Switzerland’s digital health ecosystem came together earlier this year in response to an open invitation: help solve one of the most persistent problems in clinical practice-fragmented, inefficient data capture. The Open Innovation Challenge “Improving clinical data capture for better healthcare outcomes” encouraged anyone – patients, clinicians, researchers, startups, and developers to work on solutions.
In collaboration with the University Hospital Basel and Personalized Health Basel, the initiative focused on a pressing issue: simplifying and standardizing clinical data capture using openEHR to improve data quality, interoperability and workflow efficiency.
This openness and diversity fuelled truly collaborative innovation, turning an abstract technical issue into a shared, urgent mission: to co-create practical tools that clinicians will actually use, powered by openEHR, and built for real-world settings.
Through public webinars, open Q&As, and an onsite Open Innovation Day, the Challenge built an inclusive community of more than 300 participants from across Switzerland. Out of multiple proposals, only six were selected for further support, reflecting the highly competitive nature of the process and the quality of the ideas submitted.
Lessons from the frontlines of innovation
The Challenge uncovered some critical insights for healthtech innovators:
- Structure needs design: Clinicians resist rigid templates. Interfaces must be elegant, intuitive, and reduce – not add – to their workload.
- Collaboration is key: Successful solutions involved clinicians and patients from day one – not just as test users but as co-designers.
- Open standards enable futureproofing: openEHR is not just a tech layer – it’s a foundational enabler of interoperable, reusable clinical data.
The challenge: bridging fragmented data systems
Swiss healthcare – in common with many other countries – faces a well-known interoperability gap. Most hospitals rely on closed, proprietary information systems, meaning that patient data remains siloed in local servers, printed reports, or inaccessible PDFs. Clinicians often re-enter the same data multiple times, wasting time and increasing the risk of errors occurring. This fragmentation limits care coordination and delays innovation.
With no national mandate for standardization, this Challenge aimed to pioneer scalable, interoperable solutions grounded in openEHR, an open standard for health data exchange.
To ensure clinical relevance and feasibility, selected teams were given access to a real-world clinical testbed at the University Hospital Basel, one of Switzerland’s leading institutions in digital health and a pioneer in openEHR adoption. Teams were able to test and refine their concepts in a real hospital setting, gaining insights from clinicians and building solutions that could integrate into actual workflows.
The Digital Health Nation Innovation Booster, powered by Innosuisse, provided critical backing throughout the process. Each selected project received up to CHF 20,000 in non-dilutive funding to validate, prototype or adapt their solutions. Teams also benefited from access to the University Hospital Basel’s R&D data environment, including the Clinical Data Warehouse.
Beyond financial and technical support, the teams were provided with expert coaching, tailored workshops, and clinical mentorship. This structured innovation environment helped multidisciplinary teams move from concept to clinical relevance with confidence and speed.
Spotlight on the shortlisted Challenge projects
Six outstanding teams were selected to move from the idea to MVP stage, each offering a unique approach to data capture, usability and interoperability through openEHR:
Anamedi improves data capture and quality through ambient AI in clinical settings, while reducing clinicians’ manual documentation time. Its solution captures doctor-patient conversations using speech-to-text, fine-tuned for Swiss German and medical terminology, and can be deployed in hospitals, GP offices and psychotherapy practices. Specialized models ensure accurate transcription and terminology across the field. It is compatible with most common healthcare IT standards, including openEHR.
This is an initiative of the Institute for Medical Informatics at the Bern University of Applied Sciences (BFH). The team is examining whether structured and intuitive digital symptom documentation by patients could lead to a more complete and reliable patient history, which healthcare professionals can assess during and in preparation for consultations. The project involves designing a conceptual mobile application and openEHR modelling of symptom data and initially focuses on the capture of pain symptoms.
This is a comprehensive openEHR-based clinical applications portfolio that scales across different healthcare environments and professional groups. Lifecare Clinical Modules empower healthcare professionals with unified, modular software built on open standards. Clinical data is stored in a vendor-neutral openEHR format, enabling seamless interoperability, reuse and integration across systems so that clinicians can act on patient data efficiently.
These are digital assessments of movement impairment to enhance data quality, increase data density, and automate workflows. Building upon observation of clinical workflows, Healthcore is developing frontends that guide the assessor through the data collection process. They incorporate advanced biomechanical modelling tools that enable accurate kinematic analysis, and display the results on data dashboards in near-time.
Automating clinical score calculation using EHR data to reduce errors, save time, and improve diagnostics. RADICAL automates diagnostic score calculations within hospital workflows using openEHR standards. It also reduces manual effort, improves accuracy, and enables structured, interoperable data capture, laying the foundation for scalable, machine learning-ready diagnostics algorithms.
This is a webapp by ALPINA+SANA for nutritionists to view patient meal data, stored in openEHR format; data comes from an AI-based food tracking system. The AI-powered food tracking replaces inaccurate, manual “plate protocols” with automated, next-day nutrition data in OpenEHR format – giving nutritionists a time-saving, precise overview of all patients, including those not yet flagged for malnutrition.
