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.
- INTRODUCTION
Nowadays, the role of demand forecasting of medical assets has increased significantly due to the various innovative and effective concepts of forecasting science and inventory management which helps greatly to keep the hospital operations cost under control [1]. Managing the inventory levels is important to the operations and the management of the hospital’s assets. Hospital operations have to take a look at their in-patient flow to make decisions on their resource capacity. In-patient care is one of the main drivers of demand for resources in hospitals [2]. In-patient systems have very complex throughput systems that make the medical inventory planning much more complicated. Factors of the in-patient flow process such as non-stationary arrival and varying medical service processes make current static forecasting models rather obsolete [3] as they do not capture the compound behavior of the true inpatient system. The mismanagement of resources has considerably more impact on the lives and well-being of the patients being served. Forecasting plays a critical role in the medical inventory management. However the challenge that most hospital management faces is the lack of visibility and integration of already present data; data that is routinely collected but stored in differing information systems into useful demand forecasting that can help improve the medical inventory management [4]. Current medical inventory management systems can be categorized into four main conceptual components which are physical infrastructure, inventory planning and control, information system as well as organizational embedding [5]. However, due to a huge amount of medical items and human-intensive working processes, current systems cannot provide a timely and accurate inventory management and forecasting. To improve the situation, the future of inventory management is to build up an automated work flow system that requires minimal manual interaction. This represents a state where the medical amenities replenishment requirements are aggregated and an order is placed automatically. The usage data is also recorded to allow for the hospital management to use to predict for the future demand
- NEURAL NETWORK FOR INVENTORY MANAGEMENT
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.
