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Business Intelligence for Healthcare: A Prescription for Better Managing Costs and Medical Outcomes

Business Intelligence for Healthcare: A Prescription for Better Managing Costs and Medical Outcomes

Jack S. Cook, Pamela A. Neely
Copyright: © 2016 |Pages: 24
ISBN13: 9781466687561|ISBN10: 1466687568|EISBN13: 9781466687578
DOI: 10.4018/978-1-4666-8756-1.ch076
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MLA

Cook, Jack S., and Pamela A. Neely. "Business Intelligence for Healthcare: A Prescription for Better Managing Costs and Medical Outcomes." E-Health and Telemedicine: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2016, pp. 1506-1529. https://doi.org/10.4018/978-1-4666-8756-1.ch076

APA

Cook, J. S. & Neely, P. A. (2016). Business Intelligence for Healthcare: A Prescription for Better Managing Costs and Medical Outcomes. In I. Management Association (Ed.), E-Health and Telemedicine: Concepts, Methodologies, Tools, and Applications (pp. 1506-1529). IGI Global. https://doi.org/10.4018/978-1-4666-8756-1.ch076

Chicago

Cook, Jack S., and Pamela A. Neely. "Business Intelligence for Healthcare: A Prescription for Better Managing Costs and Medical Outcomes." In E-Health and Telemedicine: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1506-1529. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-8756-1.ch076

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Abstract

Using an interpretive case study approach, this chapter describes the data quality problems in two companies: (1) a Multi-Facility Healthcare Medical Group (MHMG), and (2) a Regional Health Insurance Company (RHIS). These two interpretive cases examine two different processes of the healthcare supply chain and their integration with a business intelligence system. Specifically, the issues examined are MHMG's revenue cycle management and RHIS's provider enrollment and credentialing process. A Data and Information Quality (DIQ) assessment of the revenue cycle management process demonstrates how a framework, referred to as PGOT, can identify improvement opportunities within any information-intensive environment. Based on the assessment of the revenue cycle management process, data quality problems associated with the key processes and their implications for the healthcare organization are described. This chapter provides recommendations for DIQ best practices and illustrates these best practices within this real world context of healthcare.

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