Health and social services are complex domains that have a direct impact on people's lives and where vast amounts of money are spent globally.
When funding intended for public health programs is lost to Fraud Waste and Abuse, vulnerable citizens are ultimately the victims. In challenging times, ensuring financial integrity and fairer distribution of services by reducing disparities are among the top priorities for healthcare systems.
In this talk, I will provide a perspective from my journey in adopting research in Knowledge Graphs and AI to address significant societal problems in the healthcare industry.
In particular, Knowledge Graphs emerged as a unifying technology that facilitates bringing diverse data sources together to unlock new knowledge and empower professionals to reduce healthcare disparities.
Through a combination of natural language understanding, deep learning, and knowledge representation for modelling human-expertise and reasoning, we are investigating unique functionalities to automatically extract actionable knowledge from large text policy documents.
We identify medical claims that infringe policy, either intentionally (fraudulent) or unintentionally (e.g., providing unnecessary services or inconsistent with accepted medical practices).
We aim to understand the social program levers that drive positive health.