data mining: opportunities and challenges
Chapter XV - Data Mining in Health Care Applications
Data Mining: Opportunities and Challenges
by John Wang (ed) 
Idea Group Publishing 2003
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During the late 1990s and again more recently, CHINs were seen to offer the health care industry a platform to address and mine medical data to support public and rural health issues. CHINs have been defined as: "Interorganizational systems (IOSs) using information technology(ies) and telecommunications to store, transmit, and transform clinical and financial information. This information can be shared among cooperative and competitive participants, such as payors, hospitals, alternative delivery systems, clinics, physicians, and home health agencies" (Brennan, Schneider & Tornquist, 1996; Payton & Ginzberg, 2001). IOSs have emerged as a primary business model in a number of industries given the application of wireless, application-integration, Internet, and broadband technologies. Starting with reservation systems in the airline industry (e.g., the SABRE system), IOSs have been used to implement strategic and competitive advantage in the cotton, hospital supply, consumer goods retailing, and automotive industries, among others (Clemons & Row, 1993; Copeland & McKenney, 1988). Given their intended direction for extensive mining of medical data among health care providers, payors, employers, and research institutions, CHINs offer an immediate platform for patient profiling, fraud detection, profitability analysis, and retention management.

Based on a voluntary model of organizational participation, CHINs enable member organizations to share health services data in order to meet common objective(s), ranging from profit maximization to improvement of public health conditions and wellness. Further, CHINs can provide a myriad of services from electronic transaction processing to telephone-based referral information (Brennan et al., 1996). The deployment of CHINs is confronted with a tremendous number of implementation barriers, largely behavioral and political in nature. Among the numerous deterrents, as found in Payton and Ginzberg (2001), were loss of organizational autonomy and control; lack of vendor and patient support; and lack of understanding of customers' (patients) needs. Critical to e-health models, however, are the data quality issues that enable health care providers, researchers, payors, and consumers to make more informed medical decisions.

Cooper and Zmud (1990) adopted a diffusion process model of IT implementation that is based on stages and processes as outlined in Table 1. Cooper and Zmud suggest that the process model can be used as a framework for understanding how critical implementation factors evolve over time.

Table 1: IT Implementation process model from Cooper and Zmud (1990)




Active and/or passive scanning of organizational problems/opportunities and IT solutions are undertaken. Pressure to change evolves from either organizational need (pull), technological innovation (push), or both.


Rational and political negotiations ensue to get organizational backing for implementation of IT applications.


The IT application is developed, installed, and maintained. Organizational procedures are revised and developed. Organizational members are trained both in the new procedures and in the IT application.


Organizational members are induced to commit to IT application usage.


Usage of the IT application is encouraged as a normal activity.


Increased organizational effectiveness is obtained by using the IT application in a more comprehensive and integrated manner to support higher level aspects of organizational work.

Cooper and Zmud (1990) identify five broad contextual factors that might impact the implementation process: user, organization, task, technology, and environment. Their description of the implementation process, however, suggests an alternative clustering of contextual variables. For instance, they state that, in the initiation stage, "pressure to change evolves from either organizational need (pull) technological innovation (push) or both" (p. 124). In the adoption stage, they describe the processes of "rational and political" negotiations, and in subsequent stages, they suggest that "technology" impacts the system implementation. Following this, three factor classes are defined: push/pull factors, behavioral factors and shared systems topologies. That is, these sets of factors continue to be important as organizations are pressured to change, examine opportunities for IOS solutions, obtain organizational backing, develop the IOS applications, and continue to engage in cooperative IOS arrangements.

My proposed CHIN implementation model is shown in Figure 1. This implementation model is important to academicians and practitioners, alike as it holds implications for data utilization, management, and mining.

Push or pull factors are contextual elements that can impact and influence an organization's willingness to participate in IOS initiatives and include perceived competitive advantage (Grover, 1993), competition (Johnston & Vitale, 1988; Copeland & McKenney, 1988; Grover, 1993), government actions and policies (Linder, 1992; Anderson, Aydin & Jay, 1994), and perceived economic benefits (Moritz, 1986).

Among the Push/Pull Factors, I expected both the economic dimensions and government policies to have a positive impact on the implementation effort. Health care organizations are currently being pressed toward greater cooperation by government decisions, policies, and practices (e.g., Medicare, Medicaid, Joint Commission on Accreditation of Healthcare Organizations, prospective payment system). Further, the need to reduce costs while maintaining or increasing quality is a key objective of numerous managed care models (Ferrat, Lederer, Hall, & Krella, 1996; Grover, 1993; Kongstvedt, 1989; Little, 1994). Competition among institutions was expected to play a minor role, given the rise of community models of health care delivery (Brennan et al., 1996; Payton, Brennan, & Ginzberg, 1995), as health care players learn to cooperate and collaborate (Ferret et al., 1996) in an effort to overcome the limitations associated with previously noted economic dimensions.

Behavioral factors relate to attributes and actions of key system stakeholders. These include customer support (in this study, the customer is defined as the patient); end-user support (Anderson et al., 1994); organizational autonomy and control (Moritz, 1986); physician, application vendor, and top management support (Anderson et al., 1994; Grover, 1993; Lucas, Ginzberg, & Schultz, 1988). In the case of CHINs, application vendor support is vital for organizations to gain access to the needed products (Kim & Michelman, 1990). Another critical behavioral factor is the political dynamics of the implementation process, which often impacts system endorsement and resource commitment (Aydin & Rice, 1992; Kimberly & Evanisko, 1981).

Of the Behavioral Factors, quality of CHIN management, vendor support, patient support, physician support, and end-user support are all expected to have positive impact on implementation progress. Each of these factors has been shown to foster change in various intra- and inter-organizational domains (Anderson et al., 1994; Clemons & Row, 1993; Grover, 1993; Lucas et al., 1990; Whang, 1992), and the same result should obtain in the CHIN context. Strong autonomy and control of member organizations, however, will tend to inhibit successful CHIN implementation, as organizations struggle with the tradeoffs of losing some autonomy to the benefits of shared information (Brockhoff & Teichert, 1995). Political factors, arising from conflicting personal and organizational objectives among stakeholders, will tend to impede implementation progress (Beath, 1991; Linder, 1992).

Shared or integrated systems topologies represent certain aspects of the infrastructure needed for a CHIN. These factors include arrangements for cooperation and information sharing, as well as for assuring information quality (Clemons & Row, 1993; Kim & Michelman, 1990; Mason, 1991). These cooperative arrangements for CHINs may involve physicians, hospitals, third-party payors, laboratories, and pharmacies and will require an increased degree of electronic information sharing with anticipated improved information quality (Ernst & Young, 1994; Little, 1994).

Both elements of Shared System Topologies information sharing and information quality were predicted to have favorable impacts on implementation progress. The prior existence of shared systems would provide foundations for building more efficient and effective mechanisms for inter-organizational, community-based health care delivery. As the degree of information sharing among inter-organizational participants increased, the quality of information available was also expected to increase, thereby fostering successful implementation (Clemons & Row, 1993).

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Data Mining(c) Opportunities and Challenges
Data Mining: Opportunities and Challenges
ISBN: 1591400511
EAN: 2147483647
Year: 2003
Pages: 194
Authors: John Wang © 2008-2017.
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