A decision support tool for health service re-design

Many of the outpatient services are currently only available in hospitals, however there are plans to provide some of these services alongside with General Practitioners. Consequently, General Practitioners could soon be based at polyclinics. These changes have caused a number of concerns to Hounslow Primary Care Trust (PCT). For example, which of the outpatient services are to be shifted from the hospital to the polyclinic? What are the current and expected future demands for these services? To tackle some of these concerns, the first phase of this project explores the set of specialties that are frequently visited in a sequence (using sequential association rules). The second phase develops an Excel based spreadsheet tool to compute the current and expected future demands for the selected specialties. From the sequential association rule algorithm, endocrinology and ophthalmology were found to be highly associated (i.e. frequently visited in a sequence), which means that these two specialties could easily be shifted from the hospital environment to the polyclinic. We illustrated the Excel based spreadsheet tool for endocrinology and ophthalmology, however, the model is generic enough to cope with other specialties, provided that the data are available

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

RACER : Rule-Associated CasE-based Reasoning for supporting general practitioners in prescription making

Author name used in this manuscript: W.M. WangAuthor name used in this manuscript: S.K. KwokAuthor name used in this manuscript: Albert H.C. Tsang2010-2011 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Improving Fraud and Abuse Detection in General Physician Claims: A Data Mining Study

Background: We aimed to identify the indicators of healthcare fraud and abuse in general physicians’ drug prescription claims, and to identify a subset of general physicians that were more likely to have committed fraud and abuse. Methods: We applied data mining approach to a major health insurance organization dataset of private sector general physicians’ prescription claims. It involved 5 steps: clarifying the nature of the problem and objectives, data preparation, indicator identification and selection, cluster analysis to identify suspect physicians, and discriminant analysis to assess the validity of the clustering approach. Results: Thirteen indicators were developed in total. Over half of the general physicians (54%) were ‘suspects’ of conducting abusive behavior. The results also identified 2% of physicians as suspects of fraud. Discriminant analysis suggested that the indicators demonstrated adequate performance in the detection of physicians who were suspect of perpetrating fraud (98%) and abuse (85%) in a new sample of data. Conclusion: Our data mining approach will help health insurance organizations in low-and middle-income countries (LMICs) in streamlining auditing approaches towards the suspect groups rather than routine auditing of all physician

Mining processes in dentistry

Business processes in dentistry are quickly evolving towards digital dentistry . This means that many steps in the dental process will increasingly deal with computerized information or computerized half products. A complicating factor in the improvement of process performance in dentistry, however, is the large number of independent dental professionals that are involved in the entire process. In order to reap the benefits of digital dentistry, it is essential to obtain an accurate view on the current processes in practice. In this paper, so called process mining techniques are applied in order to demonstrate that, based on automatically stored data, detailed process knowledge can be obtained on dental processes, e.g. it can be discovered how dental processes are actually executed. To this end, we analyze a real case of a private dental practice, which is responsible for the treatment of patients (diagnosis, placing of implants and the placement of the final restoration), and the dental lab that is responsible for the production of the final restoration. To determine the usefulness of process mining, the entire process has been investigated from three different perspectives: (1) the control-flow perspective, (2) the organizational perspective and (3) the performance perspective. The results clearly show that process mining is useful to gain a deep understanding of dental processes. Also, it becomes clear that dental process are rather complex, which require a considerable amount of flexibility. We argue that the introduction of workflow management technology is needed in order to make digital dentistry a success

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

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

A model for the automated detection of fraudulent healthcare claims using data mining methods

Abstract : The menace of fraud today cannot be underestimated. The healthcare system put in place to facilitate rendering medical services as well as improving access to medical services has not been an exception to fraudulent activities. Traditional healthcare claims fraud detection methods no longer suffice due to the increased complexity in the medical billing process. Machine learning has become a very important technique in the computing world today. The abundance of computing power has aided the adoption of machine learning by different problem domains including healthcare claims fraud detection. The study explores the application of different machine learning methods in the process of detecting possible fraudulent healthcare claims fraud. We propose a data mining model that incorporates several knowledge discovery processes in the pipeline. The model makes use of the data from the Medicare payment data from the Centre for Medicare and Medicaid Services as well as data from the List of Excluded Individual or Entities (LEIE) database. The data was then passed through the data pre-processing and transformation stages to get the data to a desirable state. Once the data is in the desired state, we apply several machine learning methods to derive knowledge as well as classify the data into fraudulent and non-fraudulent claims. The results derived from the comprehensive benchmark used on the implemented version of the model, have shown that machine learning methods can be used to detect possible fraudulent healthcare claims. The models based on the Gradient Boosted Tree Classifier and Artificial Neural Network performed best while the Naïve Bayes model couldn’t classify the data. By applying the correct pre-processing method as well as data transformation methods to the Medicare data, along with the appropriate machine learning methods, the healthcare fraud detection system yields nominal results for identification of possible fraudulent claims in the medical billing process.M.Sc. (Computer Science