Building Intelligent Systems for Paying Healthcare Providers and Using Social Media to Detect Fraudulent Claims

Abstract

Using an interpretive case study approach, this paper describes the data quality problems in a regional health insurance (RHI) company. Within this company, two interpretive cases examine different processes of the healthcare supply chain and their integration with a business intelligence system. Specifically, the first case examines RHI's provider enrollment and credentialing process, and the second case examines the processes within the special investigations unit (SIU) for investigating and detecting fraud. The second case examines DIQ issues and how social media can be used to acquire evidence to support a fraud case. In addition, the second case utilized lean six sigma to streamline internal processes. A data and information quality (DIQ) assessment of these processes demonstrates how a framework, referred to as PGOT, can identify improvement opportunities within any information intensive environment. This paper provides recommendations for DIQ and social media best practices, and illustrates these best practices within this real-world context of healthcare.