Project Overview: Medical Analytics System
The Deloitte medical analytics system project was initiated under a Labo contract in February 2024 and completed in September 2024 by JVB. Deloitte required a specialized system to evaluate drug effectiveness and compare treatment methods by analyzing clinical trial data sourced from academic papers and medical literature. The platform was designed to support medical research and analytical teams by transforming complex scientific information into structured, comparable insights.
With JVB’s expertise in data analytics, visualization, and cloud-based system development, the project delivered a scalable analytical solution that enabled evidence-based evaluation of treatments, reduced manual research effort, and improved consistency in clinical analysis workflows.
Client Background & Business
Challenges of Medical Analytics
Before working with JVB, Deloitte faced challenges analyzing and validating large volumes of clinical research data. Information related to drug effectiveness and treatment outcomes was dispersed across numerous publications, requiring extensive manual review. This approach made systematic comparison difficult and slowed down research and evaluation processes.
- Client: Deloitte – Medical Analytics System
- Industry: Healthcare & Life Sciences
The company’s goal was to transition
to a centralized digital ecosystem that could:
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Aggregate and structure clinical trial data from multiple sources.
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Enable consistent comparison of treatment methodologies.
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Support analytical workflows for medical research teams.
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Improve evidence-based decision making.
Our Solution: Building a Unified Analytics Platform
JVB designed and developed a custom medical analytics platform deployed on AWS Cloud, enabling Deloitte to process, visualize, and compare clinical trial data within a single system. The solution emphasized analytical clarity, scalability, and usability — allowing both technical and non-technical stakeholders to explore complex medical datasets efficiently.
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01
Interactive analytical dashboards built with Python Dash and Plotly
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02
Centralized processing of clinical trial and literature-based data
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03
Cloud-based development and deployment environment on AWS
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04
Secure access controls for research and analysis teams
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05
Scalable architecture supporting growing volumes of medical data
By combining modern visualization frameworks with cloud infrastructure, the platform adapted seamlessly to evolving research requirements.
Key Features
of the Medical Analytics System
Clinical trial data visualization
Drug effectiveness comparison tools
Treatment methodology analysis
Interactive research dashboards
Secure access and data management
Scalable cloud-based architecture
These features enabled Deloitte’s teams to convert complex clinical information into actionable insights with improved accuracy and efficiency.
Our Role and Contributions
in the Medical Analytics Project
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Designing the medical analytics system architecture
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Developing interactive dashboards using Python Dash and Plotly
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Setting up cloud-based development environments on AWS Cloud9
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Deploying and maintaining scalable infrastructure on AWS
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Providing ongoing technical support and system enhancements
JVB’s dedicated team of five engineers worked under a Labo contract model, providing continuous development and close collaboration throughout the project period.
Impact & Results
The implementation of the medical analytics system delivered clear improvements across research and analysis activities:
Before
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Operational efficiency:
Clinical data analysis relied on manual literature review, resulting in slow evaluation cycles. -
Research capability:
Comparing treatment effectiveness across studies was inconsistent and time-consuming. -
Insight generation:
Limited visualization tools reduced the ability to identify patterns across datasets. -
Scalability:
Increasing volumes of medical publications strained existing analytical processes.
After
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Operational efficiency:
Structured dashboards streamlined workflows and reduced manual research effort. -
Research capability:
Treatment methods could be compared consistently using standardized data views. -
Insight generation:
Interactive visualizations improved interpretation of clinical evidence. -
Scalability:
Cloud-based infrastructure supported expanding datasets and future research needs.
By consolidating clinical literature into a unified analytics platform, Deloitte gained clearer visibility into drug effectiveness and treatment comparisons.
Technologies We Used
The platform leverages Python-based visualization frameworks and AWS cloud services to deliver scalable analytics and secure system operations.
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Python
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Dash
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Plotly
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AWS Cloud
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AWS Cloud9
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