The healthcare industry is entering a new phase
In 2025, companies developing AI solutions captured 55% of all healthtech sector funding, compared to 37% the year before (Bessemer Venture Partners, State of Health AI 2026). This growth clearly reflects the direction healthcare investment is heading and has a direct impact on how medical products are built.
The focal point of this shift is healthcare system integration the way in which different components of the medical environment (EHRs, devices, telemedicine platforms, analytics) are beginning to operate as a single ecosystem rather than a collection of isolated tools. This is especially relevant in the DACH region, where regulatory pressure and the pace of digitization create particularly demanding conditions for companies building healthtech products.
What is healthcare system integration?
In the context of the projects we work on, healthcare system integration is the process of connecting various applications, medical devices, and databases into a coherent architecture that enables real-time data exchange while maintaining regulatory compliance (HIPAA, GDPR, MDR, EHDS) and ensuring security and operational continuity. HL7 and FHIR standards serve as the technical framework, defining how data is exchanged between medical systems.
In practice, this means synchronizing data from CGM devices with a patient app, integrating test results with a hospital EHR system, transmitting data from wearables to an analytics platform, or connecting a telemedicine module with medical records.
Telemedicine as a system node, not a standalone app
Not long ago, an online consultation platform was a self-contained product sufficient on its own to build a valuable solution. Today, telemedicine rarely operates in isolation from the rest of the infrastructure: patient data modules, EHR systems, device integrations, and an analytics layer. This shift directly impacts how such products are designed.
Integration architecture must be considered at the MVP stage, because bolting additional systems onto an inflexible base later is one of the most common causes of costly refactoring in healthtech.
Where the real complexity of healthcare integration lies
Medical software almost never operates in isolation. Even a relatively simple solution requires data exchange with at least several external systems: hospital infrastructure, EHR providers, device registries and external services.
The complexity, however, doesn’t stem from the sheer number of integrations it comes from reliability requirements: data consistency across systems, transmission security, handling edge cases at scale and managing changing API versions on the side of external providers.
In practice – mySugr / Roche Group: When integrating the mySugr diabetes management platform for Roche Group, the key challenge was ensuring stable communication between Continuous Glucose Monitoring (CGM) devices and the patient app. Data had to be transmitted reliably and made available simultaneously in the app and in the analytics layer. In parallel, a data platform based on AWS Redshift was built to aggregate data from multiple regions and transform it into actionable clinical and product insights.
Project details: fireup.pro/pl/case-studies/mysugr
From integrated app to data platform – how the transition happens
Integration projects in healthcare have a characteristic tendency: they start as applications and grow into data platforms. This happens when continuous data streams begin flowing into the system from devices, users, external APIs and the need arises to collect, process, store, and analyze that data in a scalable way.
A classic CRUD approach is insufficient when data is pouring in from tens of thousands of devices per day. In such projects, data pipelines, event-driven architecture (e.g., Kafka), cloud solutions with auto-scaling mechanisms, and data quality monitoring tools become essential.
Why AI in healthcare starts with integration, not the model
Artificial intelligence is a real trend in healthcare from supporting medical imaging diagnostics, through therapy personalization, to automating clinical documentation. In real-world projects, however, AI is rarely the starting point.
Before models can deliver value, the data must be available, consistent, and properly integrated across systems. A predictive model receiving incomplete or inconsistent inputs doesn’t just fail to help it actively introduces errors into clinical processes.
This is why a growing number of teams are moving away from the „let’s add AI to the finished product” approach, in favor of designing systems with the long-term operation of AI in mind. In practice, this means investing in integration quality before any model is introduced.
How to rapidly develop a healthcare product without breaking integrations
Healthtech startups operate under a dual pressure: they must quickly validate product hypotheses and get to market, but they cannot afford technical decisions that will block scaling after a funding round.
In integration projects, the tension between speed and quality is best resolved through test automation especially for interfaces between systems, where regressions are harder to detect than in application logic.
