Healthcare is a universal need, catering to the full social spectrum of a nation. However, the Government has limited total resources and cannot meet every need of the population spectrum. Decision-making healthcare planners and deliverers must therefore make effective use of what is available. The ever-rising cost of healthcare is a critical issue for policy makers, healthcare providers, healthcare insurance companies as well as the patient population. For instance, there is a current trend of overburdening of specialist outpatient clinics in public hospitals by people who could well be treated for most of their illnesses in primary-care clinics. Further, the continued prevalence of persistent diseases and the emanation of new exotic strains are also taxing the healthcare system and its costs.
Ultimately, the goal of healthcare service administrators is to help maintain and enhance the health of the public. While individual citizens may hold primary responsibility for their health status, there is much that the health service administrators (working in concert with physicians, nurses, other health professionals and community leaders) can do to assist in the process. In this regard, attention could be focused on incorporation and the strategic use of new medical technologies, to make patient-treatment clinical-support units (of pathology, biochemistry, microbiology, radiology, pharmacology) as well as procurement and inventory systems and administrative services function must efficiently.
This delineation and the solution of this concept is what healthcare administration is all about. Simulation modelling can play a significant role in making health facilities more efficient in serving the users and in enabling the most effective deployment of resources. This framework is designed specially for primary care services, where each patient’s condition (for example, customer demand) is different and unique. Primary care services vary in diversity, seriousness, referrals and even include preventive care. Traffic engineering models could well be applied to primary care services, to determine the best modality of customer satisfaction and resource utilisation. Clinical services are all about decision making. In this regard, we could refer to the application of the decision support system, introduced by Sundararajan et al. (1998), to make operational decisions in a food-processing industry for determining the optimum production based on the tradeoffs between some decision factors.
The fuzzy set theory could also be applicable for decision making under fuzzy factors and uncertainties. The fuzzy optimal problems with multiple constraints need to be transformed into an ordinary constrained-optimum problem, so that they can be solved with the mathematical programming method. (Biswal, 1992; Dutta, Tiwari and Rao, 1992) Fuzzy set theory does not need quantitative description about each criterion from engineering designers. It allows engineering designers to describe the performance of each criterion with some linguistic terms, such as ‘good’, ‘poor’, ‘very good’ and so on.
Nevertheless, fuzzy programming can be adopted in engineering product design (Khong and Oh, 2002) to even incorporate the created value of the intellectual capital. In medical services, Yuan et al. (2001) have developed a pilot fuzzy logic expert system for kidney patients, to assist physicians in solving multi-criteria kidney allocation problem. It was evaluated in comparison with two existing allocation algorithms: a priority sorting system, used by the Multiple Organ Retrieval and Exchange programme in Ontario (Canada), and a point-scoring system used by the United Network for Organ Sharing in the USA.
In their expert system (internet-based fuzzy logic), information technology was used to coordinate the organ procurement and transplanting process as well as to allocate donated organs to recipients quickly, fairly and effectively. Even computer simulation of the human thinking and reasoning process by Artificial Intelligence (AI) can have a major role in the development of decision support system (Andriole and Hopple, 1986). AI has been broadly recognised to empower a decision support system to incorporate and simulate human thinking process, to improve the performance of the decision support system and bring the intent of decision-makers and the requirements of customers into the decision outcome (Angehrn and Jelassi, 1994; Finlay and Maritn, 1989; Holsapple and Whinston, 1996).
Finally, info-communication technology can also provide a means for cost saving without compromising healthcare service quality. In fact, conceptual framework of the virtual diagnosis clinic can be built, using the system-engineering approach to model and test the feasibility of the virtual diagnostic clinic.
source : 9Int. J. Healthcare Technology and Management, Vol. 7, No. 5, 2006
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