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Effective Asset Management For Hospitals with RFID

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. Code Shoppy 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


With the improvements of statistical models and forecasting techniques, the complex throughput can be studied and an ideal inventory which can process the data inputs to come up with an efficient state of inventory management can be modeled. Current time-series methodology attempts to first identity forecasting parameters such as trend cycle, seasonality and irregularity and then extrapolates these components to come up with the forecasts. However these trend-cycle and seasonal data components of a time forecast tends to evolve over time and needs to be continuously revised for higher accuracy in forecasting. In addition, a key assumption to the time-series forecasting model is that the activities responsible in influencing the past will continue to influence the future. This is often a valid assumption whilst forecasting for a short-term demand, but falls short when attempting to forecast for long-term analysis [6]. A neural network forecast is proposed to handle the deficiency. It uses analytical methodologies that make use of the historic demand data as inputs and updates information over time as the number of training data sets provided is increased [7]. The adaptive and learning abilities of this neural network improves the forecasting accuracy so that better decisions can be made. The key to achieve accurate demand forecasting is to have good pattern recognition. Back propagation algorithm of NN is a typical supervised learning algorithm, where the neural network is trained by setting the input vectors and the corresponding target vectors. After the neural network is changed, approximate function is used to recognize a pattern. Levenberg – Marquardt, which is the one of the most effective algorithm for function approximation problems, will be studied in this research. The advantage of Levenberg-Marrquart algorithm can approach second-order training speed without computing the Hessian matrix, which is the square matrix of second-order partial derivatives of errors with respect to weights.


An RFID based inventory management system (RFID-IMS) integrates RFID tracking services and the neural network model to assist in the tracking of the medical devices such as infusion pumps throughout the hospital and allows the storage of the real time data. Greater visibility on the infusion pump movement and the demand characteristics will allow the operations department to come up with more effective supply chain solutions to manage their infusion pumps. Data on the actual movement and usage of the infusion pumps are captured using the RFID technology and is feedback to the neural network platform for aggregated analysis of the inventory of the pumps. The proposed workflow has been modified to suit the infusion pump inventory management in TTSH from retail industry [7]. The detailed framework is shown in Fig. 1. Firstly, the RFID-IMS uses RFID technology to capture of the usage data within a certain periods and this information is then used as input for the neural networking model to calculate the demand forecast. Then, neural network analysis is conducted to analyze the demand pattern and to predict the systematic and random component. Neural network forecasting is an enhancement of the time series and the casual forecasting templates. In this study, neural network toolbox from Matlab is used.

Neural network forecasting requires accurate analysis of smoothening parameters such as level, trend and seasonality which may not be acquired immediately without the RFID tracking and this will affect the neural network forecasting accuracy. Next, the forecasted values are then feed into the RFID-IMS to construct virtual aggregation of demand. It uses these forecasted values to aggregate demand for the individual wards. This simulation of the infusion pumps at each ward allows for better streamlining of process and improves on the current manual system. With the RFID tracking and neural network forecasting, RFID-IMS allows auto generation of the number of sets of the pumps to be issued from the central equipment base to the wards. This eliminates the need for end users to raise a request for the number of sets of infusion pumps to be issued and also physically count the number of sets returned. The workflow process is optimized with the automation of the infusion pump inventory. Healthcare workers now have more time to focus on patient care as there is no longer a need for physical stock-taking or to raise a request to receive the set. With the neural network forecasting showing high levels of accuracy in predicting the futuristic demand patterns, the wards would have the ideal number of infusion pumps that they require hence reducing the need to borrow the infusion pumps. This saves time for the healthcare workers who can concentrate their effects in taking care of the patients


The RFID-IMS for the inventory management of infusion pumps based on the RFID tracking system and the neural network was successfully deployed at TTSH. The selection of neural network and the tracking of the movement and the usage patterns of the infusion pump in the proposed medical inventory system are integrated into a process flow framework. This framework helps in the elimination of wastage in terms of manpower and administrative time and promotes lean and efficient inventory management in healthcare industry. The proposed integrated solution that combines both RFID tracking and neural network analysis provides TTSH a basic data flow framework that can be used as a blueprint for TTSH’s proposed Information Technology Unit (ITU) Management System with respect to their inventory management of their infusion pumps. However, all forecast based on the key assumption that for every five patients there is a demand for one pump as there is a limited knowledge of the actual number of pumps per patients. Also, the values for the smoothening parameter were based on trial and error and this compromises the accuracy of the forecasts. Hospital is one of the human-intensive working environments in healthcare industry. Most of the tasks are carried by healthcare workers manually. In future, studies regarding process resign and reengineering can be conducted to improve the productivity of the inventory management and reduce the operation cost. Also, medical assets managed in current study can be further expanded to a larger group of products with the use of RFID technology. A comprehensive inventory tracking and forecasting can be established to provide better medical services to patients

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