The use of fuzzy sets techniques in managing health organizations.
During the management process in health organizations, certain situations can arise when data necessary for decision-making is in fuzzy form. In such cases, problems may be solved using fuzzy sets techniques just the way they are used in diagnostics, laboratory investigations, and other patient care processes. Sometimes there are certain situations when a problem could be solved by traditional methods (i.e., mathematical programming); however, resolution requires great efforts in data acquisition and complicated calculations. At the same time, it cannot provide 100% accuracy because of rapid changes to what can be considered as the norm. In such cases, the use of fuzzy sets techniques may also be justified. The aim of this investigation is to prove that results obtained using fuzzy sets techniques will not be much less exact then those with the use of traditional methods. As an example, the problem of resource allocation among consulting rooms in the outpatient division of one hospital in Tbilisi was chosen. Our aim was to minimize patients' queues as well as physicians' idle time. Although similar problems have been solved by operation research specialists since 1956, as a rule, they have required intensive information acquisition about real volume loads of departments and hard calculations. Instead, one could have obtained the same information (viz., which of the departments of a health institution was "overloaded", "normally loaded," or "underloaded") relying on personnel estimates. Using such approximations and fuzzy sets techniques, one can avoid tiresome data acquisition and complicated calculations. Moreover, in solving resource redistribution problems, one can take into account not only the irregularity of patient distribution among consulting rooms, but also other factors (e.g., the availability of certain specialists, etc.). In order to compare both methods, data about real consulting times of outpatients in the hospital was acquired. A model for minimizing the total time wasted by patients in queues was constructed and the problem was solved using an integer programming method. Additionally the same consulting rooms were grouped into following categories: 1) "very underloaded"; 2)"underloaded"; 3)"normally loaded"; 4)"overloaded"; and 5)"very overloaded." The proposition about the redistribution of their opening times was formulated using fuzzy sets techniques. The comparison of results gained by both methods has shown that the use of fuzzy sets techniques is quite permissible and sometimes more reasonable for solving certain health care management problems.
Kachukhashvili
G S
GS
Research Institute of Informatics and Complex Automation in Health, Tbilisi, Republic of Georgia.
Tsiskarishvili
N E
NE
Dubovik
M V
MV
Badiashvili
G V
GV
D001071:Appointments and Schedules; D003659:Decision Making, Organizational; D003661:Decision Support Techniques; D017143:Fuzzy Logic; D005846:Georgia (Republic); D015276:Health Care Rationing / Q000458:organization & administration