Gabriele Picco, Lam Thanh Hoang, et al.
EMNLP 2021
In challenging economic times, obtaining value for money by ensuring financial integrity and fairer distribution of services are among the top priorities for social and health-care systems globally. However, healthcare billing policies are complex and identifying non-compliance is often narrow-scope, manual and expensive. Maintaining ‘integrity’ is a challenge - ensuring that scarce resources get to those in need and are not lost to fraud and waste. Our approach fuses recent advances in dependency parsing with a policy ontology to convert the content of regulatory healthcare policy into human-friendly policy rules, that are amenable to machine-execution, with human oversight. We describe the ontology-guided transformation of textual patterns into a semantically-meaningful knowledge graph of rules, outline our experiments and evaluate results against policy rules obtained from professional investigators. The aim is to make a policy-compliance ‘landscape’ visible to healthcare programs - helping them identify Fraud, Waste or Abuse.
Gabriele Picco, Lam Thanh Hoang, et al.
EMNLP 2021
Sara Rosenthal, Pepa Atanasova, et al.
ACL-IJCNLP 2021
Diego Garcia-Olano, Yasumasa Onoe, et al.
ACL-IJCNLP 2021
Vanessa Lopez, Martin Stephenson, et al.
HT 2014