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Benefit Graph Extraction from Healthcare Policies

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 11779)


With healthcare fraud accounting for financial losses of billions of dollars each year in the United States, the task of investigating regulation adherence is key to reduce the impact of Fraud, Waste and Abuse (FWA) on the healthcare industry. Providers rendering services to patients typically submit claims to healthcare insurance agencies. Such claims must follow specific compliance criteria specified by state and federal policies. This paper presents an ontology-based system that aims to support the FWA claim investigation process by extracting graph-based actionable knowledge from policy text describing those compliance criteria. We discuss the process of creating a domain-specific ontology to model human experts’ conceptualisations and to incorporate early-on the feedback of FWA investigators, who are the early adopters of our solution. We explore whether the ontology is expressive and flexible enough to model the diverse compliance processes and complex relationships defined in policy documents. The ontology is then used, in combination with natural language understanding and semantic techniques, to guide the extraction of a Knowledge Graph (KG) from policies. Our solution is validated in terms of correctness and completeness by comparing the extracted knowledge to a ground truth created by investigators. Lastly, we discuss further challenges our deployed semantic system needs to tackle in this novel scenario, with the prospect of supporting the investigation process.

V. Lopez, V. Rho, T. S. Brisimi and F. Cucci—Equal research contribution. We would like to acknowledge Conor Cullen, Carlos Alzate, Spyros Kotoulas, Martin Stephenson, Pierpaolo Tommasi, Marco Sbodio, Denisa Moga and our OM: Tim Cooper, Mark Gillespie and Mark Goodhart for their support and insights.

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  • DOI: 10.1007/978-3-030-30796-7_29
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    The shallow semantic parsing of the sentence is performed through a natural language understanding capability of SystemT, currently under development, that computes and exposes information regarding the semantic roles present in the sentence, e.g. actions, agents, themes and contextual information of those actions, together with information regarding voice, polarity, etc.


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Lopez, V. et al. (2019). Benefit Graph Extraction from Healthcare Policies. In: , et al. The Semantic Web – ISWC 2019. ISWC 2019. Lecture Notes in Computer Science(), vol 11779. Springer, Cham.

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  • Print ISBN: 978-3-030-30795-0

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