Emergency ambulance booking system
ABSTARCT :
We have developed an application that will give the patient emergency medical attention because ,in India, deaths occur every second of the day. This project's primary goal is to close the time gap between the patient's request and the ambulance's arrival.
An essential component of emergency medical care is ambulances. Patients typically only have a limited number of ambulance connections, which makes things tough for them to call in an emergency.
It is suggested that the application used for this project would allow the patient to schedule transportation to the hospital. The patient can use the application to find oneself or to upload both their starting and final locations.
After that, the system would display the closest ambulances that are available, and the patient may select the best one by comparing the prices and travel times of each journey throughout an area. Billing is the last step. The project also aims to provide hospitals with blood inventory delivery services.
On the other side, the patient's reservation would prompt the ambulance driver. The application will direct the ambulance driver to the destination; the driver must verify the reservation. The administrator would have access to all central data and be in charge of calling and inquiry functions.
The suggested system, an android application that helps the user find local hospital and ambulance services aims to guarantee responsiveness, efficacy, and simplicity.
The user will gain from booking the ambulance so that the ailing person can be transported to the pharmacy promptly and potentially spare his life.
Using their location, the patient can follow the ambulance. People will gain from this effort, as patients and the infrequent few catastrophes on the street.
EXISTING SYSTEM :
The existing studies in EMS management generally evaluate the proposed dispatch policies through a simulation in which many simplifying modelling choices and assumptions are made. For example, Lee [7] simulated a hypothetical square grid of 25 vertices with a fixed driving time for all edges.
He did not distinguish between urgency levels and assumed a general distribution for transfer times and a static number of ambulances. Jagtenberg et al. [4] simulated the actual EMS region of Utrecht, but assumed a static relocation policy, static request arrivals, and static ambulance capacity, treatment and transfer times.
The existing, limited number of studies applying machine learning to model expert decisions generally seems to have the captured expert knowledge as the ultimate goal of their efforts, mostly to automate decision making. Maghrebi et al.
[9] conducted a feasibility study of automating the process of determining the order of concrete deliveries. They employ machine learning to match expert decisions with the objective of decreasing dependency on human resources.
DISADVANTAGE :
System Downtime: If the software or platform faces technical issues, such as server crashes or connectivity problems, it can delay ambulance dispatch, potentially compromising patient care.
Limited Access: Not all individuals have access to smartphones or the internet, especially in rural or underdeveloped areas. This creates a barrier for some people to book ambulances.
Personal Information Risks: Emergency booking systems collect sensitive personal and medical information, which, if not properly secured, could be vulnerable to data breaches or misuse.
Location Issues: GPS systems used to track ambulances or identify patient locations can sometimes provide inaccurate results, especially in remote areas, leading to delays or misdirected services.
Limited Ambulance Availability: In high-demand areas or during peak hours, the system might not have enough ambulances to meet the urgent needs, leading to delays in dispatch.
PROPOSED SYSTEM :
We used Dijkstra's algorithm and the current First Come First Serve scheduling technique to ensure the system operated favourably and met user needs. This selection, which includes the pseudo code, provides the means and the opportunity to manipulate rare reservations over many crises in which different ambulances are needed yet scarcely available.
In addition to the First Come First Serve algorithm, the idea of LILO or FIFO queues—which state that the first person to book is the first person to be served—can help alleviate the long tail of patients. The patient's coordinates, route, distance, and cost are not taken into account in this method.
This approach's rebuttals to long term, diversified hold times have an additional impact on the rising cost. In contrast, Dijkstra's algorithm determines the shortest path between the user-selected source and destination; in the even to traffic, a different, longer route is determined.
ADVANTAGE :
Quicker Dispatch: Ambulances can be dispatched faster because the system uses real-time data to identify the nearest available ambulance, reducing response times.
24/7 Availability: The system operates round the clock, ensuring that emergency services can be accessed at any time of day or night, which is crucial for critical incidents.
Online Booking: Users can easily book ambulances via a website, mobile app, or even through a call center, making the process convenient for people in distress.
GPS Integration: The system uses GPS to track the patient’s location in real time, ensuring ambulances are directed accurately, even in unfamiliar or hard-to-find locations. This helps avoid delays caused by miscommunication about the patient's address.
Efficient Fleet Management: The system helps ambulance operators manage their fleet more effectively by tracking the location and status of each vehicle. This reduces idle time and optimizes the allocation of resources.
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