Knowledge Management Handbook
Mediface: Anticipative Data Entry Interface for General Practitioners
OZCHI '98 Proceedings of the Australasian Conference on Computer Human Interaction
Bayesian networks for knowledge discovery in large datasets: basics for nurse researchers
Journal of Biomedical Informatics - Special issue: Building nursing knowledge through infomatics: from concept representation to data mining
Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis
Expert Systems with Applications: An International Journal
Building a case-based diet recommendation system without a knowledge engineer
Artificial Intelligence in Medicine
A formal framework of knowledge to support rational psychoactive drug selection
Artificial Intelligence in Medicine
A multi-resolution agent for service-oriented situations in ubiquitous domains
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Knowledge sharing is crucial for better patient care in the healthcare industry, but it is challenging for physicians to exchange their clinical insights and practice experiences, particularly with regard to the issuing of prescriptions for medicine. The aim of our study is to facilitate knowledge sharing and information exchange in this area by means of a knowledge-based system. We propose a knowledge-based system, CASESIAN, to automatically model each physician's prescription experience. This is done by collecting as many as possible instances of when the physician has issued a prescription. These occasions will be analyzed from a statistical perspective to form a reciprocal interactive knowledge sharing process for the issuing of medical prescriptions which we will call the prescription process. With the help of the prescription data in medical organizations, the knowledge-based system employs the Bayesian Theorem to correlate the experience of peers in order to evaluate individual prescription knowledge as retrieved through the case-based reasoning technique. In addition, a system prototype was implemented in a Hong Kong medical organization to evaluate the feasibility of such an approach. Our evaluation indicates that there is a significant improvement in knowledge sharing after the adoption of the system. CASESIAN obtains a higher rating in both recall and precision measurement when compared to traditional knowledge-based system. In particular, its information retrieval is much stronger than the baseline in around 40%. Furthermore, regarding the result of the interviews, physicians agree that the system can improve the storing and sharing of medical prescription knowledge.