The broader impact of this I-Corps project is in reducing transaction costs involved when negotiating reimbursements and claims coverage with health insurers. Health insurers play a role in the majority of healthcare transactions, but administrative regimes vary widely between insurance providers and across state lines. This complexity results in less than optimal levels of coverage for patients and creates a financial burden for consumers and providers alike. From a commercial standpoint, this technology can increase the reimbursements received by providers and reduce administrative overhead costs. Ultimately, this means providers will generate increased revenues and consumers will experience relief from healthcare related financial liabilities.
This I-Corps project further develops a platform that synthesizes natural language understanding, argumentation, and machine learning functionalities. Health care policy documents in natural language are to be translated into schematic, logical representations that are amenable to artificial intelligence (AI) automated inferencing. The platform will be informed by composite patterns, some found through supervised machine learning, with data definitions involving the degree of success and failure of patient/provider claims as manifest in insurer decisions, insurer responses to appeals, patient/provider characteristics, and schematized contracts that define policy; thus a datum in the machine learning context will be extraordinarily rich compared to other machine learning applications. Patient characteristics and contracts are contextualizing information and provide theoretical constraints with this machine learning methodology a novel instance of the relatively new field of theory-guided machine learning. Collectively, the platform will synthesize a number of AI functionalities.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
