SBIR Phase II: Predicting Healthcare Fraud, Waste and Abuse by Automatically Discovering Social Networks in Health Insurance Claims Data through Machine Learning

小型企业创新研究(SBIR)第一阶段项目的更广泛影响/商业潜力将带来新的人工智能/机器学习(AI/ML)产品，提供高精度和可解释性。没有预测背后的理由，决策者就不能信任和有效地使用AI/ML解决方案。通过该项目的研发成果将导致更准确和更快的检测，并适当解释异常相互作用，并建议有效的控制措施，以1)消除医疗保险市场中数十亿美元的欺诈、浪费和滥用(FWA)；2)降低成本，提高向消费者提供医疗保健的质量和速度；以及3)促进新兴医疗物联网(IOT)设备和系统的个性化健康和智能健康领域的新市场，促进经济增长。这项研究的结果有望使医学异常的发现成为可能，并推动新型FWA的检测。带解释的检测准确率的提高将节省数亿美元。社会影响包括通过更好的FWA控制降低消费者和纳税人的成本，以及通过早期医疗物联网异常检测提高健康结果。更广泛地说，该系统预计将检测可能的阿片类药物或药物滥用流行队列、用药不足/过量、社区健康异常的高级警报。拟议的项目将扩展和推广一种新的机器学习方法，以解决医疗保险中的欺诈、浪费和滥用(FWA)问题，并提供合理的幕后预测的解释能力，并在分布式并行计算框架中进行操作。技术问题是如何将实体(例如，医生)与它们的属性(例如，医生的处方历史)之间的关系结合起来。这个项目通过将训练数据(例如医生)中的行之间的关系与标准机器学习相结合来提高预测精度，同时促进本地解释，从而推动了最新技术的发展。其结果是极大地提高了预测精度和可解释性。因此，该方法使用网络信息来单独填补实体信息的空白，反之亦然，同时便于对测试用例的解释。这种方法有望显著提高检测FWA的能力，为节省数十亿美元铺平道路，提前发现基于物联网的医疗异常以改善健康结果，并通过提供的解释为决策者建立对预测的信任。该团队不仅打算提供具有可解释性的准确性提升，而且还打算提供一个完全可操作的系统，该系统具有自动化数据管道、并行和分布式算法处理框架，可以在SaaS基础上或企业解决方案上部署。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will usher new Artificial Intelligence/Machine Learning (AI/ML) products delivering high accuracy with explainability. Without rationale behind predictions, decision makers can't trust and effectively use AI/ML solutions. Outcome of R&D through this project would lead to more accurate and faster detection with appropriate explanation of anomalous interactions and recommend effective controls to 1) eliminate billions of dollars of fraud, waste and abuse (FWA) in Health Insurance markets; 2) lower costs, improve quality and speed of Health Care delivery to consumers; and 3) promote new markets in Personalized Health and Smart Health sector for emerging Medical Internet-of-Things (IOT) devices and systems, enabling economic growth. The results of this research are expected to enable the discovery of medical anomaly together with advancing the detection of new types of FWA. The boost in detection accuracy with explanation will save hundreds of millions of dollars. Societal impact includes reduced costs to consumers and taxpayers through better FWA control and advance health outcome through early medical IOT anomaly detection. More broadly, the system is expected to detect possible opioid or substance abuse epidemic cohorts, under/over-medication, advanced alerts for community health anomalies.The proposed project will extend and generalize a novel machine learning method to solve the Fraud, Waste, and Abuse (FWA) problem in health insurance, coupled with explanatory capability providing rational behind predictions and operationalized in a distributed parallel computing framework for scaling. The technical problem is how to combine relations between entities (e.g., doctors) with their attribute (e.g., a doctor's prescription history). This project advances the state of the art by combining relations between rows in the training data (e.g. doctors) with standard machine learning to improve prediction accuracy while facilitating local explanation. The result is vastly improved prediction accuracy with explainability. Thus, the method uses network information to fill in the gaps of entity information alone and vice versa while facilitating explanation for a test case. This method is expected to significantly improve the ability to detect FWA and pave ways for multi Billion dollars savings, call out IOT-based medical anomaly in advance to improve health outcome and build trust in the predictions for the decision makers through the explanations provided. The team intends to deliver not only the accuracy boost with explainability, but a fully operational system with automated data pipeline, parallel and distributed algorithmic processing framework which can be deployed on a SaaS basis or an enterprise solution.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.