Meta-analysis of fraud, waste and abuse detection methods in healthcare

Fraud, waste and abuse have been a concern in healthcare system due to the exponential increase in the loss of revenue, loss of reputation and goodwill, and a rapid decline in the relationship between healthcare providers and patients. Consequently, fraud, waste and abuse result in a high cost of healthcare services, decreased quality of care, and threat to patients’ lives. Its enormous side effects in healthcare have attracted diverse efforts in the healthcare industry, data analytics industry and research communities towards the development of fraud detection methods. Hence, this study examines and analyzes fraud, waste and abuse detection methods used in healthcare, to reveal the strengths and limitations of each approach. Eighty eight literatures obtained from journal articles, conference proceedings and books based on their relevance to the research problem were reviewed. The result of this review revealed that fraud detection methods are difficult to implement in the healthcare system because new fraud patterns are constantly developed to circumvent fraud detection methods. Research in medical fraud assessment is limited due to data limitations as well as privacy and confidentiality concerns.Keywords: abuse, fraud, healthcare, waste, fraud detection method

Analyzing the Application of Minimalism in Product Appearance Design using Associative Data Mining Optimized Feature Selection and Deep Learning of Bang&Olufsen Products

The application of minimalism in product appearance design has gained significant attention in recent years due to its focus on simplicity, functionality, and aesthetic appeal. This paper explores the use of Associative Data Mining Optimized Feature Selection (ADM-OFS) classifier with deep learning techniques to analyze the application of minimalism in product appearance design, using Bang&Olufsen products as a case study. The proposed ADM-OFS perform feature selection is performed using an associative data mining approach, which estimates the most relevant and influential features that contribute to minimalistic design. The optimized feature selection process enhances the accuracy and efficiency of the analysis by reducing the dimensionality of the dataset while retaining its essential characteristics. The ADM-OFS model comprises the deep learning techniques employed to capture intricate patterns and relationships between minimalism and product appearance design. The deep learning model is trained on the dataset, enabling it to recognize complex visual features and make predictions about the minimalistic qualities of new product designs. The findings of ADM-OFS provide valuable insights into the application of minimalism in product appearance design, specifically in the context of Bang&Olufsen products. The analysis demonstrated the ADM-OFS classifier with deep learning, in analyzing and interpreting the application of minimalism in product appearance design. The findings of ADM-OFS stated that the designers, manufacturers, and researchers in their pursuit of creating visually appealing and functionally efficient products that embody the principles of minimalism

Medical Insurance Fraud Detection Using Machine Learning

Medical insurance fraud poses significant challenges to the healthcare industry, impacting financial resources and patient care. This research explores the application of machine learning methodologies to detect fraudulent activities within healthcare insurance claims. Medical insurance fraud detection is crucial to help insurance companies save money. Machine learning is a powerful tool that can be used to detect fraudulent activities in the healthcare industry. Fraud can be spread broadly and extremely costly to the therapeutic protection framework. Protection can be made unscrupulous and be a case designed to hide or alter such information meant for social insurance benefits. Cheats might be numerous and submitted by the protection guarantor or the safeguarded. The unscrupulous social insurance providers are the reason for extortion in the well-being segment. This research approach is to apply machine learning to find incidents of medical insurance fraud automatically. In conclusion, machine learning is a promising tool for detecting medical insurance fraud. It can help insurers detect fraudulent activities in real time, saving money on bogus claims

The Business Intelligence Group: Towards Collaborative Research In A Management Information Systems Curriculum

This paper introduces an extension of an approach referred to as the Research Group model, an award winning pedagogical methodology based on the premise that when undergraduate students engage in academic research in close consultation with their professors that their marketable skills are greatly enhanced and that the institutions involved benefit greatly as well. The history of the Research Group concept is detailed, the incentive structure that facilitates faculty buy in is explained and the extension to the general model that defines the Business Intelligence (BI) Group is described. The paper outlines several exemplar projects that have resulted from the approach

Data-Driven Models, Techniques, and Design Principles for Combatting Healthcare Fraud

In the U.S., approximately $700 billion of the$2.7 trillion spent on healthcare is linked to fraud, waste, and abuse. This presents a significant challenge for healthcare payers as they navigate fraudulent activities from dishonest practitioners, sophisticated criminal networks, and even well-intentioned providers who inadvertently submit incorrect billing for legitimate services. This thesis adopts Hevner’s research methodology to guide the creation, assessment, and refinement of a healthcare fraud detection framework and recommended design principles for fraud detection. The thesis provides the following significant contributions to the field:1. A formal literature review of the field of fraud detection in Medicaid. Chapters 3 and 4 provide formal reviews of the available literature on healthcare fraud. Chapter 3 focuses on defining the types of fraud found in healthcare. Chapter 4 reviews fraud detection techniques in literature across healthcare and other industries. Chapter 5 focuses on literature covering fraud detection methodologies utilized explicitly in healthcare.2. A multidimensional data model and analysis techniques for fraud detection in healthcare. Chapter 5 applies Hevner et al. to help develop a framework for fraud detection in Medicaid that provides specific data models and techniques to identify the most prevalent fraud schemes. A multidimensional schema based on Medicaid data and a set of multidimensional models and techniques to detect fraud are presented. These artifacts are evaluated through functional testing against known fraud schemes. This chapter contributes a set of multidimensional data models and analysis techniques that can be used to detect the most prevalent known fraud types.3. A framework for deploying outlier-based fraud detection methods in healthcare. Chapter 6 proposes and evaluates methods for applying outlier detection to healthcare fraud based on literature review, comparative research, direct application on healthcare claims data, and known fraudulent cases. A method for outlier-based fraud detection is presented and evaluated using Medicaid dental claims, providers, and patients.4. Design principles for fraud detection in complex systems. Based on literature and applied research in Medicaid healthcare fraud detection, Chapter 7 offers generalized design principles for fraud detection in similar complex, multi-stakeholder systems.<br/

Correlating Medi-Claim Service by Deep Learning Neural Networks

Medical insurance claims are of organized crimes related to patients, physicians, diagnostic centers, and insurance providers, forming a chain reaction that must be monitored constantly. These kinds of frauds affect the financial growth of both insured people and health insurance companies. The Convolution Neural Network architecture is used to detect fraudulent claims through a correlation study of regression models, which helps to detect money laundering on different claims given by different providers. Supervised and unsupervised classifiers are used to detect fraud and non-fraud claims

JULIAN J. FARAWAY. Extending the Linear Model with R: Generalized Linear, Mixed Effects, and Nonparametric Regression Models, 2nd edition. Boca Raton: CRC Press

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141062/1/biom12825.pd