血液氣體分析儀設置最適性調配研究－－以某區域教學醫院為例

The Effect Analysis on the Optimal Deployment of Blood Gas Analyzer in a Regional Teaching Hospital

在醫院中，血液氣體分析儀常配置於檢驗科，以單一窗口方式，提供檢驗服務，但醫院常為了解決在短時間內同時有大量檢體送檢，導致檢體品質不佳或報告延遲發出情形，而在其他急、重症或有檢驗需求的單位也配置有血液氣體分析儀就近提供檢驗服務。本研究旨透過多台血液氣體分析儀最佳化設置，以發揮該儀器之最佳效益。本研究以某區域醫院為例，計算跨樓層送檢之血液氣體分析檢驗量，利用各血液氣體分析儀的檢驗量與試劑耗用成本，以及該檢驗項目之收支成本進行交叉分析，再透過類神經網路（Artificial neural network，ANN）探討會影響血液氣體分析儀成本效益的各樣因素，並利用這些因素建立血液氣體分析儀的資源重配置方案，透過資源重配置方案重新分配儀器資源，並於資源重新配置後探討每台血液氣體分析儀的成本效益，以了解是否達到成本效益最佳化。初步研究結果可知透過類神經網路找出影響成本效益的關鍵因素，並利用這些因素為成本效益不佳儀器建立血液氣體分析儀資源重配置方案，以及各項配套措施，在方案執行後，進行成本效益最佳化之評估，可以看出全院整體執行效益由-2.3%大幅提升至191.3%，故證明確實可以達到增進成本效益的目的。 In the hospital, blood gas analyzers are often located in the laboratory department for testing services provided in a one-stop window manner. However, hospitals usually have to solve problems of sending large sample quantities simultaneously in a short period of time, resulting in poor sample quality or postponement of reports. Other emergency departments, critical care departments, or departments with testing needs are also equipped with blood gas analyzers to provide with the nearest testing services. This study aims to obtain the greatest efficiency of blood gas analyzers by optimizing the setups for multiple blood gas analyzers. This study used one regional hospital as example to calculate the amount of samples sent for cross-level blood gas analyzers. The study used the testing quantity and reagent consumption cost of each blood gas analyzer and the revenue-expenditure of this test item for cross analysis. Then, an artificial neural network (ANN) was employed to investigate the various factors that will affect the cost effectiveness of blood gas analysis. We then used these factors to establish a resource reallocation proposal for blood gas analyzers. The blood gas analyzers were redistributed according to this proposal and the cost-effectiveness of each blood gas analyzer was then reanalyzed in order to see if the most optimal cost-effectiveness was achieved. Preliminary results showed that ANN was used to identify the crucial factors affecting cost- effectiveness and these factors were used to establish a resource reallocation proposal and various supporting measures for blood gas analyzers with non-optimal cost effectiveness. After implementation of this proposal, evaluation of cost-effectiveness optimization was carried out. From this, it is shown that the overall implementation efficiency of the hospital was significantly increased from –2.3% to 191.3%, thereby proving that the aim of improving cost-effectiveness can be achieved.

In the hospital, blood gas analyzers are often located in the laboratory department for testing services provided in a one-stop window manner. However, hospitals usually have to solve problems of sending large sample quantities simultaneously in a short period of time, resulting in poor sample quality or postponement of reports. Other emergency departments, critical care departments, or departments with testing needs are also equipped with blood gas analyzers to provide with the nearest testing services. This study aims to obtain the greatest efficiency of blood gas analyzers by optimizing the setups for multiple blood gas analyzers. This study used one regional hospital as example to calculate the amount of samples sent for cross-level blood gas analyzers. The study used the testing quantity and reagent consumption cost of each blood gas analyzer and the revenue-expenditure of this test item for cross analysis. Then, an artificial neural network (ANN) was employed to investigate the various factors that will affect the cost effectiveness of blood gas analysis. We then used these factors to establish a resource reallocation proposal for blood gas analyzers. The blood gas analyzers were redistributed according to this proposal and the cost-effectiveness of each blood gas analyzer was then reanalyzed in order to see if the most optimal cost-effectiveness was achieved. Preliminary results showed that ANN was used to identify the crucial factors affecting cost- effectiveness and these factors were used to establish a resource reallocation proposal and various supporting measures for blood gas analyzers with non-optimal cost effectiveness. After implementation of this proposal, evaluation of cost-effectiveness optimization was carried out. From this, it is shown that the overall implementation efficiency of the hospital was significantly increased from –2.3% to 191.3%, thereby proving that the aim of improving cost-effectiveness can be achieved.