데이터 마이닝을 활용한 병원 재방문도 영향요인 분석: 외래환자의 만족도를 중심으로 = On the Determination of Outpatient`s Revisit using Data Mining

다국어 초록 (Multilingual Abstract)

Patient revisit to used hospital is a key factor in determining a health care organization`s competitive advantage and survival. This article examines the relationship between customer`s satisfaction and his/her revisit associated with three different methods which are the Chi Square Automatic Interaction Detection(CHAID) for segmenting the outpatient group, logistic regression and neural networks for addressing the outpatient`s revisit. The main findings indicate that the important factors on outpatient`s revisit are physician`s kindness, nurse`s skill, overall level of satisfaction, hospital reputation, recommendation, level of diagnoses and outpatient`s age. Among these ones, physician`s kindness is the most important factor as guidelines for decision of their revisit. The decision maker of hospital should select the strategy containing the variable amount of the level of revisit and size of outpatient`s group under the constraint on the hospital`s time, budget and manpower given. Finally, this study shows that neural networks, as non-parametric technique, appear to more correctly predict revisit than does logistic regression as a parametric estimation technique.