The Use of Mathematic Algorithms in the Prevention of Improper Medical Payments

数学算法在预防不当医疗支付中的运用

• 批准号: 7316071
• 负责人: Merrit Quarum
• 金额: $ 9.25万
• 依托单位: QMEDTRIX SYSTEMS, INC.
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2008-03-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7316071
• 关键词: Address; Age; Algorithms; Area; Arts; Classification; Code; Collaborations; Computer Simulation; Computer software; Contractor; Contracts; Cost Control; Data; Data Reporting; Data Set; Databases; Detection; Development; Elements; Environment; Evaluation; Failure; Genetic Programming; Goals; Health Care Costs; Health Care Fraud; Health Personnel; Healthcare; Healthcare Market; Healthcare Systems; Hospitals; Industry; Inpatients; Insurance Carriers; Learning; Machine Learning; Manuals; Mathematics; Medical; Medicare; Methods; Mining; Modeling; Nature; Numbers; Operative Surgical Procedures; Outcome; Outpatients; Pattern; Phase; Policies; Population; Prevention; Private Sector; Process; Provider; Rate; Reporting; Research; Running; Small Business Technology Transfer Research; Solutions; Sorting - Cell Movement; Standards of Weights and Measures; System; Technology; Testing; Training; Work; base; college; computerized; cost; design; experience; improved; mathematical algorithm; novel; novel strategies; payment; prevent; programs; size; software development; stem; success

DESCRIPTION (provided by applicant): The goal of this research is to create software that uses mathematical algorithms to detect medical billing coding errors prior to payment. The well-publicized failure of current healthcare cost containment technologies to prevent improper payments in both the commercial healthcare market and the federal Medicare program highlights the urgent need for a new approach to the growing problem of out of control medical costs. A recent federal study by the GAO estimated that improper payments by Medicare alone were in excess of 21 billion dollars, a truly staggering 48.1 percent of all improper payments by federal programs. Like SPAM, whose dynamic nature makes static or post hoc remedies ineffective, effective cost containment in one area often merely leads to the creation of new areas of abuse. Clearly, the ideal solution is a system that can evaluate the fairness of payments before they are made, and that can respond to dynamic patterns of abuse. The first step in creating such a system is the creation of robust method for sorting bills for appropriate rule-based analysis on the basis of the type of bill. Currently neither Medicare nor major insurers are capable of making this classification reliably except through the use of inefficient, static rules and the use of manual sorting--a costly and inefficient approach to assuring timely payment to hospitals and medical providers. We propose a novel method for using mathematical algorithms that utilize machine-learning (ML) methods to address the problem of medical bill categorization, the first step in coding error detection. Specifically, we propose the evaluation of a variety of genetic algorithms that are well adapted to the problems of large, dynamic datasets and can be "trained" using real world correctly coded datasets in healthcare claims. This work is particularly timely due to recent Medicare contracting reform. Using more than 50 contractors and carriers, bill classification is largely determined by the carrier's contract. Centralizing this process to only four payment centers will require the classification system we propose. [This research is directed toward the development of software applications that will detect billing errors and perform proper edits to payment of medical bills. Current anticipated changes and reforms in the Medicare system will require these systems, which do not currently exist in the public or private sector.]

描述(由申请人提供)：这项研究的目标是创建软件，使用数学算法来检测支付之前的医疗账单编码错误。众所周知，当前的医疗成本控制技术未能防止商业医疗市场和联邦医疗保险计划中的不当支付，这突显了迫切需要一种新的方法来解决日益严重的医疗成本失控问题。美国政府问责局最近的一项联邦研究估计，仅联邦医疗保险的不当支付就超过了210亿美元，在联邦计划的所有不当支付中，这一数字达到了惊人的48.1%。与垃圾邮件一样，垃圾邮件的动态性质使静态或临时补救措施无效，在一个领域中有效的成本控制往往只会导致新的滥用领域的产生。显然，理想的解决方案是一个系统，它可以在支付之前对支付的公平性进行评估，并能够对动态的滥用模式做出回应。建立这种系统的第一步是建立可靠的票据分类方法，以便在票据类型的基础上进行适当的基于规则的分析。目前，联邦医疗保险和主要保险公司都无法可靠地进行这种分类，除非使用低效、静态的规则和使用人工分类--这是一种成本高昂且效率低下的方法，以确保及时向医院和医疗提供者付款。我们提出了一种新的方法来使用数学算法，利用机器学习(ML)方法来解决医疗票据分类问题，这是编码错误检测的第一步。具体地说，我们建议对各种遗传算法进行评估，这些算法很好地适应了大型动态数据集的问题，并且可以在医疗保健索赔中使用真实世界的正确编码数据集进行“训练”。由于最近的医疗保险合同改革，这项工作特别及时。使用50多家承包商和承运人，账单分类在很大程度上取决于承运人的合同。将这一过程集中到四个支付中心将需要我们提出的分类系统。[这项研究旨在开发软件应用程序，该应用程序将检测账单错误并对医疗账单的支付进行适当的编辑。医疗保险制度目前预期的变化和改革将需要这些制度，而这些制度目前在公共或私营部门并不存在。]