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/

A Hybrid Mining Approach to Facilitate Health Insurance Decision: Case Study of Non-Traditional Data Mining Applications in Taiwan NHI Databases

This study examines time-sensitive applications of data mining methods to facilitate claims review processing and provide policy information for insurance decision-making vis-à-vis the Taiwan National Health Insurance databases. In order to obtain the best payment management, a hybrid mining approach, which has been grounded on the extant knowledge of data mining projects and health insurance domain knowledge, is proposed. Through the integration of data warehousing, online analytical processing, data mining techniques and traditional data analysis in the healthcare field, an easy-to-use decision support platform, which will facilitate the health insurance decision-making process, is built. Drawing from lessons learned in case study, results showed that not only is hybrid mining approach a reliable, powerful, and user-friendly platform for diversified payment decision support, but that it also has great relevance for the practice and acceptance of evidence-based medicine. Researchers should develop hybrid mining approach combined with their own application systems in the future

A Hybrid Mining Approach to Facilitate Health Insurance Decision: Case Study of Non-Traditional Data Mining Applications in Taiwan NHI Databases

This study examines time-sensitive applications of data mining methods to facilitate claims review processing and provide policy information for insurance decision-making vis-à-vis the Taiwan National Health Insurance databases. In order to obtain the best payment management, a hybrid mining approach, which has been grounded on the extant knowledge of data mining projects and health insurance domain knowledge, is proposed. Through the integration of data warehousing, online analytical processing, data mining techniques and traditional data analysis in the healthcare field, an easy-to-use decision support platform, which will facilitate the health insurance decision-making process, is built. Drawing from lessons learned in case study, results showed that not only is hybrid mining approach a reliable, powerful, and user-friendly platform for diversified payment decision support, but that it also has great relevance for the practice and acceptance of evidence-based medicine. Researchers should develop hybrid mining approach combined with their own application systems in the future

