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

Examining the Transitional Impact of ICD-10 on Healthcare Fraud Detection

On October 1st, 2015, the tenth revision of the International Classification of Diseases (ICD-10) will be mandatorily implemented in the United States. Although this medical classification system will allow healthcare professionals to code with greater accuracy, specificity, and detail, these codes will have a significant impact on the flavor of healthcare insurance claims. While the overall benefit of ICD-10 throughout the healthcare industry is unquestionable, some experts believe healthcare fraud detection and prevention could experience an initial drop in performance due to the implementation of ICD-10. We aim to quantitatively test the validity of this concern regarding an adverse transitional impact. This project explores how predictive fraud detection systems developed using ICD-9 claims data will initially react to the introduction of ICD-10. We have developed a basic fraud detection system incorporating both unsupervised and supervised learning methods in order to examine the potential fraudulence of both ICD-9 and ICD-10 claims in a predictive environment. Using this system, we are able to analyze the ability and performance of statistical methods trained using ICD-9 data to properly identify fraudulent ICD-10 claims. This research makes contributions to the domains of medical coding, healthcare informatics, and fraud detection

Fraud: and anomaly detection in healthcare: an unsupervised machine learning approach

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsFraud and abuse in healthcare are critical and cause significant damage. However, the auditing of healthcare encounters is cumbersome, and the detection of fraud and abuse is challenging and binds capacity. Data-driven fraud and anomaly detection models can help to overcome these issues. This work proposes several unsupervised learning methods to understand patterns and detect abnormal healthcare encounters which might be fraudulent or abusive. The ensemble of models is split into sub-processes and applied on a healthcare data set belonging to Future Healthcare group, a Portuguese group acting in health insurance. One major part of the ensemble is the implementation of the Isolation Forest algorithm, which achieves good results in precision and recall and detect new potential fraudulent abnormal behaviour. Due to unlabelled data and the application of unsupervised learning methods, the proposed model detects new fraudulent patterns instead of learning from existing patterns. Besides the model to predict whether new incoming medical encounters are fraudulent or abusive, this work illustrates a visual method to detect suspicious networks among medical providers. In addition, this work contains an approach to predict whether a customer will cancel the insurance based on anomalous behaviour. This internship report aims to contribute to science and be public, even though some parts could not be explained in detail due to confidentiality

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/