Have you ever wondered what makes some healthcare software grow fast and serve millions, while others struggle to keep up? The answer often lies behind the scenes – in the tech stack. A tech stack is more than mere code. It is the foundation on which an entire system is built. The tools, the frameworks, and the platforms chosen early on can either open doors for innovation or hold software back when demand rises.
So, how exactly does the tech stack drive scalability in healthcare software? Let us take a closer look. For this article, we conducted an interview with our expert, Iliyan Gorki. He is an experienced .NET developer with a demonstrated history of working in the computer software industry.
What are tech stacks?
A tech stack is the combination of tools and frameworks that work together to build and run software. It provides the basic structure needed for an application to function and grow. A well-chosen tech stack can streamline development, improve performance, and even set the stage for future enhancements.
In a tech stack, each component plays a specific role. Here are the key elements:
- Frontend: The user interface. Technologies like React or Angular are used to build responsive and interactive designs.
- Backend: The server logic. Languages such as Node.js, Python, or Java manage data and processes.
- Database: The storage of information. Systems like PostgreSQL or MongoDB ensure data is handled reliably.
- Infrastructure/Cloud: The environment where the software operates. Platforms like AWS, Microsoft Azure, or Google Cloud offer scalability and security.
These building blocks come together to form the backbone of modern applications, our expert says. This includes those in the healthcare sector, where reliability and performance are crucial, so they must be executed perfectly.
Tech stacks’ impact on the scalability of healthcare software
The right choice of tech stack depends on the problem that needs to be solved in order to maximise the scalability of healthcare software. In some cases, software is so large and complex that scalability is almost an afterthought. In other words, software must handle many users at the same time, with high demand.
Iliyan Gorki advises that the first question to consider is the number of users. Another thing to decide is whether the system should be built on cloud infrastructure or hosted on premises. For instance, if the system is designed as an informational platform where data is stored but not actively processed, the technology needs are different from those of a system that performs constant calculations or simulations.
A microservice architecture is one popular solution for building scalable healthcare software. In this approach, one service performs one specific task, such as adding a job or processing a calculation. These services can be scaled both vertically, by increasing resources for a single service, and horizontally, by adding more instances of that service.
Cloud infrastructures, such as Amazon Web Services or OpenShift, offer the necessary tools to manage such dynamic scaling. The technology selection depends on the nature of the data and the required calculations, which ultimately shapes a system that is robust, efficient, and capable of growing with demand. For a deeper dive into how tech choices align with regulatory needs, check out our article on building a GMP tech stack that supports validated healthcare systems.
How BGO Software builds custom healthcare tech stacks
Our expert emphasises that every healthcare project requires an individualised approach. There’s no one-size-fits-all stack—the right tools depend entirely on the project’s specific demands.
Programming Languages
The choice of programming language hinges on the task:
- Python: Often used for backend tasks thanks to its fast libraries that streamline data transformation.
- R: Ideal for data analysis and statistical modelling, especially when handling large volumes of calculations.
- PHP / .NET: Commonly used for managing communication between the frontend, databases, and third-party services.
Each language offers distinct strengths, and the selection depends on the project’s technical and functional needs.
Databases
Database selection follows the same strategic logic:
- Microsoft SQL (with .NET): Suited for structured, relational data where consistency and reliability are key.
- MongoDB: Chosen when handling a high volume of small, loosely structured data points. Offers flexibility and speed in scenarios where a traditional relational model would be too rigid.
Decision Criteria
At every step, the tech stack is aligned with the project’s goals:
- What kind of data is being handled?
- What’s the expected load and complexity?
- How will different components need to communicate?
By evaluating these factors early, the team ensures that the chosen stack delivers a scalable, efficient, and maintainable solution.
Practical example on healthcare tech stacks impact
Some healthcare projects come with unique technical requirements from the very beginning. Such is the example that our expert, Iliyan Gorki, gave us. The case is about a system that had to run only on a dedicated Linux server. This type of server offers stability, but it also needs constant maintenance. Unlike modern cloud platforms, Linux servers do not have built-in tools to automatically detect when an application fails or stops working. That meant 24/7 monitoring.
The team embraced .NET for the backend because of its ability to process many requests with exceptional speed and security. For data management, R and SQL worked in tandem. R is brilliant at data crunching and uncovering trends with its advanced libraries. SQL, on the other hand, excels at organizing and retrieving structured information quickly.
