RulesLab

www.ruleslab.com

RulesLab is a niche solution for the medical, private health insurance and social insurance market in the Australian market. RulesLab believe that there is a common set of problems for all these disparate industries, which can be solved with the pragmatic application of open source, user maintained, real-time rules technology. Reduce or remove manual application of business rules from your large data sets such as claims or medical procedure notes and enable your business to go faster, more consistently and more accurately. Some of the issues RulesLab solves are: -Reducing or removing manual application of business rules to large data sets such as claims or medical procedure notes. -Centralising and managing domain specific knowledge. Taking the knowledge out of individual silos and placing it in a controlled and managed structure. -Standardising unstructured or differently structured messages to the accepted medical industry standards. -Application of natural language processing and named entity recognition to capture freeform data (such as clinical notes) to a structured machine readable format. -Applying business rules in real time at point of receipt of data to allow rapid response and rapid decisions based on the data received.

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RulesLab is a niche solution for the medical, private health insurance and social insurance market in the Australian market. RulesLab believe that there is a common set of problems for all these disparate industries, which can be solved with the pragmatic application of open source, user maintained, real-time rules technology. Reduce or remove manual application of business rules from your large data sets such as claims or medical procedure notes and enable your business to go faster, more consistently and more accurately. Some of the issues RulesLab solves are: -Reducing or removing manual application of business rules to large data sets such as claims or medical procedure notes. -Centralising and managing domain specific knowledge. Taking the knowledge out of individual silos and placing it in a controlled and managed structure. -Standardising unstructured or differently structured messages to the accepted medical industry standards. -Application of natural language processing and named entity recognition to capture freeform data (such as clinical notes) to a structured machine readable format. -Applying business rules in real time at point of receipt of data to allow rapid response and rapid decisions based on the data received.

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City (Headquarters)

Melbourne

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Employees

11-50

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Potential Decision Makers

  • Director Europe and Africa Operations

    Email ****** @****.com
    Phone (***) ****-****
  • Director of Operations Australia

    Email ****** @****.com
    Phone (***) ****-****
  • Marketing Manager

    Email ****** @****.com
    Phone (***) ****-****
  • Strategic Technology Consultant

    Email ****** @****.com
    Phone (***) ****-****

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