Hospital Management System

Hospital Management System

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. https://codeshoppy.com/php-projects-titles-topics.html 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

CASE STUDY AND DISCUSSION

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.