Using Artificial Neural Networks for Predicting Surgery Overtime

• Main Authors: Shi-jr Tzeng, 曾希執
• Other Authors: Jun-der Leu
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/85420285609123888742

碩士 === 國立中央大學 === 企業管理學系 === 101 === Wasting in health care spending has become a common issue in health care systems worldwide. After the implementation of the national health insurance system in 1995, the Bureau of National Insurance’s deficit has also becoming a serious issue in Taiwan. With the implementation of the global budget System the budget of hospitals are limited by expenditure cap which increase the needs of more precise cost control for the managers in hospital. Operating rooms have been estimated to account for more than 40% of a hospital’s total revenues thus the largest cost center in a hospital (Denton. Et al. 2006). Scheduling is an important factors of operating room performance. This paper focus on predicting surgery overtime. The data set used for developing and testing the neural network was collected from a medical center in Taipei. We discovered that Years of service of the surgeon, Years of service of the anesthetist, numbers of the assistant during operation, emergency operation, emergency anesthesia, full body anesthesia, anesthesia, inpatient, ICU, overtime, sex of patients and sections are best fit for developing the neural network. The performance of the network was also evaluated by calculating the area under the Receiver Operating Characteristic (ROC) curve in the test set, 0.637. Compare with Naïve Bayesian Classifier, the Neural network has better validity. We believe the surgery overtime prediction model we developed would serve as a prediction aid for scheduling surgery operations.