Health data interoperability is a crucial aspect of the digital transformation in healthcare. The FHIR (Fast Healthcare Interoperability Resources) standard is gaining global importance to facilitate data exchange between healthcare providers and, ultimately, improve patient care efficiency. This article explores future developments and trends in health data interoperability and how FHIR serves as the foundation for innovation in German healthcare.
Future Developments and Trends in Health Data Interoperability
Interoperability in healthcare is continually evolving, driven by technological advancements and regulatory requirements. A significant trend is the growing importance of real-time data and its use for clinical decision-making and patient monitoring. Additionally, big data and artificial intelligence (AI) play an increasingly crucial role in analyzing and utilizing health data.
FHIR as a Basis for Future Innovations
FHIR was developed to standardize and simplify the exchange of health information. By using modern web technologies such as RESTful APIs and JSON, FHIR provides a flexible and scalable solution for the integration and exchange of health data. In Germany, Gematik, the agency responsible for telematics applications in healthcare, has recognized FHIR as an important standard to promote interoperability in healthcare.
One example of the successful use of FHIR is the University Hospital Essen, which has made FHIR the foundation of its Smart Hospital ecosystem and now has a FHIR repository with over 1.5 billion resources. By implementing FHIR, various systems and data sources (HIS, LIS, PACS, etc.) have been integrated to create a comprehensive overview of patient care.
At University Hospital Essen, numerous complex analyses—both for controlling and everyday medical use—are based on FHIR data. FHIR also offers enormous benefits for developing new, AI-supported innovations in healthcare.
The Modern Healthcare System: Artificial Intelligence Meets FHIR
Artificial intelligence (AI) in healthcare relies on large amounts of data currently collected or aggregated from various sources and technologies. Based on these data sets, both supervised and unsupervised learning methods are used to recognize patterns, make predictions, or solve generative problems. The goal is to develop generalizable AI models for potential medical applications that are not only suitable for specific locations or institutions but also easily transferable. Such models should prevent fragmentation and serve as integrative solutions to enable consistent and improved patient care or to enhance processes within healthcare.
To achieve this, the use and establishment of unified data standards such as FHIR play an important role in ensuring the easy transferability and expansion of these models. FHIR enables the consistent and structured integration of data from different sources, improving the development, performance, and generalizability of AI models. By creating an interoperable data structure, the necessary foundation is laid to ensure the easy transfer and adaptation of AI models between institutions and countries. Furthermore, FHIR supports a variety of data types and concepts, which enables the development of complex multimodal AI models.
A specific example of the use of FHIR in the context of AI is the development of predictive models for patient-specific risks. By integrating FHIR-based electronic health records, AI algorithms can access a variety of structured patient data, including demographic information, medical histories, lab results, and medication plans. With this comprehensive data, AI models can be trained to predict the risk of hospital admission, deterioration in health status, or other adverse events. Such systems can then be used by doctors and nursing staff to enable early preventive measures. Additionally, the clinical parameters mentioned can also be used for personalized medicine, where AI algorithms are used to develop tailored treatment plans.
Another example is the optimization of clinical studies and research through AI and FHIR. Recruiting suitable participants for clinical studies is often a significant challenge. By using AI models that access FHIR data, researchers can quickly and efficiently identify potential study participants based on specific criteria such as genetic markers, disease stages, and previous treatment outcomes. This not only significantly shortens the recruitment time but also improves the quality and relevance of research results.
Furthermore, large language models (LLMs) in combination with FHIR play an important role. They can be used to automatically recognize specific medical entities such as disease names and diagnoses from clinical notes and doctor's letters, making data processing more efficient and accurate. Additionally, so-called Retrieval-Augmented Generation (RAG) systems combine text generation (prompts) with targeted data access to provide precise answers to medical queries by extracting relevant information from FHIR data and enriching the context.
The examples and application areas listed demonstrate why FHIR is indispensable for the use and development of AI systems in healthcare. It is also important to note that AI models can not only consume FHIR data but also, in relevant cases, produce data (predictions) that are then stored as FHIR resources and made usable. For example, AI models can generate predictions about the course of a disease, the response to a particular therapy, or image-based biomarkers or measurements. These predictions can then be stored in FHIR format so that they are available to other clinical systems and professionals. This promotes the seamless, interoperable, and standardised integration of AI results into clinical workflows.
Conclusion
FHIR plays a crucial role in promoting health data interoperability and forms the foundation for future innovations in healthcare. By integrating new technologies based on FHIR, healthcare providers can improve their efficiency, optimise patient care, and gain valuable insights into health data. Major hospitals like the University Medicine Essen already show how FHIR can be successfully implemented and used to revolutionize healthcare.
---------------------------------------------------------------------------------------------------------------
Sources: [RH1]https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6110979/Â
Comments