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Data Analytics System for Clinics
Client: Сhain of veterinary clinics
QA
Expertise:
Tech Stack:
DevOps
UX/UI
PM
AI/ML
Frontend
Jupiter
Apache HBase
Apache Hadoop
Java
MySQL
MapR
UI (Angular)
RabbitMQ
Apache Spark
RStudio
AWS Zeppelin
Business Analyst
Backend
The client is a chain of veterinary clinics aiming to improve operational efficiency, enhance patient care, and optimize resource allocation through advanced data analytics.
About the Client:
Our team developed a scalable, cost-effective, and flexible data analytics system to store, process, and analyze patient medical records. The system supports comprehensive data collection, including visits, purchases, medications, test results, and other clinical records. It uses machine learning to enable faster analysis, forecasting, and decision-making for healthcare planning and management.
About the Project

Challenges & Objectives

Challenges:
  1. Data Overload: Managing large volumes of structured and unstructured medical data.
  2. Forecasting Needs: Accurately predicting disease trends and treatment outcomes.
  3. Access Control: Ensuring secure, role-based access to sensitive medical records.
Objectives:
  1. Build a system capable of handling diverse medical records and processing large datasets efficiently.
  2. Implement advanced analytics to forecast disease trends and assess treatment efficacy.
  3. Provide intuitive data visualization tools for clinics to make informed decisions.
Implementation
01
Data Storage and Processing:
  • Deployed a hybrid data architecture using Apache Hadoop and HBase for distributed storage.
  • Integrated RabbitMQ for real-time data ingestion from clinic systems.
02
Machine Learning Integration:
  • Developed ML models to analyze clinical data and forecast disease trends.
  • Used RStudio and AWS Zeppelin for model training and performance optimization.
03
Data Visualization:
  • Built dynamic dashboards with Angular for displaying graphs, charts, and trends.
  • Provided clinics with actionable insights through interactive visualizations.
04
User Management System:
  • Implemented role-based access controls to ensure secure information sharing among staff.
05
Scalability and Cost-Effectiveness:
  • Leveraged AWS and Apache Spark for scalable, high-performance data processing.

Outcomes & Business Impact

  1. Streamlined Data Management: Clinics now store and classify medical records with ease.
  2. Predictive Insights: ML-driven forecasts help clinics prepare for seasonal disease trends and resource demands.
  3. Improved Decision-Making: Intuitive visualizations enable better planning for treatments and operational workflows.
  4. Enhanced Security: Role-based access ensures compliance with data privacy regulations.
Business Impact:
  1. +30% Efficiency Improvement: Automated data processing reduced manual workload.
  2. +25% Forecast Accuracy: ML models delivered precise disease trend predictions.
  3. +20% Revenue Growth: Optimized resource allocation increased patient throughput.
  4. Improved Patient Care: Enhanced data analysis led to better treatment planning and outcomes.
  • +30% Faster Data Analysis: Enabled clinics to make quicker, data-driven decisions.
  • +25% Disease Forecast Accuracy: Supported better preparation for resource and treatment allocation.
  • +20% Increase in Efficiency: Reduced administrative overhead and manual processing.
  • Scalable Infrastructure: Ensured long-term cost savings with cloud-based deployment.
Results & ROI
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