Data-driven models, techniques, and design principles for combating healthcare fraud
Dallas Deverieux Thornton is a PhD student in the department Industrial Engineering & Business Information Systems. (Co)Promotors are prof.dr. J. van Hillegersberg from the faculty of Behavioural, Management and Social Sciences and prof.dr. R.M. Müller from HWR Berlin.
In the U.S., approximately $700 billion of the $2.7 trillion spent on healthcare is linked to fraud, waste, and abuse. This presents a significant challenge for healthcare payers, including governments, insurers, and businesses, as they navigate fraudulent activities from dishonest practitioners, sophisticated criminal networks, and even well-intentioned providers who inadvertently submit incorrect billing for legitimate services. Government-run programs are particularly vulnerable to fraud, given the challenges in excluding problematic providers compared to private networks.
The system's complexity, diversity of actors, and sparsity of labeled data make applying data analysis methods used in other sectors challenging. However, with careful engineering and ongoing adjustments, data analysis techniques such as outlier detection can support programs in controlling escalating costs and maintaining financial stability. This thesis adopts Hevner’s (2004) research methodology to guide the creation, assessment, and refinement of a healthcare fraud detection framework and recommended design principles for fraud detection in other similarly complex environments. The thesis provides the following significant contributions to the field:
1. A formal literature review of the field of fraud detection in Medicaid. Chapters 3 and 4 provide formal reviews of the available literature on healthcare fraud. Chapter 3 focuses on defining the types of fraud found in healthcare. Chapter 4 reviews fraud detection techniques in literature across healthcare and other industries. Chapter 5 focuses on literature covering fraud detection methodologies utilized explicitly in healthcare.
2. A multidimensional data model and analysis techniques for fraud detection in healthcare. Chapter 5 applies Hevner et al. to help develop a framework for fraud detection in Medicaid that provides specific data models and techniques that identify the most prevalent fraud schemes. Based on the environment and knowledge base analysis, a multidimensional schema based on Medicaid data and a set of multidimensional models and techniques to detect fraud in large sets of claim transactions are presented. These artifacts are evaluated through functional testing against known fraud schemes. This chapter contributes a set of multidimensional data models and analysis techniques that can be used to detect the most prevalent known fraud types.
3. A framework for deploying outlier-based fraud detection methods in healthcare. Chapter 6 proposes and evaluates methods for applying outlier detection to healthcare fraud based on literature review, comparative research, direct application on healthcare claims data, and known fraudulent cases. Based on a multi-dimensional data model developed for Medicaid claim data a method for outlier-based fraud detection is presented and evaluated using Medicaid dental claims, providers, and patients in an actual US state Medicaid program.
4. Design principles for fraud detection in complex systems. Based on literature and applied research in Medicaid healthcare fraud detection, Chapter 7 offers generalized design principles for fraud detection in similar complex, multi-stakeholder systems.
