Overview

E-Mareez is a healthcare platform which aims at enhancing the relationship between the patient and the doctor through the use of telemedicine services. This technology enhances systems to offer excellent centered patient care and apologize for cost inadequate economical results.

Client:

E-Mareez – A healthcare service providing company that aims at bringing drastic transformation through application of technological innovations in the delivery of patient’s care.

Project Focus:

This was an ambitious project that was meant to promote a secure, reliable and effective telemedicine system that in real time could capture data that would help improve patient health while at the same time improving the usability of the platform.

Requirements:

1. Real-Time Communication:

E-Mareez aimed to have a Web Real Time Communication that will link the patients and the healthcare workers to avail medical consultation through streaming audio and videos most shortly and securely possible.

2. Data Security and Compliance

Since the data involves patients effective measures needed to be put in place to align the platform to healthcare compliance while at the same time ensuring that data was accurate and secure.

3. Scalability and Performance:

And as the number of users increased it was very important for the system to expand without any loss in performance or functionality.

4. Predictive Analytics with Machine Learning:

Therefore, the identified knowledge areas are able to reveal the key points that describe the current state of the enterprise. The client was seeking to use big data for machine learning analytics for improvement of the care of patients and medical advices in future using previous data.

Our Solution:

1. Technology Integration:

– For the front end of the real-time telemedicine platform, we utilized the React JS, so as to make it more interactive and simple to use.
– Node. js was used behind the scenes while managing multiple request responses on a single tab, thus enhancing speed.
– The application was hosted on AWS, and the scalability and the highest availability were some of the mechanism used in the cloud security.
-WebSockets were implemented to cover the need to have real time communication between patients and doctors in case of video conferencing or using chat.

2. Data Storage and Management:

– Structured patient data were safely and credibly stored by using **PostgreSQL**, thus meeting the requirements for the secure and compliant management of healthcare records.
– Use of encryption and secure access to the computerized patient data was put in place to enhance security of the patients data.

Machine Learning for Predictive Care:

: Machine Learning for Predictive Care:
By including some artificial intelligence systems, it became possible to use the machine learning algorithm to diagnose and probably devise treatment plans from trends of results observed in patient details.
These models were used for identifying expected health threats and providing individual specific guidance in terms of treatment.
The other was the use of Git for version control to encourage team work among the developers and have a well organized code that is easily trackable on GitHub.
Next. js was used for the rendering of the server-side which enhances the site’s SEO and TTI for the users.

Marketing Strategy

Introduction/Background:

The environment within the healthcare industry is gradually moving toward the online space for better and easier reach. Thus, with the increasing need for a patient-centered telemedicine solution, E-Mareez wanted to seize this opportunity to become the next big development in patient care.

Research & Analysis:

After evaluating the current state solutions that are present in the telemedicine domain, we analyzed the opportunities for improving user experience, data processing, and system scalability. We also created a patient’s needs-oriented platform based on their behavior.

Strategy Development

The strategy mainly encompassed designing a serviceable architecture that focused on user experience, Realtime BPM, and data security with the incorporation of web socket and machine learning for Realtime and Predictive health care delivery.

Implementation

React JS and Node. This paper thus addresses the following questions; The first question is to identify the frameworks used in the construction of js that would support the interaction of multiple users and consequently, make the platform easily scalable;
AWS was selected due to cloud computing solution providing competitive uptime.
Real-time video stream and chat was integrated by using Web Socket that provides good user experience.
Well, PostgreSQL provided high-level security to the patient’s data to be stored was sensitive and complied with regulatory requirements.

Results and Measurement:

It also involved more patient interactions due to better organisational structure of the consultations on the platform.
Predictive care analytics enhanced diagnosis precision, and consequently, raise the possibility and quality of positive patients’ results.
This factor made E-Mareez easily accommodate more and more users while experiencing few problems.

Challenges and Adjustments:

With expectation of more users the system has to be scalable and thus needed further improvements in terms of performance.
Adapting to making use of the models in real-time-data monitoring of patients’ care was challenging since it involved incorporating a machine learning model into the existing patient-care monitoring system.

Conclusion

The development of the telemedicine platform with E-Mareez allowed for the design of a highly reactive and easily scalable solution that was supported by modern technology as well as real time communication. The solution also improved the patient care and also used machine learning to predict and prevent the diseases. The successful implementation of this project testifies to the firm’s ability to create customised digital solutions in highly specialised sectors such as healthcare.

Tools & Technologies:

PostgreSQL – Keeping your data safe and sound.
React JS – Open source javascript library for developing frontend for graphics user interface.
Node. js – The technology to deal with simultaneous requests in the backend.
Next. js – Optimisation and initial rendering on the server – better loading speed and improved SEO.
AWS – Solutions for Elastic compute Cloud, cloud web-scales services and cloud security.
GitHub – Systems for coordinating the management of codes.
WebSockets – The multimedia, real time transport facile for video conference and for chat.
Machine Learning – If patient’s data will be used in predictive models for disease diagnosis & treatment.