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.
I.
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.https://codeshoppy.com/shop/product/
II.
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.
III.
AN RFID BASED INVENTORY MANAGEMENT
SYSTEM
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
IV.
CASE STUDY
ANDDISCUSSION
The proposed framework was trial
implemented in a Tan Tock Seng Hospital (TTSH). It is the second largest
hospital in Singapore and one of the nation’s biggest multi-disciplinary
hospitals with more than 160 years of pioneering medical care and development.
In 2012, TTSH had 36 clinical and allied health departments with 15 specialist
centers and powered by more than 6000 healthcare staff. Tan Tock Seng Hospital
currently held 827 infusion pumps that were manually tracked for usage and preventive
maintenance by the healthcare workers in the wards. Due to the management of
huge amount of infusion pumps, several problems regarding infusion pump are
triggered and shown in A, B and C in section IV respectively. Table I shows the
current time-line of the work flow process when shortage of infusion pumps.
A.Manual Search for the Pumps for
Patient Usage
Healthcare workers had to perform a
manual search within the ward for any available infusion pumps and then checked
with other wards manually if there were no available infusion pumps within
their wards. This caused an increase in the waiting time of the patients for
the infusion pumps.
B.Manual Administrative and Paperwork
Due to a lack of a visibility over the
infusion pumps a lot of administrative time was spent on locating the pumps for
periodic maintenance, stock-taking or to find a ‘lost’ pump. When a certain
ward had a shortage of a pump and there arose a need for loan or swapping,
healthcare workers had to spend time doing manual searches for the pump. When
they found the pump, they had to spend time doing manual paperwork and
handover. This decreased the direct patient care time.
C.Fluctuation in Infusion Pump Supply
Pumps were periodically taken away for
periodic maintenance with having replacement units. This batch maintenance
approach created periodic variations to the supply of in-service pumps at the
different wards as a shortage of pumps was triggered during the periodic
maintenance wards. In this study, the RFID-IMS was developed to improve the
situation. The RFID-IMS leverages the RFID technology based on the hospital’s
existing wireless ‘N’ access points to track real-time location. It is able to
track the location of tagged assets or individuals in real-time. It also allows
for the provision of real time information system that offers more visibility
on the infusion pump location and the utilization rate. In order to evaluate
the forecasting performance of the RFID-IMS, the error difference between the
forecasted values and the actual values for both the time-series forecasting
and the nodal forecasting methodologies were measured and compared. The error
analysis algorithms such as mean squared error (MSE), mean absolute deviation
(MAD) and mean absolute percentage error (MAPE) were used for evaluation. Table
II shows the evaluation of ward pump utilization data of Ward 3A and 3B. It
shows that neural network model used in RFID-IMS holds a key edge over the
rest.
Three potential benefits of using the
RFID-IMS regarding workers’ productivity, patient safety and maintenance
planning are shown as below.
1)The RFID-IMS should save time and
allow healthcare workers to channel more of their time for more direct patient
care by reducing the time spent on search for infusion pumps, periodic maintenance,
stock-take or locating of the ‘lost’ pump and handling the paperwork when there
is a pump loan/swap.
2)Automation of the current inventory
management system should prevent and reduce the risks to improve the patient
safety. The RFID-IMS ensures that there is an undisrupted infusion pump service
available for the patients and also provide timely detection of defective
infusion pumps during pump operations for enhanced patient safety. It collects
the utilization and movement data of the infusion pumps and allows for the most
efficient allocating of the infusion pumps giving patients with a greater need
for the pump to be given a greater priority.
3)The RFID-IMS should provide better
information to allow a maintenance schedule that is based on the pump
utilization and availability, rather than on a batch basis. This allows a more
proactive way to ensure that pump functionality and availability are still
feasible during maintenance where there would be a shortage of infusion pumps.
V.
CONCLUSION
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
