Development road of the Patients Influence, an app that connects patients, influencers and hospitals to make the world a better place.

Description

Patients Influence is a platform that bridges the gap between healthcare companies and patient influencers, offering a trustworthy and effective channel for healthcare marketing. The platform elevates patient voices through comprehensive surveys that utilize machine learning to analyze responses across a range of demographics and needs. These insights empower healthcare companies to tailor their products and services more precisely, while providing patient influencers with opportunities for growth and monetization.
Tech Stack
JavaScript, TypeScript Node.js, Nest.js, Next.js,  PostgreSQL, MaterialUI, Python, Google Bert, OpenAI

 

Client Requirements

The client had a dual need. Firstly, they wanted to create a platform to efficiently connect healthcare companies with patient influencers—patients who are also social media influencers—for authentic, targeted marketing campaigns. To achieve this, the platform would use an AI algorithm to match the ideal patient influencers based on various factors like location, language, and demographics. Secondly, the client sought an advanced, AI-powered survey module to extract in-depth insights directly from these patient influencers. The client approached us with these intricate needs, and not only did we meet those requirements, but we exceeded their expectations by creating a state-of-the-art AI-driven platform. Our solution has the potential to revolutionize pharmaceutical marketing by providing highly targeted patient influencer matching and rich, data-driven insights through our advanced survey capabilities.

Challenges:

01

Predictive Modeling for Campaigns

The intricate task of predicting a campaign’s performance involves handling numerous variables and metrics. This is particularly challenging when it comes to understanding the characteristics of each patient influencer’s audience. Our developers solved this by employing machine learning algorithms that use semi-supervised learning, combining smaller labeled datasets with larger unlabeled sets for more accurate predictions.
02

Backend and Front-End Coordination

Creating a seamless experience between complex backend algorithms and an intuitive front-end interface is a notable challenge. This is especially true when the software needs to be both highly functional and user-friendly. Our developers tackled this by using a microservices architecture for the backend and implementing responsive design for the front end, successfully achieving balance between complexity and usability.

Main Features:

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