Combining data mining and case-based reasoning for intelligent decision support for pathology ordering by general practitioners Zoe Y. Zhuang a , Leonid Churilov b, * , Frada Burstein c , Ken Sikaris d a Faculty of Business and Economics, Monash University, Australia b The University of Melbourne, Australia c Faculty of Information Technology, Monash University, Australia d Melbourne University Clinical School, St. Vincent’s & Geelong Hospitals, The University of Melbourne, Australia Available online 7 November 2007 Abstract Pathology ordering by general practitioners (GPs) is a significant contributor to rising health care costs both in Australia and world- wide. A thorough understanding of the nature and patterns of pathology utilization is an essential requirement for effective decision sup- port for pathology ordering. In this paper a novel methodology for integrating data mining and case-based reasoning for decision support for pathology ordering is proposed. It is demonstrated how this methodology can facilitate intelligent decision support that is both patient-oriented and deeply rooted in practical peer-group evidence. Comprehensive data collected by professional pathology companies provide a system-wide profile of patient-specific pathology requests by various GPs as opposed to that limited to an individual GP practice. Using the real data provided by XYZ Pathology Company in Australia that contain more than 1.5 million records of pathology requests by general practitioners (GPs), we illustrate how knowledge extracted from these data through data mining with Kohonen’s self-organizing maps constitutes the base that, with further assistance of modern data visualization tools and on-line process- ing interfaces, can provide ‘‘peer-group consensus’’ evidence support for solving new cases of pathology test ordering problem. The con- clusion is that the formal methodology that integrates case-based reasoning principles which are inherently close to GPs’ daily practice, and data-driven computationally intensive knowledge discovery mechanisms which can be applied to massive amounts of the pathology requests data routinely available at professional pathology companies, can facilitate more informed evidential decision making by doc- tors in the area of pathology ordering. Ó 2007 Elsevier B.V. All rights reserved. Keywords: Decision support; Data mining; Case-based reasoning; Data clustering; Kohonen’s self-organizing maps; Health care systems 1. Introduction Increasing use of clinical pathology services has long been recognized as a worldwide phenomenon in countries with different healthcare systems, and has attracted the attention of researchers, practitioners and governments all over the world. In Australia, general practitioners (GPs) order and manage most of the pathology requests (Cohen et al., 1998). According to the bettering the evalua- tion and care of health (BEACH) study, a nationwide sur- vey and ongoing program on general practice activity in Australia (Britt et al., 2004), there has been a significant increase in the number of pathology tests ordered per 100 consultations, from 19.7 in 2000–2001 to 35.2 in 2003– 2004, representing an increase of almost 20% over the recent 4 years of the BEACH program. Among various recognized systemic factors influencing the growth of GP pathology utilization (Guibert et al., 2001); an important one is the lack of assurance in the appropriateness of doctors’ decision making when ordering the pathology services (Vining and Mara, 1998; Van Wal- raven and Naylor, 1998; Lundberg, 1998; Smellie, 2003). Stuart et al. (2002) argue that the wide variation in test 0377-2217/$ - see front matter Ó 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.ejor.2007.11.003 * Corresponding author. E-mail address: leonidc@unimelb.edu.au (L. Churilov). www.elsevier.com/locate/ejor Available online at www.sciencedirect.com European Journal of Operational Research 195 (2009) 662–675