstructured data located within a cluster location; and

analyzing, at the processor, a second set of relevant connections between the sub-set of unstructured data located in different cluster location.