Showing papers by "Paul Jen-Hwa Hu published in 2000"

Investigation of factors affecting healthcare organization's adoption of telemedicine technology

Abstract: Recent advances in information and biomedicine technology have significantly increased the technical feasibility, clinical viability and economic affordability of telemedicine-assisted service collaboration and delivery. The ultimate success of telemedicine in an adopting organization requires the organization's proper addressing both technological and managerial challenges. Based on Tornatzky and Fleischer's framework, we developed and empirically evaluated a research model for healthcare organizations' adoption of telemedicine technology, using a survey study that involved public healthcare organizations in Hong Kong. Results of our exploratory study suggested that the research model exhibited reasonable significance and classification accuracy and that collective attitude of medical staff and perceived service risks were the two most significant factors in organizational adoption of telemedicine technology. Furthermore, several implications for telemedicine management emerged from our study and are discussed as well.

Automated learning of patient image retrieval knowledge: neural networks versus inductive decision trees

Abstract: Retrieving a patient's prior examination images that are relevant to the current ones is a critical component in radiologists' primary examination reading services. The important role of such image retrieval support will be greatly accentuated in digital radiology practice. Radiologists' knowledge of patient prior image retrievals is rooted in their interpretation and application of the pertinent underlying medical/radiological knowledge as well as in their clinical training and experiences. At the same time, this knowledge may vary with individual practice preferences and styles, and may dynamically evolve over time. The complexity and dynamics suggest that patient image retrievals are a promising area for artificial intelligence-based automated learning techniques. Automated learning of patient image retrieval knowledge can provide continuous knowledge repository update support in an image retrieval knowledge-based system. However, the implementation of the learning techniques needs to address several challenges that include missing and noisy data, as well as multiple decision outcomes. Two techniques based on salient automated learning paradigms, neural network and symbolic learning, are investigated. Specifically, we describe the design or extension of each learning technique to address the unique characteristics of patient image retrieval knowledge and compare the resulting learning performances. The results show that the knowledge derived from the automated learning methods can achieve effective image retrievals that are comparable to those based on a knowledge-engineer-driven approach.

Introduction to the minitrack: databases, data warehousing, and data mining in health care

Abstract: The nature of healthcare services is essentially information-based and can be greatly improved with effective information support, including data modeling, archive, retrieval and analysis. In this light, database and data warehousing/mining technologies are crucial to healthcare organizations' services as well as individual professionals' practices. The contemporary Information Age can be characterized by rapid information expansion/creation, which has initiated and propelled an increasing shift to a knowledge-based society where information organization/retrieval and knowledge generation/discovery become increasingly challenging. In response, we organized the first Databases, Data Warehousing and Data Mining in Health Care Minitrack, which intended to serve as a presentation and discussion vehicle for sharing interesting recent research work among researchers and practitioners from both information systems and health care communities.