Graph Analysis for Detecting Fraud, Waste, and Abuse in Healthcare Data

@inproceedings{Liu2015GraphAF,
  title={Graph Analysis for Detecting Fraud, Waste, and Abuse in Healthcare Data},
  author={Juan Liu and Eric A. Bier and Aaron Wilson and John Alexis Guerra G{\'o}mez and Tomonori Honda and Kumar Sricharan and Leilani Gilpin and Daniel Davies},
  booktitle={AI Mag.},
  year={2015}
}
Detection of fraud, waste, and abuse (FWA) is an important yet difficult problem. In this paper, we describe a system to detect suspicious activities in large healthcare claims datasets. Each healthcare dataset is viewed as a heterogeneous network of patients, doctors, pharmacies, and other entities. These networks can be large, with millions of patients, hundreds of thousands of doctors, and tens of thousands of pharmacies, for example. Graph analysis techniques are developed to find… Expand
Graph analytics for healthcare fraud risk estimation
TLDR
A novel approach to estimating healthcare fraud (HCF) risk that applies network algorithms to graphs derived from open source datasets that calculates behavioral similarity to known fraudulent and non-fraudulent healthcare providers with respect to measurable healthcare activities. Expand
A 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 relationshipExpand
Healthcare Fraud Detection Based on Trustworthiness of Doctors
TLDR
The numerical validation with a healthcare dataset demonstrates that healthcare fraud by misdiagnosis in healthcare treatments can be successfully detected by employing the developed fraud detection approach and introduces the copy precision behavior in the treatment sequences of patients, which is a critical metric to learn the trustworthiness of doctors. Expand
Fraudster Detection Based on Modularity Optimization Algorithm
TLDR
Fraudster Detection Based on Modularity Optimization Algorithm (FDMOA) approach is developed to find doctor fraudsters in a good accuracy, which models the medical insurance records as a heterogeneous weighted network of doctors and medicines, and uses a modularity optimization algorithm to divide doctors and drugs into corresponding communities on a heterogeneity network. Expand
Medical Knowledge Graph to Enhance Fraud, Waste, and Abuse Detection on Claim Data: Model Development and Performance Evaluation
TLDR
The medical knowledge graph–based method successfully identified suspected cases of FWA (such as fraud diagnosis, excess prescription, and irrational prescription) from the claim documents, which helped to improve the efficiency of claim processing. Expand
Automatic Detection of Excess Healthcare Spending and Cost Variation in ACOs
There are more than nine hundred Accountable Care Organizations (ACOs) in the United States, both in the public and private sector, serving millions of patients across the country in a process toExpand
A Framework for Fraud Detection in Government Supported National Healthcare Programs
  • Irum Matloob, S. Khan
  • Business
  • 2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)
  • 2019
Fraud, waste and abuse have created prominent cost overruns in health care sector for the last few decades. There is a critical need of fraud detection system to control and monitor health insuranceExpand
Fraudster Detection Based on Label Propagation Algorithm
  • Tingting Luan, Zhongmin Yan, S. Zhang, Yongqing Zheng
  • Computer Science
  • 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)
  • 2018
TLDR
Fraudster Detection Based on Label Propagation Algorithm (FDBLPA) approach is developed to find patient fraudsters in a good accuracy and has higher accuracy and efficiency. Expand
A Hybrid Approach for Detecting Fraudulent Medical Insurance Claims: (Extended Abstract)
TLDR
A Hybrid Fraud Detection Approach (HFDA) is proposed to address medical insurance frauds, which is compared with four state-of-the-art approaches using a real-world dataset and shows that the proposed approach is significantly more effective and efficient. Expand
A Review of Anonymization for Healthcare Data
TLDR
It is illustrated via a reconstruction attack that anonymization, though necessary, is not sufficient to address patient privacy and methods for protecting against such attacks are illustrated. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 19 REFERENCES
Outlier Analysis
TLDR
Outlier Analysis is a comprehensive exposition, as understood by data mining experts, statisticians and computer scientists, and emphasis was placed on simplifying the content, so that students and practitioners can also benefit. Expand
Community detection in graphs
TLDR
A thorough exposition of community structure, or clustering, is attempted, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists. Expand
Isolation Forest
  • F. Liu, K. Ting, Z. Zhou
  • Computer Science
  • 2008 Eighth IEEE International Conference on Data Mining
  • 2008
TLDR
The use of isolation enables the proposed method, iForest, to exploit sub-sampling to an extent that is not feasible in existing methods, creating an algorithm which has a linear time complexity with a low constant and a low memory requirement. Expand
Modularity and community structure in networks.
  • M. Newman
  • Physics, Medicine
  • Proceedings of the National Academy of Sciences of the United States of America
  • 2006
TLDR
It is shown that the modularity of a network can be expressed in terms of the eigenvectors of a characteristic matrix for the network, which is called modularity matrix, and that this expression leads to a spectral algorithm for community detection that returns results of demonstrably higher quality than competing methods in shorter running times. Expand
Group-in-a-Box Layout for Multi-faceted Analysis of Communities
TLDR
Group-In-a-Box (GIB) is proposed, a meta-layout for clustered graphs that enables multi-faceted analysis of networks and uses the tree map space filling technique to display each graph cluster or category group within its own box, sized according to the number of vertices therein. Expand
A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise
TLDR
DBSCAN, a new clustering algorithm relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape, is presented which requires only one input parameter and supports the user in determining an appropriate value for it. Expand
Big data: The next frontier for innovation, competition, and productivity
The amount of data in our world has been exploding, and analyzing large data sets—so-called big data— will become a key basis of competition, underpinning new waves of productivity growth,Expand
The Health Insurance Portability and Accountability Act.
  • A. Meyer
  • Business, Medicine
  • Tennessee medicine : journal of the Tennessee Medical Association
  • 1997
TLDR
The Health Insurance Portability and Accountability Act, also known as HIPAA, was designed to protect health insurance coverage for workers and their families while between jobs and establishes standards for electronic health care transactions. Expand
Latent Dirichlet Allocation
We propose a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams [6], andExpand
Drug wars in the United States.
TLDR
The recent declaration by President George Bush of a war on drugs raised few eyebrows here in the United States, but the most visible effect of the announcement has been to make many people confront the failure of traditional prohibition style policies in controlling illicit drug use among American ghetto dwellers and middle class youth alike. Expand
...
1
2
...