SBIR Phase II: Reducing Claims Denials in Healthcare Through Blockchain and Machine Learning

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This Small Business Innovation Research (SBIR) project is focused on improving healthcare reimbursement and revenue cycle management. Two key concerns are that errors in healthcare claims result in frequent payment denials, and failure to obtain prior authorization can make those services non-reimbursable. These issues can lead to inappropriate direct billing to patients and/or a write-off by the healthcare provider and cost-shifting to cover losses. In addition, complexity within the rules for payment and prior authorization require considerable administrative overhead for claim submission, reconciliation, and rework, inflating administrative costs. The proposed project addresses these inefficiencies in the health care reimbursement system. This SBIR Phase II project proposes to use advanced analytics, machine learning, and blockchain technologies to address the following research objectives: (1) analyze and predict healthcare claim risk for denial of payment; (2) predict the likelihood of a prior authorization requirement before a clinical intervention is undertaken; and (3) incentivize accuracy in the claim submission process and decrease associated administrative burden and cost. The research will conduct advanced claims parsing, data extraction, modeling, and machine learning to define specific patterns of risk, and build reproducible, efficient, and accurate predictive algorithms. These approaches will be applied to both claim denials and to clinical data predictive of prior authorization. Blockchain technology will be utilized to incentivize demographic and clinical data collection and claims processing workflows for improvements in data accuracy, efficiency of collection, and predictive quality. The technical result will be an accurate, predictive, continually learning, highly efficient machine learning toolset integrated with a productivity engine.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.