Safely Streamlining Healthcare Policy Management using Ideas from Structured Natural Language Processing (SNLP)
Asif Khalak & Sergio Martinez-Ortuno | Collective Health
ABOUT THE TALK
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.
Director of Data Science | Collective Health
Asif is Director of Data Science at Collective Health, a company focused on building a workforce management platform for the $1.2 trillion employer healthcare economy. He has worked in inference and control of complex systems in a number of domains including aero-defense, DNA sequencing, and digital health, and has published on these topics in artificial intelligence, bioinformatics, and controls conferences. He holds a PhD in Mechanical Engineering from MIT and a BS in Engineering and Applied Science from Caltech.
sergio martinez ortuno
Staff Data Scientist | Collective Health
Sergio is a Staff Data Scientist at Collective Health, where his areas of focus are medical claims adjudication, insurance plan analysis, and automated decision-making. Sergio has a Master’s Degree in Management Science & Engineering from Stanford, and his tech industry experience spans fields such as machine learning, natural language processing, digital logic design, reliability engineering, and the design of safety-critical control systems.