運用決策樹演算法於護理人員離職預測－－以某公立醫院為例

Applying Decision Tree to Predict Nursing Turnover --A Case Study in a Public Hospital

近年來，個人的生活品質提升，一般民眾對於醫療品質的要求也就愈來愈高，也希望就醫時能夠得到妥善的治療。整體醫療品質的高低與否，會間接影響到病人是否會相信醫療專業技能及後續的醫療過程。這都和護理人員對病人的服務態度、耐心、細心等工作表現，有很大的關係。而護理人員的離職將直接影響整體醫療品質，部分就業人員在投入職場後，因現實工作環境和理想落差過大，造成長期以來心理和生理無法調適，造成離職率持續增加。如何早期發現離職傾向進而改善，除了減少人事及教育訓練費用，也能促進醫療品質提升與病患滿意度。本研究以決策樹演算法探討護理人員之基本資料，尋找出其離職傾向之間的關聯進行分析。以北部某公立醫院護理人員為研究對象，樣本收集期間至2011年七月止共2351筆資料。配合各種參數組合進行實驗，結果顯示決策樹準確度可達78.81％。透過各項決策樹規則及改善建議，對於預防優良員工離職，及未來甄選新進人員時應有所幫助，故研究結果可以做為醫院管理階層之參考。

With enhancement of living quality, general public has pursued better patient-centered service when seeing a doctor. Besides professional medical treatments, the overall healthcare quality receives more attention these years. While nursing staff plays a critical role during the whole medical process, its caring attitude, patience, and attentiveness greatly influence the patient's satisfaction, leading to mutual trust between the medical staff and the patients. High turnover rate of the nursing staff, ranging from 15% to 40% each year, however jeopardizes consistency of its services. Many factors can raise such instability, such as long and tiring working hours, potential life-threatening injuries at work place, lack or insufficiency of colleague supports, mental and physical maladjustment towards realistic work environment, especially for the newcomers, etc. Early detection of the potential nursing staff turnover will thus not only reduce the recruiting and training costs, but also help render assistance to the nursing staff and promote the patient's satisfaction as well as the healthcare quality. This study employs a decision tree algorithm, with respect to the basic personnel information, to correlate tendency of nursing staff's departure. A public hospital in northern Taiwan participated as the research subject: 2351 personnel samples were collected as they were in July 2011. With parameter optimization and rule-reasoning association, the proposed decision tree algorithm exhibits an accuracy rate of 78.81% of nursing staff turnover prediction, and offers recommendations of alleviating potential departures. The results of the study can serve as a great reference for the hospital administration and could be applicable to the other medical institutions.