SBIR-STTR Award

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to create transparency around the business logic used by stakeholders in the healthcare system to make transactional decisions as well as create alignment around the current status of healthcare transactions that are part of the work in process. To most people who interact with the healthcare system, it functions more or less as a "black box" with decisions that sometimes defy logic and common sense. Our goal is to use Blockchain and Machine Learning technology to convert this "black box" into a "glass box." The commercial impact of this project could be an up to 25% reduction in claims processing costs for healthcare providers by the elimination of redundant work, re-work and errors. This STTR Phase 1 project proposes two innovations: 1) claims transaction and reason codes managed through a Blockchain so that Providers and Payers can confidently know the accurate status of a claim, and 2) sophisticated statistical and deep learning algorithms for predicting the likelihood of a claim denial with natural language processing of associated notes and appeals. Our product is a mathematically driven software service that utilizes and innovates with multiple technologies: Blockchain distributed ledgers, smart contracts and tokens, and prediction, recommendation, forecasting, non-linear optimization and natural language processing engines within a privacy-preserving data management system. 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.

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.