The administration of medical health plans requires policy definitions that are highly complex with legal, ethical, clinical, and financial considerations. Managing and updating these policies therefore requires significant subject matter expertise, and balancing these considerations makes it difficult to make updates that satisfy all of the constraints.
This talk focuses on bringing concepts from computing and language processing such as the use of custom lexers/parsers and git-integration to streamline policy management. The policy representation and translation problem is handled using a structured natural language programming (SNLP) approach which translates from a policy language usable by a healthcare administrator into a semantic serialized object. This makes it possible to build a configuration management framework for policy management that is equivalent to “safe” policy management in mission-critical regulated industries such as developing software requirements for nuclear power systems.
Palo Alto-based Data Scientist with a MS in Management Science & Engineering from Stanford, BS in Computer Engineering, and 6 years of engineering and management experience.
Successfully booted AI / data science teams from scratch at two places in different verticals (tech, healthcare). Started as first member and grew successful, collaborative innovation teams. Key component in production releases while also publishing IP/patents.