A key part of this project was the use of the Parquet file format for storing and analyzing large datasets. Parquet is a column-based storage format that is incredibly fast when it comes to data analysis. It compresses well, reads quickly, and is widely used in healthcare for managing large volumes of clinical and research data.
On the front frontend, Angular was chosen to build a dynamic and interactive user interface. This mix of tools helped balance heavy data processing with a smooth user experience. Choosing the right stack was not just a technical decision – it was what made the project work under tight conditions.
Challenges in healthcare tech stack implementation
Implementing a tech stack in healthcare software is rarely smooth. Each stage brings its own set of challenges that require smart and flexible solutions. Here are some of the most common challenges in the field:
- Convincing the client
“Most of the time, one of the hardest parts of implementing a tech stack is convincing the client that they need new technology with up-to-date tools. Persuading clients that an upgrade will save time and optimize their work is a long and difficult process.”
–Iliyan Gorki
Clients may hesitate to invest in new technology due to concerns about cost and downtime. Sometimes, they do not even see the benefits of such an upgrade. It is important to understand the client’s budget and their expectations. For instance, if a product needs fast visualization and data analysis, it demands technologies that support rapid rendering and efficient processing. A practical solution is to conduct detailed consultations and present clear, data-backed proposals. Prototypes, demos, or even case studies from similar projects help illustrate the benefits.
- Legacy systems
Legacy systems pose another hurdle. Many organizations struggle with outdated code that is not documented, leading to time-consuming reverse engineering. Different scenarios require different tactics, from incremental modernization to complete system overhauls. A practical solution is to conduct a thorough audit of the existing system and tailor the upgrade strategy based on its specific limitations.
- Regulatory compliance and security
In healthcare, strict regulations demand rigorous security measures. The challenge includes preparing for regular audits, penetration tests, performance tests, and stress tests, our expert says. A good resolution is to integrate a robust testing schedule and use automated tools to ensure that all aspects of the system meet compliance and security standards.
Healthcare fraud costs an estimated $2.6 billion annually in the United States, with blockchain-based fraud detection systems achieving up to 97.96% accuracy in identifying fraudulent claims and 98.09% sensitivity in detection rates.
- Productivity maintenance
The implementation of new technologies can disrupt ongoing workflows and lower productivity due to steep learning curves and continuous process changes. The resolution is to adopt agile project management practices and provide continuous team training. This strikes a balance between the implementation efforts and the development process, ensuring it is efficient and on track.
Overcoming these challenges is not always easy, but with the right approach and tools, you can build stronger healthcare software.
Scalability potential
Scalability must be a consideration in the very beginning of any process. It is much more than just adding more users or data. It is embedded in the software’s most basic structure. The architecture and how modules are designed play a huge role. If the foundation is solid, everything that follows becomes easier to scale. Each module must be built with care, keeping in mind the bigger picture. The goal is to make them flexible so they can grow without breaking the system. Once the architecture is set, the next step is to focus on speed. How fast we can develop and expand the system depends on how well these modules interact and perform.
Our expert, Iliyan Gorki, also reminds us that the first module is especially critical. It must pass stress tests and prove its stability. This is where unit testing and thorough QA work come in. If the first module is flawed, those mistakes get passed down, making future scaling much harder. That is why testing early and testing seriously is key to unlocking a system’s true scalability potential.
Prime example is Ontario’s Emergency Department Return Visit Quality Program. It covers 89 hospitals and serves 90% of all ED visits in the province, processed 33,956 return visits out of 3,235,751 total ED visits (1.05% return rate), demonstrating how scalable healthcare systems can efficiently handle population-level continuous quality improvement programs.
Building scalable healthcare software starts with choosing the right tech stack and having the courage to innovate. Even older systems can be transformed with the right tools and mindset. Contact our team at BGO Software to upgrade your scalability. Why stick with what no longer works when new technology can take us further?
“Remember to always continue to improve.”
–Iliyan Gorki
Iliyan Gorki is Experienced Dotnet Developer with a demonstrated history of working in the computer software industry. Skilled in Windows Communication Foundation (WCF), HTML, C#, Object-Oriented Programming (OOP), and Cascading Style Sheets (CSS). Strong engineering professional with a Master’s degree focused in Computer Software Engineering
