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Virtual care platform for insurance service patients

Meedy is a comprehensive healthcare platform designed to connect medical entities, insurance providers, doctors, and patients. Its advanced functionalities empower insurance service patients to remotely oversee the experience of all parties involved in the healthcare processes.
Virtual care




Virtual care, Telemedicine, EHR




Project Based


Web app

Team Size

6 Developers, QA, UI/UX, PM


6 months


10 years (ongoing)
Project description

Meedy is a comprehensive healthcare platform designed to connect medical entities, insurance providers, doctors, and patients. This platform is built upon a complex set of algorithms, with four separate interfaces catering to the distinct user groups:

Meedy is a platform empowering insurance companies to seamlessly integrate healthcare benefits into their insurance services. The platform offers their clients access to several hundred rehabilitation and physical therapy centers, diagnostic networks, as well as thousands of doctors.

The application developed by Apzumi boasts an extensive database of physicians, each with unique specializations, providing an unparalleled advantage to users. With Meedy, patients can easily schedule appointments and medical tests, access medical records, collect and deliver test results and receive personalized treatment recommendations. Doctors can view patient histories, communicate with colleagues, and collaborate on treatment plans.

Project results
About the problem

Meedy revolutionizes patient access to medical services across 10,000 locations throughout Poland. Under the umbrella of private insurance providers, patients unlock the convenience of accessing individual doctors' calendars and effortlessly scheduling appointments or arranging tests. This seamless experience extends to online management from any device, enhancing the accessibility of medical care.

Beyond facilitating appointments, Meedy's robust features empower patients to:

  • schedule visits at diverse locations across Poland
  • share test results with their healthcare providers
  • review details of both upcoming and past consultations
  • integrate calendar and get appointment notifications
  • check the availability of treatment in the National Health Fund
Project scope
Step 1
Document Digitalization

The first step involved converting scanned documents into digital formats using OCR technology. This phase was crucial due to the diverse nature and quality of the scanned documents. Advanced OCR solutions were employed, capable of handling various text formats, handwriting, and even low-quality scans, ensuring high accuracy in digitization.

Step 2
Document Categorization

Once digitized, the documents were categorized into predefined classes such as medical reports, lab tests, and billing documents. This categorization was facilitated by a machine learning model trained on a large dataset of annotated healthcare documents. The model was fine-tuned to recognize and categorize documents accurately, even when the formats and templates varied significantly.

Step 3
Key Facts Retrieval

The extraction of key facts from the categorized documents was the next critical step. Using natural language processing (NLP) and machine learning algorithms, the system identified and extracted pertinent information such as patient names, birthdates, addresses, ICD codes, and details of medical procedures. The AI model was trained to understand the context and semantics of the healthcare domain, ensuring a high level of precision in fact retrieval.

Step 4
Medical Summary and Report Generation

The final step involved synthesizing the extracted information into coherent medical summaries and reports. Generative AI models, trained on a vast corpus of medical texts, were employed to generate summaries that were both accurate and easily comprehensible. These summaries provided a consolidated view of the patient's medical history and current claims, significantly aiding in the decision-making process.

Key features
Appointment scheduling
Laboratory test scheduling
Video consultations
Periodic examination calendar
Drug database
live chat
with doctor
Project timeline
Project tech stack


Frontend Development


Backend Development

Mongo DB



Payments integration
Project tool stack

Jira Cloud

Project Management

Confluence Cloud

Project Documentation

Atlassian Cloud

Project Management


Project Communication


Project Documentation


UI/UX Design


Video Calls Integration

Google Maps



E-mails Integration
Technical description

The backend platform harnesses the robustness of Java technology, ensuring stability and reliability. Meanwhile, the frontend, accessible via the web and crafted with Angular, offers users a seamless and intuitive experience. Moreover, the platform seamlessly integrates with the PayU payment system, empowering patients with convenient payment options for accessing essential services.