護理人員由於長期面臨薪資低、挫折感重、工作壓力大、升遷機會少和不被重視等問題,使得離職率居高不下引起護理人力之短缺,進而讓留任護理人員的負荷更高,在此增強環路不斷運作之下,逐漸形成現在「護士荒」的窘境。雖然醫院管理階層嘗試運用各種不同措施以改善護理人員的高離職率,而使情況曾經稍有緩解,但是經過一段時間後,問題卻又再度浮現。因此,護理人力問題應屬於一「動態性複雜」之問題,倘若仍以傳統線性思考方式來解決,往往可能會產生越治越糟的局面。 故本研究使用系統動力學為研究分析之工具,以T醫院為個案,進行醫院內護理人力之模擬模式的建構。在經過模式測試之後,再針對政府單位與醫院管理階層可能採行之政策進行模擬,並進一步將模擬效果較佳之政策進行若干情境模擬,以期待找出最適合之高槓桿解政策。 由本研究模擬之結果可知:(1)在政府方面,改善病護基準的政策長期之下無法有效降低護理人員之負荷;在醫院方面,進行提高聘用比例、延長留任時間之政策,在長期下亦無法降低護理人員負荷。此結果符合了系統動力學所強調之「反直覺」現象。(2)醫院改善試用期護理人員離職率之效果,遠大於改善資淺與資深護理人員離職率者;此外,醫院改為只聘用有臨床經驗之護理人員,則在面對更加艱困之情境下,亦能予以克服。此結果顯示這兩項政策可能為本模擬模式之「高槓桿解」。 本個案醫院之研究,或許可以看做是整個醫療環境下的其中一個「縮影」。故期待本研究以系統動力學建立之模式模擬,可以提供給其他醫療機構做為護理人力政策運作時的參考。
For quite some time, nurses in Taiwan suffered from the situations as low wages, heavy frustrations, high work pressure, few opportunities for advancement, being disrespected, and etc. Those situations result in high turnover rate and further cause shortage of nursing manpower. Thereby, the workload of nurses remaining in hospital get further increased. In such circumstance of reinforcing loop, the dilemma of "nursing shortage horror" develops gradually. Although hospital administrators try a variety of measures to ease those problems, those problems still resurface over again after some time. The nursing manpower issues should belong to the problem of "dynamic complexity", and it might become worse and worse while we treat it with traditional linear way of thinking. In this study, we introduce T hospital as a case, use system dynamics as an analysis tool, and build up the simulation model for nursing manpower in T hospital. After model verification, those policies probably applied by the government and hospital administrators are put into simulation. Furthermore, we choose the policies with better effects for following scenario simulation and look forward to find the most suitable solution of high leverage. Simulation results from the present study show as following: (1) For the government, the policy to improve patient-nurse ratio would not effectively reduce workload of nurses in a long run; for hospital administrators, the policies to increase recruitment ratio or to extend contract period of nurses also would not effectively reduce workload in a long run. These results are compatible with the "counter-intuitive" phenomenon which system dynamics emphasize. (2) The effect of improving turnover rate of nurses in probation period would be much better than that of junior and senior nursing staff. Besides, if the hospital change to hire nurses with clinical experience only, it is possible for the hospital to survive more difficult situations. The results indicate that these two policies may be the "high leverage solutions" in this model. This case study of T hospital may be seen as one "epitome" within the entire healthcare environment. Hence, we expect the simulation model built with system dynamics in this study could be provided to other healthcare institutions as a reference when operating nursing manpower policy.
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