Roland M. Mueller

Outlier detection in healthcare fraud: A case study in the Medicaid dental domain

Health care insurance fraud is a pressing problem, causing substantial and increasing costs in medical insurance programs. Due to large amounts of claims submitted, estimated at 5 billion per day, review of individual claims or providers is a difficult task. This encourages the employment of automated pre-payment controls and better post-payment decision support tools to enable subject matter expert analysis. This paper presents how to apply unsupervised outlier techniques at post-payment stage to detect fraudulent patterns of received insurance claims. A special emphasis in this paper is put on the system architecture, the metrics designed for outlier detection and the flagging of suspicious providers which may support the fraud experts in evaluating providers and reveal fraud. The algorithms were tested on Medicaid data encompassing 650,000 health-care claims and 369 dentists of one state. Two health care fraud experts evaluated flagged cases and concluded that 12 of the top 17 providers (71%) submitted suspicious claim patterns and should be referred to officials for further investigation. The remaining 5 providers (29%) could be considered mis-classifications as their patterns could be explained by special characteristics of the provider. Selecting top flagged providers is demonstrated to be a valuable as an targeting method, and individual provider analysis revealed some cases of potential fraud. The study concludes that, through outlier detection, new patterns of potential fraud can be identified and possibly utilized in future automated detection mechanisms.

Information quality dimensions for the social web

User generated content has become a valuable part of the Social Web, and high quality web content is used to influence buying decisions in a growing number of business domains. Numerous information quality frameworks have been proposed in the last years. Since 1989 at least twenty frameworks were written concerning information quality in different contexts. Most of these frameworks have been developed for databases or data warehousing in a business context. But information quality has also gained vital importance for collaboratively created content in the so-called Web 2.0. Based on literature reviews, recent publications, and Grounded Theory research on Web 2.0 sites, we propose an extended set and a new classification of information quality dimensions for the Social Web in general. Our comprehensive list of information quality dimensions can be applied to the engineering, analysis, and management of digital ecosystems and serves as a base for future research in this area.

Handling of Task Hierarchies on the Nepomuk Social Semantic Desktop

The idea of the Semantic Web is becoming more and more reality. Recent developments in Semantic Desktop research bring semantically enriched data onto the knowledge workers desktop environment. A crucial application for todays knowledge worker is task management, which goes beyond simple ToDo lists. In this paper we explain why task management applications in social semantic environments need an appropriate user interface to take advantage of the opportunities ahead. Then we propose the usage of an UI which is tailored towards the underlying data schema: the task management ontology. One challenge for task management based on flexible semantic schemas in social environments is to scope on what should be shared and what not. Sharing and transfer of semantic, graph-represented information is highly desired. We propose WYSIWYS: What You See Is What You Share. This paradigm gives the human user an interface where he/she maintains control over what is shared and what not.

A Meta-model for Inferring Inter-theory Relationships of Causal Theories

This paper suggests a new way of comparing and analyzing causal theories. The main contribution is a meta-model that represents causal theories and a taxonomy of inter-theory relationships. The inter-theory relationships can be automatically calculated for two theories that are described with the meta-model. Two visualizations are presented with which to analyze set of theories: the inter-theory relationship matrix and the theory evolution graph. An exemplary application of the approach is shown for a small set of information systems theories. The proposed approach should help researchers improve their understanding of the contribution and evolution of theories.

Theory-Data Maps: A Meta-Model and Methods for Inferring and Visualizing Relationships between Causal Theories and Empirical Evidences

The number of scientific publications has increased exponentially in the last decades. To better compare these ever-increasing results and to help further replication studies, the use of open data has been suggested. But how should empirical data be described? This paper presents a meta-model for empirical findings that is conceptually linked with the formal description of causal theories. Based on the meta-model, we present methods to calculate the expected empirical outcome for causal theories given a specific research setting. Additionally, a method for automatically determining the relationship between a causal theory and an empirical test is suggested. Based on the meta-model and the methods, a new form of visualization (theory-data maps) is used to show the relationship between causal theories and empirical data. We demonstrate the preliminary applicability of the approach based on an example application to information systems theories and empirical results.

Ethnographic Design Research With Wearable Cameras

This paper presents a novel ethnographic research approach based on a wearable camera that automatically takes pictures according to sensor changes within the wearers environment. The opportunities and challenges that are raised by such technologies are investigated and discussed. We describe an exemplary application of the approach and point out the arising possibilities and limitations. As the main contribution we present a method for evaluating the ethnographic research data that is being produced through this kind of research approach. Furthermore, we propose a set of frameworks for analyzing the resulting images and related metadata. This method is considered a first attempt to facilitate the analysis process of the produced qualitative and quantitative data and is subject to further investigation and development.

Using Incentive Systems to Increase Information Quality in Business Intelligence: A Quasi-Experiment in the Financial Services Industry

We present an alignment model of Business Intelligence and incentive systems with the aim to increase the information quality. We propose two hypotheses: An aligned incentive system leads to higher quality of information. However, information quality that is not subject to incentives will decrease after the introduction of such an incentive scheme. This can be explained by a crowding-out effect of previous intrinsic motivated behavior. These hypotheses are tested with a quasi-experiment in the financial services industry.