Fraud, waste, and abuse in the U.S. healthcare system are estimated at $700 billion annually. Predictive analytics offers government and private payers the opportunity to identify and prevent or recover such billings. This paper proposes a data-driven method for fraud detection based on comparative research, fraud cases, and literature review. Unsupervised data mining techniques such as outlier detection are suggested as effective predictors for fraud. Based on a multi-dimensional data model developed for Medicaid claim data, specific metrics for dental providers were developed and evaluated in analytical experiments using outlier detection applied to claim, provider, and patient data in a state Medicaid program. The proposed methodology enabled successful identification of fraudulent activity, with 12 of the top 17 suspicious providers (71%) referred to officials for investigation with clearly anomalous and inappropriate activity. Future research is underway to extend the method to other specialties and enable its use by fraud analysts.

Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data

Increasingly available open medical and health datasets encourage data-driven research with a promise of improving patient care through knowledge discovery and algorithm development. Among efficient approaches to such high-dimensional problems are a number of machine learning methods, which are applied in this paper to pressure ulcer prediction in modular critical care data. An inherent property of many health-related datasets is a high number of irregularly sampled time-variant and scarcely populated features, often exceeding the number of observations. Although machine learning methods are known to work well under such circumstances, many choices regarding model and data processing exist. In particular, this paper address both theoretical and practical aspects related to the application of six classification models to pressure ulcers, while utilizing one of the largest available Medical Information Mart for Intensive Care (MIMIC-IV) databases. Random forest, with an accuracy of 96%, is the best-performing approach among the considered machine learning algorithms.

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Machine Learning-Based Pressure Ulcer Prediction in Modular Critical Care Data

Detecting fraudulent and abusive cases in healthcare is one of the most challenging problems for data mining studies. Existing studies have a lack of real data for analysis and focus on a very partial version of the problem by covering only a specific actor, healthcare service, or disease. In this article, the proposed strategy identifies fraudulent behaviors in Medicare claims data using several predictors as model inputs. The methodology involves preprocessing and model development phases. At the initial phase, the feature mining is done by estimating their feature importance score. The labeling of instances by using the classification rules to the whole dataset. Thus, a transformed dataset is obtained by the model. In the development phase, the RF with SMOTE is applied against the training and testing data. Specifically, SMOTE adapted to balance data and sorts misclassified instances and finds the interesting instances. The results of the proposed model improvises the classifier performance RF with SMOTE when contrast with RF method.

Identifying Fraudulent Behaviors in Healthcare Claims Using Random Forest Classifier With SMOTEchnique

In this work, we consider the task of detecting anomalous nodes in static attributed graphs in the absence of labeled samples. Anomaly (or outlier) detection is generally a diﬃcult problem to solve. This is due to the absence of a con-crete deﬁnition of what constitutes an anomaly, but also because anomalies themselves occur very rarely and ﬁnding enough samples to learn from is often impossible. In this work, we propose two methods that combine Graph Neural Networks (GNNs) with a self-supervised training scheme to learn a representation of normal (non-anomalous) nodes in the graph, and identify anomalies using these learned representations. Both models, called HideGNN and MaskGNN , use a mask-based self-supervision approach, whereby the algorithm is set up to predict the masked or hidden features of nodes at train time. We show experimentally that our approach outperforms known baselines on real-world datasets and we explore the power of the methods in detecting speciﬁc types of anomalies with synthetic data.

Self-supervised anomaly detection in static attributed graphs

Detection of Undeserved Sick Leaves in Hospitals using Machine Learning Techniques

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A Supervised Approach for Detection of Outliers in Healthcare Claims Data


Purpose
Analyzing medicare data is a role undertaken by the government and commercial companies for accepting the appeals and sanctioning the claims of those insured under Medicare. As the data of medicare is robust and made up of heterogeneous typed columns, traditional approaches consist of a laborious and time-consuming process. The understanding and processing of such data sets and finding the role of each attribute for data analysis are tricky tasks which this research will attempt to ease. The paper aims to discuss these issues.


Design/methodology/approach
This paper proposes a Hierarchical Grouping (HG) with an experimental model to handle the complex data and analysis of the categorical data which consist of heterogeneous typed columns. The HG methodology starts with feature subset selection. HG forms a structure by quantitatively estimating the similarities and forms groups of the features for data. This is carried by applying metrics like decomposition; it splits the dataset and helps to analyze thoroughly under different labels with different selected attributes of Medicare data. The method of fixed regression includes metrics of re-indexing and grouping which works well for multiple keys (multi-index) of categorical data. The final stage of structure is applying multiple aggregation function on each attribute for quantitative computation.


Findings
The data are analyzed quantitatively with the HG mechanism. The results shown in this paper took less computation cost and speed, which are usually incurred on the publicly available data sets.


Practical implications
The motive of this paper is to provide a supportive work for the tasks like outlier detection, prediction, decision making and prescriptive tasks for multi-dimensional data.


Originality/value
It provides a new efficient approach to analyze medicare data sets.


Experimental analysis of Medicare data using hierarchical grouping mechanism

Decision making toward Medicare in the running environment has been taken topmost priority. Processing and understanding of Medicare data are a tricky challenge. The recommended system design carries in various stages to work insight to the problem. In this paper, we use machine learning algorithms for accurate decision on Medicare data. Specifically, the paper analyzes heterogeneous-typed data with the use of hierarchical grouping (HG) mechanismin at the preprocessing phase. Application of metrics like multiple aggregate, grouping and re-indexing simplifies the process in the both the phases. While in the development phase, detection outlier with the perspective of claims provided by the provider given at prior (history). Prior cost acts as good indicator for the decision. Use of statistical-based approach the outlier amount is detected. Random forest (RF) algorithm generates RF trees, and they were able to generate accurate results to choose cost of surgery (disease) from the provided data. Our system is useful to evaluate with reasonably low costs and error free, as demonstrated in experimentation on real-world datasets which are publicly available.

P Naga Jyothi

Papers

A Supervised Approach for Detection of Outliers in Healthcare Claims Data

Identifying Fraudulent Behaviors in Healthcare Claims Using Random Forest Classifier With SMOTEchnique

Experimental analysis of Medicare data using hierarchical grouping mechanism

A Comprehensive View for Providing the Decision on Medicare Data