Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81942
Title: Development of a knowledge-based decision support system for long-term geriatric care management
Authors: Tang, Valerie
Advisors: Choy, K. L. (ISE)
Keywords: Decision support systems
Nursing homes
Older people -- Long-term care
Long-term care of the sick
Issue Date: 2020
Publisher: The Hong Kong Polytechnic University
Abstract: Facing the unavoidable phenomenon of the aging population, the demands of long term care services in nursing homes have been raised for serving the elderly. In nursing homes, long-term geriatric care management (LGCM) is classified as a complicated task which involves the activities of health monitoring, health assessment, care plan formulation, implementation and evaluation. It requires the performance of knowledgeable and experienced caregivers so as to maintain the high quality of care. However, one major problem faced by the nursing homes in LGCM is the shortage of healthcare resources including budgets, knowledgeable caregivers and facilities. The implementation of LCGM becomes challenging to achieve the goals of delivering healthcare services for the elderly in an accurate, time-efficient and cost-effective manner. In addition, due to dynamic change in the health deterioration of the elderly, reviewing care plans at regular periods of time may not be appropriate for every elderly. Hence, it may affect the services provided in care planning. Adding this consideration in LGCM, the decision making processes in LGCM becomes more complicated. Therefore, it is crucial to develop a knowledge-based decision support system to provide knowledge support for generating customized long-term care solutions in LGCM so as to enhance the efficiency and improve the quality of care in nursing homes. In this research, an intelligent system, namely the knowledge-based geriatric care system (K-GCS), is developed to generate long-term care solutions with consideration of the dynamic changes in the health condition of the elderly. Three modules are involved in the proposed system: IoT-based data collection module (IDCM), health determination module (HDM), and, care plan formulation module (CPFM). Under the IoT environment, biometric data can be captured and stored in IDCM which allows better data management and integration in nursing homes. In addition, it enables real-time health monitoring for caregivers in order to determine the occurrence of abnormalities. HDM triggers when to activate care plan modifications according to the level of the severity of health deterioration for the elderly. CPFM generates elderly-centred care plans with the specific goals, types of healthcare services and operation guidelines and procedures so as to improve the quality of care offered by caregivers and to satisfy the individual needs. Considering there is rapidly changing healthcare information, technologies and regulations, a novel algorithm, i.e. GA-based document clustering (GADC) algorithm, is embedded in the case adaptation engine of CPFM to improve performance in acquiring useful online knowledge rather than relying on human knowledge for case revision. In order to validate the feasibility of proposed system, two case studies were conducted in a nursing home, namely the Comfort Nursing Home. The aim of the first case study was to investigate whether the LGCM, relying on the knowledge of domain experts in the case adaptation of case-based reasoning (CBR) is sufficient or not for satisfying the needs of the elderly. According to the problems observed and highlighted from the first case study, the K-GCS with the GADC algorithm is implemented in the second case study to improve the performance of the case adaptation of CBR. Through pilot runs of the system in the nursing home, the improvements in the daily health monitoring and formulation of care plans in terms of effectiveness, efficiency and consistency were observed. The major contribution of this research is in the design and implementation of an intelligent system that facilitates appropriate decision making in the formulation of effective care plans so as to address the increasing demands for healthcare services in the healthcare industry through the adoption of emerging smart health concept.
Description: xx, 261 pages : color illustrations
PolyU Library Call No.: [THS] LG51 .H577P ISE 2020 Tang
URI: http://hdl.handle.net/10397/81942
Rights: All rights reserved.
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