Outlier Detection in Inpatient Claims Using DBSCAN and K-Means

Health insurance helps people to obtain quality and affordable health services. The claim billing process is manually input code to the system, this can lack of errors and be suspected of being fraudulent. Claims suspected of fraud are traced manually to find incorrect inputs. The increasing volume of claims causes a decrease in the accuracy of tracing claims suspected of fraud and consumes time and energy. As an effort to prevent and reduce the occurrence of fraud, this study aims to determine the pattern of data on the occurrence of fraud based on the formation of data groupings. Data was prepared by combining claims for inpatient bills and patient bills from hospitals in 2020. Two methods were used in this study to form clusters, DBSCAN and KMeans. To find out the outliers in the cluster, Local Outlier Factor (LOF) was added. The results from experiments show that both methods can detect outlier data and distribute outlier data in the formed cluster. Variable that high effect becomes data outlier is the length of stay, claims code, and condition of patient when discharged from the hospital. Accuracy K-Means is 0.391, 0.003 higher than DBSCAN, which is 0.389

Autonomic care platform for optimizing query performance

Background: As the amount of information in electronic health care systems increases, data operations get more complicated and time-consuming. Intensive Care platforms require a timely processing of data retrievals to guarantee the continuous display of recent data of patients. Physicians and nurses rely on this data for their decision making. Manual optimization of query executions has become difficult to handle due to the increased amount of queries across multiple sources. Hence, a more automated management is necessary to increase the performance of database queries. The autonomic computing paradigm promises an approach in which the system adapts itself and acts as self-managing entity, thereby limiting human interventions and taking actions. Despite the usage of autonomic control loops in network and software systems, this approach has not been applied so far for health information systems. Methods: We extend the COSARA architecture, an infection surveillance and antibiotic management service platform for the Intensive Care Unit (ICU), with self-managed components to increase the performance of data retrievals. We used real-life ICU COSARA queries to analyse slow performance and measure the impact of optimizations. Each day more than 2 million COSARA queries are executed. Three control loops, which monitor the executions and take action, have been proposed: reactive, deliberative and reflective control loops. We focus on improvements of the execution time of microbiology queries directly related to the visual displays of patients' data on the bedside screens. Results: The results show that autonomic control loops are beneficial for the optimizations in the data executions in the ICU. The application of reactive control loop results in a reduction of 8.61% of the average execution time of microbiology results. The combined application of the reactive and deliberative control loop results in an average query time reduction of 10.92% and the combination of reactive, deliberative and reflective control loops provides a reduction of 13.04%. Conclusions: We found that by controlled reduction of queries' executions the performance for the end-user can be improved. The implementation of autonomic control loops in an existing health platform, COSARA, has a positive effect on the timely data visualization for the physician and nurse

A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets

The term "outlier" can generally be defined as an observation that is significantly different from the other values in a data set. The outliers may be instances of error or indicate events. The task of outlier detection aims at identifying such outliers in order to improve the analysis of data and further discover interesting and useful knowledge about unusual events within numerous applications domains. In this paper, we report on contemporary unsupervised outlier detection techniques for multiple types of data sets and provide a comprehensive taxonomy framework and two decision trees to select the most suitable technique based on data set. Furthermore, we highlight the advantages, disadvantages and performance issues of each class of outlier detection techniques under this taxonomy framework

A reasonable benchmarking frontier using DEA : an incentive scheme to improve efficiency in public hospitals

There exists research relating management concepts with productivity measurement methods that offers useful solutions for improving management control in the public sector. Within this sphere, we connect agency theory with efficiency analysis and describe how to define an incentives scheme that can be applied in the public sector to monitor the efficiency and productivity of managers. To fulfill the main objective of this research, we propose an iterative process for determining what we define as a ‘reasonable frontier’, a concept that provides the foundation required to establish the incentive scheme for the managers. Our ‘reasonable frontier’ has the following properties: i) it detects the presence of outliers, ii) it proposes a procedure to establish the influence introduced by extreme observations, and iii) it sorts out the problem of data masking. The proposed method is applied to a sample of hospitals taken from the public network of the Spanish health service. The results obtained confirm the applicability of the proposal made. Summing up, we define and apply a useful method, combining aspects of agency theory and efficiency analysis, which is of interest to those public authorities trying to design effective incentive schemes which influence the decision making of the public managers

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

Unsupervised learning for anomaly detection in Australian medical payment data

Fraudulent or wasteful medical insurance claims made by health care providers are costly for insurers. Typically, OECD healthcare organisations lose 3-8% of total expenditure due to fraud. As Australia’s universal public health insurer, Medicare Australia, spends approximately $A 34 billion per annum on the Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme, wasted spending of$A1–2.7 billion could be expected.However, fewer than 1% of claims to Medicare Australia are detected as fraudulent, below international benchmarks. Variation is common in medicine, and health conditions, along with their presentation and treatment, are heterogenous by nature. Increasing volumes of data and rapidly changing patterns bring challenges which require novel solutions. Machine learning and data mining are becoming commonplace in this field, but no gold standard is yet available. In this project, requirements are developed for real-world application to compliance analytics at the Australian Government Department of Health and Aged Care (DoH), covering: unsupervised learning; problem generalisation; human interpretability; context discovery; and cost prediction. Three novel methods are presented which rank providers by potentially recoverable costs. These methods used association analysis, topic modelling, and sequential pattern mining to provide interpretable, expert-editable models of typical provider claims. Anomalous providers are identified through comparison to the typical models, using metrics based on costs of excess or upgraded services. Domain knowledge is incorporated in a machine-friendly way in two of the methods through the use of the MBS as an ontology. Validation by subject-matter experts and comparison to existing techniques shows that the methods perform well. The methods are implemented in a software framework which enables rapid prototyping and quality assurance. The code is implemented at the DoH, and further applications as decision-support systems are in progress. The developed requirements will apply to future work in this fiel

A reasonable benchmarking frontier using DEA : an incentive scheme to improve efficiency in public hospitals

There exists research relating management concepts with productivity measurement methods that offers useful solutions for improving management control in the public sector. Within this sphere, we connect agency theory with efficiency analysis and describe how to define an incentives scheme that can be applied in the public sector to monitor the efficiency and productivity of managers. To fulfill the main objective of this research, we propose an iterative process for determining what we define as a ‘reasonable frontier’, a concept that provides the foundation required to establish the incentive scheme for the managers. Our ‘reasonable frontier’ has the following properties: i) it detects the presence of outliers, ii) it proposes a procedure to establish the influence introduced by extreme observations, and iii) it sorts out the problem of data masking. The proposed method is applied to a sample of hospitals taken from the public network of the Spanish health service. The results obtained confirm the applicability of the proposal made. Summing up, we define and apply a useful method, combining aspects of agency theory and efficiency analysis, which is of interest to those public authorities trying to design effective incentive schemes which influence the decision making of the public managers.