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

진료 내역 데이터를 활용한 딥러닝 기반의 건강보험 남용 탐지

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2020. 8. 조성준.As global life expectancy increases, spending on healthcare grows in accordance in order to improve quality of life. However, due to expensive price of medical care, the bare cost of healthcare services would inevitably places great financial burden to individuals and households. In this light, many countries have devised and established their own public healthcare insurance systems to help people receive medical services at a lower price. Since reimbursements are made ex-post, unethical practices arise, exploiting the post-payment structure of the insurance system. The archetypes of such behavior are overdiagnosis, the act of manipulating patients diseases, and overtreatments, prescribing unnecessary drugs for the patient. These abusive behaviors are considered as one of the main sources of financial loss incurred in the healthcare system. In order to detect and prevent abuse, the national healthcare insurance hires medical professionals to manually examine whether the claim filing is medically legitimate or not. However, the review process is, unquestionably, very costly and time-consuming. In order to address these limitations, data mining techniques have been employed to detect problematic claims or abusive providers showing an abnormal billing pattern. However, these cases only used coarsely grained information such as claim-level or provider-level data. This extracted information may lead to degradation of the model's performance. In this thesis, we proposed abuse detection methods using the medical treatment data, which is the lowest level information of the healthcare insurance claim. Firstly, we propose a scoring model based on which abusive providers are detected and show that the review process with the proposed model is more efficient than that with the previous model which uses the provider-level variables as input variables. At the same time, we devise the evaluation metrics to quantify the efficiency of the review process. Secondly, we propose the method of detecting overtreatment under seasonality, which reflects more reality to the model. We propose a model embodying multiple structures specific to DRG codes selected as important for each given department. We show that the proposed method is more robust to the seasonality than the previous method. Thirdly, we propose an overtreatment detection model accounting for heterogeneous treatment between practitioners. We proposed a network-based approach through which the relationship between the diseases and treatments is considered during the overtreatment detection process. Experimental results show that the proposed method classify the treatment well which does not explicitly exist in the training set. From these works, we show that using treatment data allows modeling abuse detection at various levels: treatment, claim, and provider-level.사람들의 기대수명이 증가함에 따라 삶의 질을 향상시키기 위해 보건의료에 소비하는 금액은 증가하고 있다. 그러나, 비싼 의료 서비스 비용은 필연적으로 개인과 가정에게 큰 재정적 부담을 주게된다. 이를 방지하기 위해, 많은 국가에서는 공공 의료 보험 시스템을 도입하여 사람들이 적절한 가격에 의료서비스를 받을 수 있도록 하고 있다. 일반적으로, 환자가 먼저 서비스를 받고 나서 일부만 지불하고 나면, 보험 회사가 사후에 해당 의료 기관에 잔여 금액을 상환을 하는 제도로 운영된다. 그러나 이러한 제도를 악용하여 환자의 질병을 조작하거나 과잉진료를 하는 등의 부당청구가 발생하기도 한다. 이러한 행위들은 의료 시스템에서 발생하는 주요 재정 손실의 이유 중 하나로, 이를 방지하기 위해, 보험회사에서는 의료 전문가를 고용하여 의학적 정당성여부를 일일히 검사한다. 그러나, 이러한 검토과정은 매우 비싸고 많은 시간이 소요된다. 이러한 검토과정을 효율적으로 하기 위해, 데이터마이닝 기법을 활용하여 문제가 있는 청구서나 청구 패턴이 비정상적인 의료 서비스 공급자를 탐지하는 연구가 있어왔다. 그러나, 이러한 연구들은 데이터로부터 청구서 단위나 공급자 단위의 변수를 유도하여 모델을 학습한 사례들로, 가장 낮은 단위의 데이터인 진료 내역 데이터를 활용하지 못했다. 이 논문에서는 청구서에서 가장 낮은 단위의 데이터인 진료 내역 데이터를 활용하여 부당청구를 탐지하는 방법론을 제안한다. 첫째, 비정상적인 청구 패턴을 갖는 의료 서비스 제공자를 탐지하는 방법론을 제안하였다. 이를 실제 데이터에 적용하였을 때, 기존의 공급자 단위의 변수를 사용한 방법보다 더 효율적인 심사가 이루어 짐을 확인하였다. 이 때, 효율성을 정량화하기 위한 평가 척도도 제안하였다. 둘째로, 청구서의 계절성이 존재하는 상황에서 과잉진료를 탐지하는 방법을 제안하였다. 이 때, 진료 과목단위로 모델을 운영하는 대신 질병군(DRG) 단위로 모델을 학습하고 평가하는 방법을 제안하였다. 그리고 실제 데이터에 적용하였을 때, 제안한 방법이 기존 방법보다 계절성에 더 강건함을 확인하였다. 셋째로, 동일 환자에 대해서 의사간의 상이한 진료 패턴을 갖는 환경에서의 과잉진료 탐지 방법을 제안하였다. 이는 환자의 질병과 진료내역간의 관계를 네트워크 기반으로 모델링하는것을 기반으로 한다. 실험 결과 제안한 방법이 학습 데이터에서 나타나지 않는 진료 패턴에 대해서도 잘 분류함을 알 수 있었다. 그리고 이러한 연구들로부터 진료 내역을 활용하였을 때, 진료내역, 청구서, 의료 서비스 제공자 등 다양한 레벨에서의 부당 청구를 탐지할 수 있음을 확인하였다.Chapter 1 Introduction 1 Chapter 2 Detection of Abusive Providers by department with Neural Network 9 2.1 Background 9 2.2 Literature Review 12 2.2.1 Abnormality Detection in Healthcare Insurance with Datamining Technique 12 2.2.2 Feed-Forward Neural Network 17 2.3 Proposed Method 21 2.3.1 Calculating the Likelihood of Abuse for each Treatment with Deep Neural Network 22 2.3.2 Calculating the Abuse Score of the Provider 25 2.4 Experiments 26 2.4.1 Data Description 27 2.4.2 Experimental Settings 32 2.4.3 Evaluation Measure (1): Relative Efficiency 33 2.4.4 Evaluation Measure (2): Precision at k 37 2.5 Results 38 2.5.1 Results in the test set 38 2.5.2 The Relationship among the Claimed Amount, the Abused Amount and the Abuse Score 40 2.5.3 The Relationship between the Performance of the Treatment Scoring Model and Review Efficiency 41 2.5.4 Treatment Scoring Model Results 42 2.5.5 Post-deployment Performance 44 2.6 Summary 45 Chapter 3 Detection of overtreatment by Diagnosis-related Group with Neural Network 48 3.1 Background 48 3.2 Literature review 51 3.2.1 Seasonality in disease 51 3.2.2 Diagnosis related group 52 3.3 Proposed method 54 3.3.1 Training a deep neural network model for treatment classi fication 55 3.3.2 Comparing the Performance of DRG-based Model against the department-based Model 57 3.4 Experiments 60 3.4.1 Data Description and Preprocessing 60 3.4.2 Performance Measures 64 3.4.3 Experimental Settings 65 3.5 Results 65 3.5.1 Overtreatment Detection 65 3.5.2 Abnormal Claim Detection 67 3.6 Summary 68 Chapter 4 Detection of overtreatment with graph embedding of disease-treatment pair 70 4.1 Background 70 4.2 Literature review 72 4.2.1 Graph embedding methods 73 4.2.2 Application of graph embedding methods to biomedical data analysis 79 4.2.3 Medical concept embedding methods 87 4.3 Proposed method 88 4.3.1 Network construction 89 4.3.2 Link Prediction between the Disease and the Treatment 90 4.3.3 Overtreatment Detection 93 4.4 Experiments 96 4.4.1 Data Description 97 4.4.2 Experimental Settings 99 4.5 Results 102 4.5.1 Network Construction 102 4.5.2 Link Prediction between the Disease and the Treatment 104 4.5.3 Overtreatment Detection 105 4.6 Summary 106 Chapter 5 Conclusion 108 5.1 Contribution 108 5.2 Future Work 110 Bibliography 112 국문초록 129Docto

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

African Americans\u27 Understanding of the Provisions of the Affordable Care Act

African Americans as a group tend to have numerous health issues, and by the time they seek healthcare, the conditions often advance into more serious ailments. Researchers have shown that cultural distrust of the U.S. healthcare system coupled with some discriminatory practices has hindered African Americans seeking care and taking full advantage of the healthcare system. With the introduction of the Patient Protection and Affordable Care Act (ACA), there is some confusion about the application of the law, resulting in many intended beneficiaries misinterpreting it, and not accessing quality healthcare. This interpretive phenomenological study explored how African Americans in the Washington D.C., metropolitan area understand and interpret ACA provisions related to their healthcare needs. A sample of 10 African Americans adults participated in in-depth interviews that generated data for this study. Critical communicative methodology provided a framework for exploring the phenomenon. Study findings highlight the themes of interpreting the ACA to mean having more individual control and responsibility for healthcare decisions and having fewer perceived restrictions acting as barriers to access healthcare. Study results contribute to social change by providing knowledge to those who implement healthcare policy on optimal policy delivery to intended beneficiaries. This knowledge may also apply to other new social policy dissemination, thus ensuring policies reach their intended target populations, facilitating optimal utilization