A Hybrid Clustering Technique to Improve Patient Data Quality

The demographic and clinical description of each patient is recorded in the databases of various hospital information systems. The errors in patient data are: wrong data entry, absence of information provided by the patient, improper identity of the patients (in case of tourists in Hong Kong), etc. These data errors will lead to a phenomenon that records of the same patient will be shown as records of different patients. In order to solve this problem, we use “clustering,” a data mining technique, to group “similar” patients together. We used three algorithms: hierarchical clustering, partitioned clustering, and hybrid algorithm combining these two, and applied on the patient data using a C program. We used six attributes of patient data: Sex, DOB, Name, Marital status, District, and Telephone number as the basis for computing similarity, with some weights to the attributes. We found that the Hybrid algorithm gave more accurate grouping compared to the other algorithms, had smaller mean square error, and executed faster. Due to the privacy ordinance, the true data of patients is not shown, but only simulated data is used.

Distributed Data Warehouse for Geo-spatial Services

This chapter introduces the research issues on spatial decision-making in the context of distributed geo-spatial data warehouse. Spatial decision-making in a distributed environment involves access to data and models from heterogeneous sources and composing disparate services into a meaningful integration. The chapter reviews system integration and interoperability issues of spatial data and models in a distributed computing environment. We present a prototype system to illustrate the collaborative access to data and as a model for supporting spatial decision-making.

Challenging the Unpredictable

This chapter deals with the changeability of order management systems (OMS). OMS are here referred to complex commercial off-the-shelf software (CCOTS) used, for example, for enterprise resource planning (ERP). Due to turbulent conditions in the business environment, a permanent need for change is the defining challenge for enterprises. However, far too often the rigidity of today’s CCOTS-OMS does not allow users to implement the intended changes in the business organization. In order to deal with this challenge, a cybernetic model of order management is presented in this chapter. Additionally, a decision oriented software engineering and architectural design for CCOTS-OMS is sketched. The authors are convinced that these approaches enhance the changeability of the development and operation of CCOTS-OMS as well as their co-operation with a business organization.

Healthcare Information

This chapter defines and discusses healthcare data and various healthcare databases as resources for knowledge discovery that can support effectiveness research, quality improvement, and resource allocation. Privacy and confidentiality of health records are addressed along with the dimensions and complexity of information retrieval from healthcare databases and patient health records. The Veterans’ Health Administration (VHA) data and databases are specifically addressed. Issues and methods of data preparation for a data mining exploration of a VHA Spinal Cord Injury (SCI) clinical database are presented from a nursing perspective. The potential of using healthcare databases for research is noted.

Data Mining for Business Process Reengineering

Transitioning from a mining to meaning perspective in organization data mining can be a crucial step in the successful application of data mining technologies. The purpose of this paper is to examine more fully the implications of that shift. The use of data mining technology was part of our cycle time study of the Poplar County Criminal Justice System (a fictitious name). In this paper we will report on the use of data mining in the Poplar County Criminal Justice System (PCCJS) study in an attempt to speed up their case handling processes. Marketing and finance researchers are more involved with “simple” (i.e., direct) relationships, whereas BPR researchers are more concerned with long chains of interacting processes. This difference appears in the tools these researchers use: marketing and finance researchers are more interested in set-theoretic problems, BPR researchers, in graph-theoretic problems. Yet data mining technologies incorporate graph-theoretic algorithms. Consequently, they should be able to support hypothesis generation in BPR activities. We were able to come up with relevant and meaningful hypotheses for BPR in the PCCJS system by using data mining technology, specifically sequential pattern analysis: “Which areas we should look into in order to speed up the case handling process?” This valuable outcome would have not been possible without data mining technology, considering the large volume of data on hand. It is hoped that this study will contribute to broadening the scope of applicability of data mining technology.

Context Management of ERP Processes in Virtual Communities

A methodology is proposed for sharing the contextual knowledge/resources that flow within ERP processes in virtual communities. Context is represented by a set of relevant collaborative semantic concepts or “objects.” These are the objects that are localised/contextualised to specific sub-process within the ERP process. Two sets of objects are identified: (i) objects that make up a community member’s actual contextual knowledge/resources with regards to the ERP process, and (ii) objects that make up the required contextual knowledge that various sub-processes/tasks expect from the member to possess for successful execution of those tasks. The excess of the objects in (ii) compared to those in (i) are identified and are put within the focus of the community member in order to enable the member to effectively participate in various collaborative interactions within the community’s ERP process(es).

The Second Wave ERP Market

This chapter presents the market penetration of SAP systems in the Australian market together with an analysis of three mini-case study implementations. The implementations showcase a global rollout, a global consolidation and a “greenfields” small to medium implementation, and present the diverse range of implementations that are occurring in the Australian ERP marketplace. The global ERP industry blossomed in the 1990s, automating back office operations and, in the new century, moves have been made to introduce a “second and third wave” of functionality in ERP systems. Research up-to-date has been limited, especially in the relation to market penetration, of these new “second wave” products in the Australian region. The trend in 2000/01 was for upgrades and restructure in preparation for the move to e-commerce. In 2002, there has been an expanded focus on mysap.com, small to medium enterprises and the expansion into “third wave” products. This chapter looks at the market movement and demographics of companies that have implemented SAP software, the dominant ERP vendor within the Australian marketplace, and will focus on the trends that are impacting the Australian ERP market.

Intrinsic and Contextual Data Quality

This chapter introduces the concepts of intrinsic and contextual data quality and presents research results on how individual perceptions of data quality are impacted by media (World Wide Web versus print) and personal involvement with the topic. The author advances four hypotheses, which are tested with a randomized experiment (n=127), dealing with information on cancer. First, subjects perceive reputable information sources as having higher data quality than non-reputable sources. Second, subjects perceive web-based material to be more timely, but less believable and of lower reputation, accuracy and objectivity than printed material. Third, individuals with greater personal involvement will be better discriminators of data quality in viewing reputable and non-reputable cancer information. Fourth, women are better discriminators of data quality in viewing reputable and non-reputable information than men. The first hypothesis was supported and limited support was provided for the second hypothesis.

The Effects of an Enterprise Resource Planning (ERP) Implementation on Job Characteristics

As organizations undertake the deployment of integrated ERP systems, concerns are growing about its impact on people occupying jobs and roles in those organizations. The authors set out to assess the impact of ERP implementation on job characteristics. Using the Hackman and Oldham Job Characteristics Model as a basis, the study assesses how ERP affected work redesign and job satisfaction of people working in several Canadian federal government organizations.

ERP Systems Impact on Organizations

Enterprise Resource Planning (ERP) systems are in most cases implemented to improve organizational effectiveness. Current research makes it difficult to conclude how organizations may be affected by implementing ERP systems. This chapter addresses this issue by presenting an artifact evaluation of ERP systems. The evaluation is based on the Competing Values Model (Quinn & Rohrbaugh, 1981; Rohrbaugh, 1981). The evaluation shows that ERP systems support effectiveness criteria, related to internal and rational goals of organizations. The evaluation also points out weaknesses in ERP systems, especially in areas related to human resource management and organizational flexibility. The result of the evaluation is used to discuss the impact of ERP systems on organizations and is presented as a series of hypotheses.