PubMed · 8460309
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M Swindlehurst. Project management.. https://pubmed.ncbi.nlm.nih.gov/8460309/
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Mathematical modeling and its potential applications in pharmacy are discussed. A model is a simplified representation of the real world. As an experimental approach, modeling minimizes expense, risk, and disruption, but its validity can be hard to ascertain. Mathematical models describe numerically the relationships among elements of a system and are a powerful tool in making decisions affecting that system. There are two types of mathematical models: analytical models, which directly describe the relationships between system inputs and outputs using mathematical equations (such as pharmacokinetic models), and simulation models, which involve the replication, usually with a computer, of events as they occur in the real world. Analytical models are easier to develop but are not appropriate for describing highly complex systems. In continuous-time simulation, the system is represented as an uninterrupted flow of material; in discrete-event simulation, it is assumed that events occur only at distinct times. Various simulation programs are commercially available. The stages of a mathematical modeling study are (1) formulate the problem, (2) determine the model's structure, (3) collect and analyze initial data, (4) develop the model further, (5) validate the model, (6) experiment using the model, and (7) use the results. There have been many applications of modeling in health care, but relatively few have involved the study of pharmacy systems. Mathematical modeling offers pharmacists a low-risk, low-cost tool for aiding decisions about pharmacy systems by predicting alternative futures.
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OBJECTIVES: To analyse the structure of the new model of primary care (NMPC) in the Community of Valencia, and to identify the strategic importance of its characteristic variables and the possibilities of intervention to affect these variables. DESIGN: A qualitative study through a method of structural analysis (crossed impact method-multiplication applied to a classification) of the relationships between 37 variables characterising the NMPC which were identified by prior qualitative research, with interpretation of the results using the Téniere-Buchot Model. SETTING: Community of Valencia. RESULTS: The structural variables identified were those relating to the political-legal framework and to the allocation of primary care resources; and the resultant variables, those relating to efficiency and primary care quality. Between these two categories, the intervention variables covered management, NMPC professionals, health needs and the community's use of services. CONCLUSIONS: The structural analysis gives the legal-political and economical framework a determining role in NMPC, which can hardly be influenced from within the system. Management and organisation are identified as key variables from which an intervention can be made in the short or medium term to achieve the aims of the system.