In practice – Mentalyc: On the Mentalyc project, rather than solely speeding up feature delivery, fireup.pro implemented a test automation framework that allowed the team to increase release frequency without compromising stability.
Project details: fireup.pro/pl/case-studies/mentalyc
Medical devices and software – one ecosystem, not two products
The boundary between medical hardware and software is becoming increasingly blurred. CGMs, wearables, diagnostic systems, and data-transmitting implants generate streams of information that must reach an app, an EHR system, an analytics platform, or directly a AI model.
From a healthcare systems integration perspective, device manufacturers and software developers increasingly need to design together. In the mySugr project, the CGM device itself was the entry point. Real utility only began at the data processing stage within the app, analytics, and the feedback loop to the patient and physician.
The specifics of system integration in the DACH region
The DACH region (Germany, Austria, Switzerland) is one of the most demanding markets for healthtech companies, both in regulatory and technical terms. National requirements layer on top of EU regulations (MDR, GDPR), hospital systems are often built on years of heterogeneous legacy infrastructure, and data security standards rank among the strictest in Europe.
In 2025, Germany introduced the mandatory ISiK Stage 3 standard for hospitals, defining requirements for clinical data exchange between systems. Companies building software that integrates with German hospital infrastructure must account for ISiK compliance already at the architecture design stage. Simultaneously, the rollout of the electronic patient record (ePA) is underway in an opt-out model by 2026, the majority of insured Germans will have active digital health records, opening new integration opportunities but also imposing specific technical requirements.
A separate category is DiGA (Digitale Gesundheitsanwendungen) digital health applications reimbursed by German statutory health insurance. For companies targeting the German market, the DiGA certification pathway requires meeting specific interoperability requirements, which directly affects architectural decisions as early as the MVP phase.
At the EU level, EHDS (European Health Data Space) is playing an increasingly important role, obligating providers to offer standardized electronic records and establishing a framework for cross-border medical data exchange. For companies developing products for the DACH market, EHDS is not a distant prospect the first requirements came into effect in 2025 and will be gradually expanded.
Healthcare system integration in this region therefore involves working with standards (HL7, FHIR, ISiK, DICOM), local regulations on medical data storage, and often complex tendering structures and certification requirements. Companies achieving lasting results in DACH treat compliance and integration not as a project phase, but as a permanent component of product engineering.
Why the traditional MVP approach doesn’t work in healthtech
In a standard startup, MVP means: build as fast as possible, measure, iterate. In healthcare, this logic requires modification. Medical data regulations, security requirements, and the necessity of integrating with external clinical systems mean that an MVP must be technically solid from the start.
This doesn’t mean it has to be complete. Architectural decisions made during the MVP phase especially those related to integration and data processing will have consequences for the next 2–3 years of product development.
The most common mistakes in healthcare system integration
- Treating integration as the last step – planning connections with external systems only after the product core is built leads to costly refactoring.
- Underestimating error handling – external APIs change versions, temporarily unavailable systems are the norm rather than the exception; the lack of handling for these scenarios only becomes apparent at scale.
- Ignoring data standardization – data from different sources (devices, EHRs, users) rarely shares the same format; consistency must be actively enforced through the pipeline.
- Outsourcing without knowledge transfer – in integration projects, technical context is just as important as the code itself; losing it when a team changes costs months of work.
- Skipping DACH requirements at the MVP stage – compliance with ISiK, DiGA, or EHDS built in from the beginning is many times cheaper than retrofitting before certification.
Healthcare system integration is not a technical problem solved once during the project phase it is a permanent element of medical product engineering, from architectural decisions in the MVP, through data management at scale, to preparing infrastructure for AI.
In the DACH region, where regulatory requirements are among the highest in Europe and hospital infrastructure is often heterogeneous, the combination of technical expertise and market-specific understanding is what counts. Knowledge of ISiK standards, the DiGA certification pathway, and EHDS requirements is not supplementary knowledge it is the entry condition for companies aiming to build products with a genuine chance on this market.
