The architecture of laboratory IT systems is becoming a critical factor in clinic stability, medical data security, and continuity of diagnostic processes. Kateryna Kuznetsova—a solution architect at US-based SCC with experience in production deployment of laboratory and logistics IT systems for laboratories in the US, Canada, and Europe—explains which architectural solutions enable scaling these platforms without sacrificing stability.
The digitalization of laboratory diagnostics and data management has emerged as one of healthcare's most important trends in 2025. According to industry reports, the laboratory information systems (LIS/LIMS) market is valued at nearly $600 million USD and continues to grow steadily, with expectations to exceed $940 million by 2030. This demand reflects a shift from legacy systems to comprehensive digital platforms capable not only of tracking biological materials and results, but also ensuring their integrity, security, and integration into broader laboratories IT workflows.
In this context, architecture development and design practices extend beyond efficiency concerns and directly relate to regulatory, operational, and security requirements. As Kateryna Kuznetsova—a software architect with experience in production deployment of laboratory and logistics platforms for clinics in the US, Canada, and Europe, former software architect at ISD, author of a published methodology for identifying and preventing security vulnerabilities, and member of the international AITEX association—notes, architectural decisions shape the resilience of medical IT systems against loads, threats, and changing industry requirements.
Scaling No Longer Means "Add More Servers"
In recent years, medical and laboratory IT systems have faced a qualitatively new type of load. We're not just talking about growth in user numbers or data volume, but rather a combination of several factors: distributed clinic infrastructure, high-density data operations, and strict data integrity requirements.
According to software architect Kateryna Kuznetsova, under these conditions, scaling is no longer an operational task.
Drawing on her own experience designing and supporting laboratory IT systems, Kateryna Kuznetsova notes that resilience to peak loads is built into architectural decisions.
"In practice, these projects have shown good results with the following approaches. First, smart multi-level caching, which reduces database load and allows the system to respond faster to repeated queries. Second, queuing messages from external systems with subsequent processing as backend resources become available—this smooths out load spikes without data loss," she explains.
Additionally, the ability to handle batch data processing should be built into client-server contracts at the design stage, Kuznetsova emphasizes. This approach reduces the number of database calls and increases overall system throughput.
Where DevOps Ends, and Architecture Begins
DevOps approaches are now industry standard, but their role in medical projects is limited. As Kuznetsova explains, DevOps effectively automates deployment, updates, and monitoring, but doesn't answer how the system behaves as load increases and use cases become more complex. This is confirmed by Kateryna Kuznetsova's practical experience with real laboratory systems.
"Medical IT platforms use the HL7 protocol for data exchange: all messages from equipment and external systems enter a common pool, then get processed by the system itself. During peak load periods—for example, during morning laboratory hours—processing delays can reach several minutes, and how the system handles this depends directly on the chosen deployment model," she emphasizes.
With on-premise deployment, resources are typically provisioned with significant overhead to cover possible peak loads. Cloud deployment is different: most cloud providers offer built-in monitoring and auto-scaling services that allow increasing or decreasing resources based on current load, Kateryna explains.
That said, she notes, a significant portion of clients still remain on on-premise solutions. In the US, this is often due to regulatory restrictions that prevent patient medical data from leaving a specific state or country. In such cases, she personally decided to apply hybrid architectures: the database remained local—inside the laboratories—while the application itself was deployed in the cloud.
Practice shows that nearly all system components—application servers, message queues, processors, report generation modules—can scale independently of each other, depending on where the bottleneck occurs. A properly configured load balancer plays a key role here, one that accounts for user sessions: even when adding new instances, requests from a specific user should be routed to the same host, keeping scaling transparent to the end user.
Data Flows and Security as the Foundation of Scalability
Kateryna's experience with LIS platforms and biological material tracking systems shows that the key bottleneck as load grows isn't interfaces, but data processing.
"The architectural transition from synchronous operations to asynchronous data flows allows the system to adapt to peak loads—for example, during morning hours when the laboratory receives the maximum number of samples. Using queues and data buses enables dynamic scaling of processors without blocking user scenarios," Kuznetsova notes.
At the same time, she says, not all operations require immediate strict consistency. In some cases, applying eventual consistency maintains high performance without violating regulatory requirements.
A separate aspect the architect highlights is the close connection between scalability and security. This approach formed the basis of a methodology developed by Kateryna Kuznetsova and published in 2024, dedicated to identifying and eliminating security vulnerabilities at the software development stage.
In LIS projects for the US and Europe where Kuznetsova participated, security was treated as an integral part of the architecture. Zero Trust and Built-in Security approaches were incorporated at the design stage, allowing the system to scale without constant reworking of security mechanisms. The practical significance of this approach is confirmed by the professional community: in 2025, Kateryna Kuznetsova served as a judge for the international AITEX Tech for Good Health Hackathon, dedicated to digital solutions in healthcare.
Laboratory Platform Resilience: Hybrid Architecture, Data Control, and Architect Responsibility
Many laboratories continue using on-premise solutions for legal or operational reasons, supplementing them with cloud services.
As an architect, Kateryna has to design systems that work equally reliably in different environments. Therefore, it's important to understand not just server status, but the complete data path within the system.
"In biological material tracking systems, this means being able to trace every operation—from scanning a tube to writing information to storage. This level of observability allows identifying bottlenecks before they lead to failures, and significantly simplifies regulatory audits," Kuznetsova explains.
In her view, medical IT system architects are increasingly acting not as technical coordinators, but as strategists of sustainable platform growth. This role requires a deep understanding of technology, regulations, and real-world processes in clinics and laboratories. It's architectural thinking that allows such platforms to scale while maintaining stability, security, and trust from medical organizations.
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