Knowledge acquisition, synthesis, and validation: a model for decision support systems
Article first published online: 14 JUN 2004
Journal of Advanced Nursing
Volume 47, Issue 2, pages 134–142, July 2004
How to Cite
O'Neill, E. S., Dluhy, N. M., Fortier, P. J. and Michel, H. E. (2004), Knowledge acquisition, synthesis, and validation: a model for decision support systems. Journal of Advanced Nursing, 47: 134–142. doi: 10.1111/j.1365-2648.2004.03072.x
- Issue published online: 14 JUN 2004
- Article first published online: 14 JUN 2004
- Submitted for publication 1 May 2003 Accepted for publication 1 December 2003
- clinical decision support systems;
- evidence-based practice;
- point-of care system;
- knowledge development;
- practice maps;
- evidence evaluation;
Background. Decision tools such as clinical decision support systems must be built on a solid foundation of nursing knowledge. However, current methods to determine the best evidence do not include a broad range of knowledge sources. As clinical decision support systems will be designed to assist nurses when making critical decisions, methods need to be devised to glean the best possible knowledge.
Aims. This paper presents a comprehensive knowledge development process to develop a nursing clinical decision support system.
Discussion. The Nurse Computer Decision Support Project (N-CODES) is developing a prototype for a prospective decision support system. The prototype is being constructed on rules and cases generated by the best available evidence. To accommodate the range of decisions made in practice, different types of evidence are necessary. The process incorporates procedures to uncover, evaluate, and assimilate information to develop the knowledge domain for a clinical decision support systems. Both formal and practice-based knowledge are included. The model contains several innovative approaches including the use of clinical experts and a network of practicing clinicians.
Conclusion. These strategies will assist scientists and practitioners interested in determining the best evidence to support clinical decision support systems.