Client was looking to implement a rules engine to process custom health quality rules on distributed computing platform. These rules are used by health plans and providers to identify possible chronic conditions or gaps in care which might affect a consumer’s health and well-being. The objective was to provide a capability for analysts to define a set of custom rules which could be parsed into a common rules framework and executed against a target data set.
Enable Data managed a team of software engineers with deep expertise in rules engine frameworks, Python and Hadoop ecosystem tools. The rules framework was developed using Python which could parse a configuration file and generate a list of input rules. Rules can be sequential, dependent or excluded based on the results as consumer records are processed. The results of the custom rules engine are delivered in a standard output file that can imported into other client tools for analysis or case management. Our team worked with the client to understand functional and non-functional requirements, developed the rules framework, functions and distributed processing code to enable this custom rule capability.
Our application engineers successfully deployed the core rules framework and distributed process engine as part of the initial release. Work continues to develop additional functions and build cross data analytics for defined quality and risk requirements.