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Healthcare data management platform

Healthcare solution in the form of a machine learning platform for data processing, storage, and visualization.


Our customer specializes in providing medical software for private healthcare organizations. When he approached us, the customer was looking for a team of specialists to implement his idea with modern technologies, namely machine learning algorithms. 

The main goal of our team was to make a medical care solution that had to optimize processes where most of the time was spent on data processing and diagnostics. So that doctors and health care providers could spend more time exclusively on patient care. Also, algorithms could help identify patterns in the data that may have been missed in human analysis, which could help in the early diagnosis and treatment of disease.

Invatechs already had experience creating medical IT solutions and a team specializing in ML and AI, so this was a great chance for us to implement something new. So we suggested to our customer to implement the platform with machine learning in healthcare so that employees could organize layers of data into usable and informative dashboards, which would help optimize the quality of services provided.

The Invatechs team, as healthcare solutions providers, decided to take the next steps in implementing this platform:

  • Create cross-platform software that could run on both PCs and terminals, and mobile devices;

  • Create a machine learning algorithm and provide a database for it;

  • Integrate cloud storage for secure data storage.


Initially, our specialists had to create a user-friendly application that any employee could understand without extensive training. We created an interface, and filters, where you could easily sort information about each patient for a certain time. Additional features were implemented in the form of diagrams, where it was possible to track the dynamics and status of patients in a particular center or the entire chain. 

Each patient has a profile with basic information about his parameters, contact data, and all doctor visits. Also, implementing algorithms will help doctors quickly analyze and suggest which procedures or visits a patient should make for further treatment.

Before integrating machine learning algorithms, the Invatechs team built medical software from scratch. We also integrated various tools that provide a stable and secure connection to the cloud storage servers to avoid data leaks. We also integrated several solutions against DDoS attacks.

Technologies & tools


We started working on the project in 2018, where we first created a prototype for the client to approve. Invatechs offered to work on an Agile methodology, where our specialists provided progress reports through two weekly sprints. The client gave feedback on the features and could also make adjustments. After the MVP was developed and fully approved, we began writing the code for the platform itself. 

We used Jira to set tasks for specialists and maintain documentation on the project, while we used Slack messenger to manage sprints and communications with the client. Throughout the platform's development cycle, our specialists' knowledge was very useful in creating and implementing various functions. 

After realizing the shell platform and its initial testing, we began the labor-intensive work on implementing machine learning algorithms. The customer provided us with large amounts of data, which were processed by algorithms and trained for recognition, sorting, analysis, and hints. Our QA specialists then tested the algorithm and the platform together until the completion. After that, we provided this software to the customer so that he could test and subsequently fully integrate it into the private clinic environment.


Project duration


Invatechs experts fully met the client's requirements in creating modern and innovative software for processing data using machine learning. Since the application is cross-platform, it can be used on different devices, making it even more convenient for the medical staff. The app has helped increase efficiency in patient care, leading to positive growth in the private clinic chain.

Moreover, the use of machine learning can also help reduce the costs associated with medical care. Healthcare providers can save money on staffing and other costs by automating certain tasks and reducing the need for manual data entry and analysis. In addition, machine learning can help reduce the overall cost of care for patients and insurance companies by making medical care more efficient and effective.

We have connected closely with our customer to have an open dialogue, creating a great healthcare solution. We haven't stopped the partnership, and we're updating the software, adding new and useful features for medical staff.

  • Employee efficiency increased by 80%

  • Convenient and cross-platform application 

  • Secure cloud storage

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