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

SBIR 第二阶段：通过区块链和机器学习减少医疗保健领域的索赔拒绝

• 批准号: 2126982
• 负责人: Sarah Brown
• 金额: $ 100万
• 依托单位: BRILLIANTMD, LLC
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126982&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Reducing; Claims

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.

这个小型企业创新研究（SBIR）项目的重点是改善医疗保健报销和收入周期管理。两个关键问题是医疗保健索赔中的错误导致经常拒绝付款，并且未能获得事先授权可以使这些服务不足。这些问题可能会导致对患者和/或医疗保健提供商的宣传和/或成本转移的签约以弥补损失的不适当收费。此外，付款规则和事先授权的复杂性需要大量的行政间接费用，以提交索赔，和解和返工，从而使行政费用夸大。拟议的项目解决了医疗保健报销系统中的这些效率低下。该SBIR II期项目建议使用高级分析，机器学习和区块链技术来解决以下研究目标：（1）分析和预测医疗保健索赔的拒绝付款风险； （2）预测在进行临床干预之前先前授权要求的可能性； （3）激励索赔提交过程中的准确性，并减少相关的行政负担和成本。该研究将进行高级索赔解析，数据提取，建模和机器学习，以定义特定的风险模式，并构建可重复，高效和准确的预测算法。这些方法将应用于要求否认和临床数据预测先前授权的临床数据。区块链技术将用于激励人口统计和临床数据收集，并要求处理工作流程，以提高数据准确性，收集效率和预测质量。技术结果将是与生产力引擎集成的准确，预测性，不断学习，高效的机器学习工具集。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，认为值得通过评估来获得支持。