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PL
W artykule znalazły się refleksje dotyczące zasadności monitorowania procesów logistycznych szpitala w procesie doskonalenia jakości usług medycznych. Wśród przykładowych kryteriów oceny jakości świadczenia usług medycznych wymieniono następujące: zawodowe (lekarzy, pielęgniarek), dostępności usług medycznych, obsługi, komunikatywności oraz prestiżu placówki medycznej. Autorka podaje przykłady wskaźników, które mogą być wykorzystane w procesie monitorowania jakości szpitalnych procesów logistycznych. Efektem realizowanych badań naukowych jest prezentacja przykładowej karty kontrolnej x – R dla średniego procentowego wskaźnika wykorzystania łóżek szpitalnych w Specjalistycznym Szpitalu X (w latach 2008-2013).
EN
Reflections on the appropriateness of hospital logistics process monitoring in the process of improving the quality of medical services are located in this article. Among the examples of criteria for assessing the quality of the provision of medical services listed: professional criteria (doctors, nurses), availability of health care services, communication skills, and the prestige of a medical facility. Author gives examples of indicators that can be used to monitor the quality of hospital logistics processes. Result of carried out research is the presentation of x - R sample chart for the average percentage of hospital beds utilization rate in the Specialist Hospital X (2008-2013).
EN
The control chart is a tool of statistical quality control, which is widely used in factories. The fulfillment of its basic assumptions ensures faultless assessing the monitored process. Infringements the assumptions of classical control charts can cause false signals in the case of a regulated process, either lack of signal or the signal delayed in time, when process is out-of-control. Incorrect assessment of the accuracy of the manufacturing process is of course the economic impact. In this paper, based on actual data an attempt to determine control limits for the manufacturing process of the distribution of the controlled characteristics, which is significantly different from a normal distribution, was taken. The result of this work is the method of determining the control limits based on the quantile of a random variable estimated by the kernel estimation. The article pays attention to the economic consequences of infringements the assumptions of classical control charts.
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