Effective asset management for hospitals with RFID

Abstract

The healthcare sector has been confronted with a growing necessity to reduce operational cost. Many hospitals have been focusing their efforts in optimizing their inventory management procedures through the incorporation of technological solutions such as tracking devices and data mining to come up with an ideal inventory model. Demand forecasting is an integral part of inventory management and hospitals are no exception. Time series forecasting methods are widely used in traditional approaches. Limited studies integrated asset tracking technology and neural network analysis to facilitate demand forecast. This paper proves that neural network forecasting has a key edge over traditional time series forecasting methods. It also evaluates the improvements in the efficiency of the inventory management of infusion pumps at Tan Tock Seng Hospital (TTSH) due to the integration of radio frequency identification (RFID) tagging and neural network forecasting to the current work flow process to allow it to capture and manipulate the data relating to the movement and usage of the infusion pumps. Projected ward and the total in-patient usage data were compared using error analysis algorithms such as mean squared error (MSE), mean absolute deviation (MAD) and mean absolute percentage error (MAPE). The potential benefits of the proposed system, contribution of current study and recommendations for future research are also mentioned at the end of this paper.