Unifying the fragmented models of information systems implementation
Critical issues in information systems research
Airline reservations systems: lessons from history
MIS Quarterly
Information systems implementation: testing a structural model
Information systems implementation: testing a structural model
Supporting the information technology champion
MIS Quarterly - Special issue on the strategic use of information systems
Implementing systems across boundaries: dynamics of information technology and integration
Implementing systems across boundaries: dynamics of information technology and integration
Data mining: a hands-on approach for business professionals
Data mining: a hands-on approach for business professionals
Discovering data mining: from concept to implementation
Discovering data mining: from concept to implementation
Lessons learned from three interorganizational health care information systems
Information and Management
Exploring data mining implementation
Communications of the ACM
Visual exploration of large data sets
Communications of the ACM
Information Networks for Community Health
Information Networks for Community Health
Evaluating Health Care Information Systems: Methods and Applications
Evaluating Health Care Information Systems: Methods and Applications
Computer Methods and Programs in Biomedicine
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Recent attention has turned to the healthcare industry and its use of voluntary community health information network (CHIN) models for e-health and care delivery. This chapter suggests that competition, economic dimensions, political issues, and a group of enablers are the primary determinants of implementation success. Most critical to these implementations is the issue of data management and utilization. Thus, health care organizations are finding value as well as strategic applications to mining patient data, in general, and community data, in particular. While significant gains can be obtained and have been noted at the organizational level of analysis, much attention has been given to the individual, where the focal points have centered on privacy and security of patient data. While the privacy debate is a salient issue, data mining (DM) offers broader community-based gains that enable and improve healthcare forecasting, analyses, and visualization.