An Intelligent Clinical Decision Support System for Assessing the Needs of a Long-Term Care Plan

With the global aging population, providing effective long-term care has been promoted and emphasized for reducing the hospitalizations of the elderly and the care burden to hospitals and governments. Under the scheme of Long-term Care Project 2.0 (LTCP 2.0), initiated in Taiwan, two types of long-term care services, i.e., institutional care and home care, are provided for the elderly with chronic diseases and disabilities, according to their personality, living environment and health situation. Due to the increasing emphasis on the quality of life in recent years, the elderly expect long-term care service providers (LCSP) to provide the best quality of care (QoC). Such healthcare must be safe, effective, timely, efficiently, diversified and up-to-date. Instead of supporting basic activities in daily living, LCSPs have changed their goals to formulate elderly-centered care plans in an accurate, time-efficient and cost-effective manner. In order to ensure the quality of the care services, an intelligent clinical decision support system (ICDSS) is proposed for care managers to improve their efficiency and effectiveness in assessing the long-term care needs of the elderly. In the ICDSS, artificial intelligence (AI) techniques are adopted to distinguish and formulate personalized long-term care plans by retrieving relevant knowledge from past similar records

CWDM: A Case-based Diabetes Management Web System

Managing diabetes using intelligent techniques is a recent priority for healthcare information systems and the medical domain. Diabetes is one of the most widespread diseases around the world including Australia. Numerous intelligent systems supporting diabetes management (DM) have been widely deployed, yet how to effectively develop a DM system integrating intelligent techniques remains a big issue. Case-based reasoning (CBR), as an intelligent technique, has been applied in various fields including customer services, medical diagnosis, and clinical treatment. This paper proposes a case-based lifecycle for DM consisting of case-based symptoms, case-based diagnosis, case-based prognosis, case-based treatment, and case-based care. The lifecycle is integrated with a web-based system in which CBR functions as an intelligent intermediary. The approach proposed in this research might facilitate research and development of diabetes management, healthcare information systems and intelligent systems

Exploring Best Practices to Utilize Business Intelligence Systems

Organizational leaders who can manage business intelligence system (BIS) resources may achieve sustainable success in economic, political, and corporate environments. The review of professional literature indicated that effective resource management in a BIS environment requires the establishment of best practice. The purpose of this qualitative, single-case study was to explore best practices among 9 BIS practitioners for effective resource management. Participation criteria included the active engagement in BIS professional disciplines and the willingness to share their perspectives. The conceptual framework for this study was the cognitive experiential self-theory (CEST). Five leaders and 4 data analysts at an eastern U.S. county government agency were interviewed. Using computer based qualitative data analysis software to assist with the coding process, interview transcripts and the published directives of government agency leaders were reviewed to identify themes and achieve triangulation. Five themes emerged: the need for comprehensive policies and procedures for creating operating standards, updated data acquisition training, human capital dynamics management for improved efficiency, protocols for transforming raw information into knowledge, and safeguards for preventing bias in data analysis. Findings derived from this study could contribute to global social change as BIS leaders use best practices to improve resource and data management proficiencies for rapidly transforming information into knowledge for developing policies, services, and regulations that affect public safety, fiscal planning, and social risk management