Health Monitoring with a Real-Time Metrics Collection Service

The platform is an innovative health monitoring service that collects real-time health metrics from wearable devices, analyzes the data and alerts users to potential health issues. By enabling early detection of pathologies, the platform helps users receive timely medical assistance and supports preventive care.

Health
Metrics Collection
Python
API
Telehealth
Back-end development
Web
Django
Analysis

Customer

industry
Healthcare
region
USA
client since
2020

Our customer is a US-based healthcare organization dedicated to enhancing patients lives through advanced health monitoring solutions.

They strive to provide proactive, data-driven healthcare services that enable early detection and intervention, improving overall health and well-being.

Detailed information about the client cannot be disclosed under the provisions of the NDA.

Challenge

The major project objective was to create a secure, efficient, and robust back-end system, capable of collecting, processing, and analyzing real-time health data.

It was also necessary to create a convenient dashboard for doctors, where they can view patient’s metrics and predict the possible development of diseases.

Solution

Our team developed an application from scratch.

Recognizing the unique needs of the project Pynest decided to build a microservices-oriented back-end application, splitting the system into four distinct services, each handling a specific functionality.

Device service
The first part of the solution was the implementation of device service that took on the crucial task of seamless data transmission from various wearable devices to the system.
It securely handled encrypted data streams, ensuring device authentication, error detection, and appropriate handling, thereby maintaining the integrity and confidentiality of the health data.
Data Processing and Analysis Service
The second part was Data Processing and Analysis Service. This service was responsible for the real-time processing and in-depth analysis of incoming health metrics. It examined the collected data, applied machine learning algorithms to identify patterns, and flagged any potential health risks based on the predefined parameters.
Data security was a paramount concern, given the sensitivity of health information. We implemented robust encryption measures to secure data during transit and at rest. Access controls were enforced to ensure data integrity and confidentiality. Our system was designed to adhere to the stringent standards of health data regulations like HIPAA.
Notification Service
The third part of the solution was the Notification Service. Once potential health risks were identified, the notification service was triggered. It rapidly sent alerts to the concerned parties, informing both the users and their respective healthcare professionals. This prompt communication allowed for timely intervention, preventing the exacerbation of potential health issues.
Dashboard service
The last part of the solution was Dashboard service. This service was devoted to delivering the dashboard functionality to doctors and healthcare professionals.
It consolidated and displayed the collected health metrics in a user-friendly manner, facilitating the medical staff to efficiently monitor patients’ health status and interpret the received data.
This feature enables healthcare professionals to predict possible disease development and take proactive steps in patient care.

Results

The customer successfully launched a cutting-edge health metrics monitoring service that has already made a significant impact on patients’ lives. The platform’s early detection capabilities have led to several patients being diagnosed with pre-infarction conditions and receiving necessary medical assistance. The customer’s innovative approach to healthcare has attracted new сlients, improved patient outcomes, and reinforced their commitment to data-driven, proactive care.dfgdfd

95%

clinics onboard within 6 months

19%

dollars raised

Team

4

Backend engineers

1

Frontend engineers

1

DevOps

1

Team lead

Technology stack

backend

Python
Django
Django REST Framework
Celery
Kafka
Pandas

DevOps

Docker
Docker-compose
Kubernetes

Platforms

AWS