Data-Driven Implementation To Filter Fraudulent Medicaid Applications

There has been much work to improve IT systems for managing and maintaining health records. The U.S government is trying to integrate different types of health care data for providers and patients. Health care fraud detection research has focused on claims by providers, physicians, hospitals, and other medical service providers to detect fraudulent billing, abuse, and waste. Data-mining techniques have been used to detect patterns in health care fraud and reduce the amount of waste and abuse in the health care system. However, less attention has been paid to implementing a system to detect fraudulent applications, specifically for Medicaid. In this study, a data-driven system using layered architecture to filter fraudulent applications for Medicaid was proposed. The Medicaid Eligibility Application System utilizes a set of public and private databases that contain individual asset records. These asset records are used to determine the Medicaid eligibility of applicants using a scoring model integrated with a threshold algorithm. The findings indicated that by using the proposed data-driven approach, the state Medicaid agency could filter fraudulent Medicaid applications and save over $4 million in Medicaid expenditures

Data-Driven Implementation To Filter Fraudulent Medicaid Applications

There has been much work to improve IT systems for managing and maintaining health records. The U.S government is trying to integrate different types of health care data for providers and patients. Health care fraud detection research has focused on claims by providers, physicians, hospitals, and other medical service providers to detect fraudulent billing, abuse, and waste. Data-mining techniques have been used to detect patterns in health care fraud and reduce the amount of waste and abuse in the health care system. However, less attention has been paid to implementing a system to detect fraudulent applications, specifically for Medicaid. In this study, a data-driven system using layered architecture to filter fraudulent applications for Medicaid was proposed. The Medicaid Eligibility Application System utilizes a set of public and private databases that contain individual asset records. These asset records are used to determine the Medicaid eligibility of applicants using a scoring model integrated with a threshold algorithm. The findings indicated that by using the proposed data-driven approach, the state Medicaid agency could filter fraudulent Medicaid applications and save over $4 million in Medicaid expenditures

Fighting Cybercrime After \u3cem\u3eUnited States v. Jones\u3c/em\u3e

In a landmark non-decision last term, five Justices of the United States Supreme Court would have held that citizens possess a Fourth Amendment right to expect that certain quantities of information about them will remain private, even if they have no such expectations with respect to any of the information or data constituting that whole. This quantitative approach to evaluating and protecting Fourth Amendment rights is certainly novel and raises serious conceptual, doctrinal, and practical challenges. In other works, we have met these challenges by engaging in a careful analysis of this “mosaic theory” and by proposing that courts focus on the technologies that make collecting and aggregating large quantities of information possible. In those efforts, we focused on reasonable expectations held by “the people” that they will not be subjected to broad and indiscriminate surveillance. These expectations are anchored in Founding-era concerns about the capacity for unfettered search powers to promote an authoritarian surveillance state. Although we also readily acknowledged that there are legitimate and competing governmental and law enforcement interests at stake in the deployment and use of surveillance technologies that implicate reasonable interests in quantitative privacy, we did little more. In this Article, we begin to address that omission by focusing on the legitimate governmental and law enforcement interests at stake in preventing, detecting, and prosecuting cyber-harassment and healthcare fraud

Financial Exploitation of the Elderly in Tennessee

https://digitalcommons.memphis.edu/govpubs-tn-comptroller-office-research-education-accountability/1037/thumbnail.jp

Disaster Relief Medicaid Evaluation Project

[Excerpt] This study is a retrospective evaluation of the enrollment processes and service delivery associated with DRM. It examines this unexpected experiment and assesses the outcomes. This report begins with an overview of the Medicaid/Family Health Plus program in September 2001, and is followed by a description of the challenges of, and responses to, the World Trade Center disaster. It then looks at how well the DRM process worked, how accessible needed services were for recipients, how costs compared to costs associated with those previously enrolled in the traditional Medicaid program, and how the different eligibility/verification procedures affected program integrity. Finally, in the section Background Information: Detailed History of Disaster Relief Medicaid, it presents a narrative timeline, detailing the decision steps by which DRM was implemented

POVERTY LAWGORITHMS A Poverty Lawyer’s Guide to Fighting Automated Decision-Making Harms on Low-Income Communities

Automated decision-making systems make decisions about our lives, and those with low-socioeconomic status often bear the brunt of the harms these systems cause. Poverty Lawgorithms: A Poverty Lawyers Guide to Fighting Automated Decision-Making Harms on Low-Income Communities is a guide by Data & Society Faculty Fellow Michele Gilman to familiarize fellow poverty and civil legal services lawyers with the ins and outs of data-centric and automated-decision making systems, so that they can clearly understand the sources of the problems their clients are facing and effectively advocate on their behalf

Low Medicaid Spending Growth Amid Rebounding State Revenues: Results From a 50-State Medicaid Budget Survey State Fiscal Years 2006 and 2007

Examines the implementation of the new Medicare prescription drug benefit and the rate of Medicaid spending growth and enrollment in 2006. Identifies possible state level changes in eligibility requirements, program expansion, and enrollment processes