Executive Summary
Situation
Dutch first-line healthcare still faces major problems with how its information systems work together. Many of these systems are outdated, don't follow the same standards, and can't easily share data with each other. This makes it difficul
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Executive Summary
Situation
Dutch first-line healthcare still faces major problems with how its information systems work together. Many of these systems are outdated, don't follow the same standards, and can't easily share data with each other. This makes it difficult for doctors, pharmacists, and nurses to work with complete and up-to-date patient information. It also creates obstacles for third-party developers, like those building telehealth apps or digital tools, who need to connect to these systems to offer new services.
Even though most healthcare providers use digital tools, these tools don’t speak the same language. That means that when one provider enters data, another might not be able to read or use it properly. This lack of data interoperability slows down care, increases paperwork, and limits innovation.
To address these issues, this thesis introduces a modular platform architecture that follows shared rules and standards. The goal is to help different systems work together more smoothly and allow developers to build useful tools without having to create custom connections for each system. The platform uses common APIs and reusable services to exchange data in a secure and structured way, while staying in line with Dutch healthcare policies.
Research Question
This research focuses on the following main question:
How can a platform architecture be designed to enhance data interoperability in first-line healthcare, enabling seamless integration for third-party complementors to support healthcare practitioners?
The question comes from the need to reduce technical complexity and help third-party developers deliver value to healthcare professionals faster and more reliably.
Approach
To answer this question, a systems engineering approach was used. The research started with interviews, where one was completed with a general practitioner and one with an expert from a third-party healthcare IT company. These conversations revealed key issues, such as outdated systems, mismatched data meanings, and a lack of shared standards.
From these insights, a list of requirements was created to guide the platform’s design. Then, several UML diagrams were developed to model how different components of the platform should interact. Based on these models, a working prototype was built using Python (Flask). This included core services like data validation, transformation, and user authentication.
Finally, expert software architects reviewed the prototype. Their feedback confirmed that the design made sense and had potential for real-world use. They also highlighted areas that still need improvement, especially in terms of security and handling data meaningfully and correctly.
Results and Conclusions
The research confirmed that fragmentation, outdated technology, and poor standard usage are the main barriers to data sharing in Dutch first-line healthcare. To address this, a modular platform was developed and tested.
The prototype showed that using shared components for data transformation and validation makes it easier to connect systems. Two services, the DataTransformationModule and the ExternalSystemConnector were seen as essential for creating an interoperable data platform.
The proposed design uses a layered structure and modular services. This makes it scalable, flexible, and easier for external developers to integrate with. While the technical foundation is strong, more work is needed—especially on semantic interoperability (ensuring data has the same meaning everywhere), user access control, and real-life testing in clinics.
Contribution
This thesis adds to both academic knowledge and practical system development.
From a theoretical perspective, it applies platform thinking to healthcare IT, based on Tiwana’s core–periphery model. It also builds on Deshmukh’s layered model of interoperability by turning abstract ideas into working components where each deals with structure, meaning, or security in healthcare data.
On the practical side, it provides a working system design that others can build on. The prototype shows how developers can use reusable components, like data validation or transformation services, to save time and improve data quality. This approach can help reduce technical effort and improve care delivery by making systems more connected.
Recommendations
To make this platform usable in real healthcare environments, several improvements are needed. First, authentication should be upgraded from basic API keys to more secure methods like OAuth2 or federated identity systems. These changes will better protect patient data and support real-world compliance without making the system harder to use.
Second, semantic interoperability needs more attention. Although the platform handles structure and syntax well, it still lacks tools to make sure that medical data means the same thing across systems. Future versions should include support for terminology mapping and validation using Dutch coding systems like SNOMED CT, ICPC, and LOINC.
Third, flexibility should remain a core design principle. Some services, like conflict detection, may only be useful in specific cases and should be added as optional modules rather than built-in features. This keeps the platform adaptable for different types of healthcare organizations.
Next, the platform should be tested in live clinical environments. Real-world deployment would uncover workflow issues, performance bottlenecks, and user experience gaps that are not visible in lab tests. Feedback from actual users, namely: GPs, nurses and pharmacists, is vital to make the system both effective and usable.
Finally, as data standards evolve, the platform must stay up-to-date. Future versions should be able to work with new releases of HL7 FHIR and support multiple profiles. This ensures long-term compatibility and encourages broader adoption.
In summary, this research lays the groundwork for a modular, standards-based platform that makes healthcare data more interoperable. It supports both better patient care and more efficient innovation by reducing the technical barriers that developers and healthcare professionals currently face.
