In many care pathways—particularly in complex clinical scenarios such as stroke—a central weakness in the German healthcare system becomes apparent:
Patient information is fragmented, transferred in an unstructured manner, or not available to subsequent care providers. In daily clinical practice, it is often impossible for medical staff to document data in a way that would support future treatment, e.g., by the general practitioner.
At Actimi, we are addressing this gap with targeted solutions developed as part of the DaDriv research project.
Challenge: Heterogeneous Documentation and Lack of Standards
Stroke patients pass through multiple care settings in a short period of time—from acute hospital care to rehabilitation and then to outpatient follow-up. This generates a wide range of information: nursing reports, medical discharge letters, therapy progress notes, medication plans. In practice, however, these are:
not standardized
often paper-based or handed over orally
and only partially or belatedly accessible to the general practitioner.
The result: Critical information is missing—posing potential risks to the quality of care.
The Potential of the ePA and the Need for Supporting Infrastructure
The introduction of the electronic patient record (ePA) was a meaningful step toward enabling cross-sector communication. However, the ePA alone is not sufficient:
There is a need for systems that capture data in a structured way, consolidate it meaningfully, and make it available at the right time and place—ideally along the actual disease course and tailored to individual patient needs.
Modular Architecture as a Response to Complex Conditions
We therefore pursue a modular approach to meet the diverse requirements of different conditions and care scenarios. Our platform is based on the international HL7 FHIR standard and is designed to integrate heterogeneous data sources in a structured way—be it clinical data, nursing documentation, or patient-generated input.
AI and LLMs as Assistive Systems – Used Thoughtfully
A forward-looking component is the use of artificial intelligence (AI) and large language models (LLMs) to analyze unstructured content such as discharge letters or nursing notes and transform them into structured therapy summaries.
The potential is significant—particularly for the automated generation of interactive therapy plans—but there are also regulatory and trust-related hurdles. For this reason, we currently follow a "human-in-the-loop" approach, where clinical staff validate the AI-generated results.
Use Case: Interactive Care Plans in the DaDriv Project
As part of the BMBF-funded DaDriv project, we are already applying these principles in practice:
For stroke patients, we are developing interactive, digital therapy plans that connect stakeholders across hospitals, rehabilitation centers, outpatient providers, and nursing services. These are based on Actimi’s modular platform architecture—augmented by AI-powered tools for analyzing patient trajectories and condensing key information.
Conclusion
A structured, digital overview of patient data is not a vision for the future—it is a necessary foundation for delivering high-quality, cross-sectoral care. Projects like DaDriv demonstrate how modular platforms supported by intelligent assistive systems can pave the way—always with the goal of providing better and more seamless care for patients.
In many care pathways—particularly in complex clinical scenarios such as stroke—a central weakness in the German healthcare system becomes apparent:
Patient information is fragmented, transferred in an unstructured manner, or not available to subsequent care providers. In daily clinical practice, it is often impossible for medical staff to document data in a way that would support future treatment, e.g., by the general practitioner.
At Actimi, we are addressing this gap with targeted solutions developed as part of the DaDriv research project.
Challenge: Heterogeneous Documentation and Lack of Standards
Stroke patients pass through multiple care settings in a short period of time—from acute hospital care to rehabilitation and then to outpatient follow-up. This generates a wide range of information: nursing reports, medical discharge letters, therapy progress notes, medication plans. In practice, however, these are:
not standardized
often paper-based or handed over orally
and only partially or belatedly accessible to the general practitioner.
The result: Critical information is missing—posing potential risks to the quality of care.
The Potential of the ePA and the Need for Supporting Infrastructure
The introduction of the electronic patient record (ePA) was a meaningful step toward enabling cross-sector communication. However, the ePA alone is not sufficient:
There is a need for systems that capture data in a structured way, consolidate it meaningfully, and make it available at the right time and place—ideally along the actual disease course and tailored to individual patient needs.
Modular Architecture as a Response to Complex Conditions
We therefore pursue a modular approach to meet the diverse requirements of different conditions and care scenarios. Our platform is based on the international HL7 FHIR standard and is designed to integrate heterogeneous data sources in a structured way—be it clinical data, nursing documentation, or patient-generated input.
AI and LLMs as Assistive Systems – Used Thoughtfully
A forward-looking component is the use of artificial intelligence (AI) and large language models (LLMs) to analyze unstructured content such as discharge letters or nursing notes and transform them into structured therapy summaries.
The potential is significant—particularly for the automated generation of interactive therapy plans—but there are also regulatory and trust-related hurdles. For this reason, we currently follow a "human-in-the-loop" approach, where clinical staff validate the AI-generated results.
Use Case: Interactive Care Plans in the DaDriv Project
As part of the BMBF-funded DaDriv project, we are already applying these principles in practice:
For stroke patients, we are developing interactive, digital therapy plans that connect stakeholders across hospitals, rehabilitation centers, outpatient providers, and nursing services. These are based on Actimi’s modular platform architecture—augmented by AI-powered tools for analyzing patient trajectories and condensing key information.
Conclusion
A structured, digital overview of patient data is not a vision for the future—it is a necessary foundation for delivering high-quality, cross-sectoral care. Projects like DaDriv demonstrate how modular platforms supported by intelligent assistive systems can pave the way—always with the goal of providing better and more seamless care for patients.
In many care pathways—particularly in complex clinical scenarios such as stroke—a central weakness in the German healthcare system becomes apparent:
Patient information is fragmented, transferred in an unstructured manner, or not available to subsequent care providers. In daily clinical practice, it is often impossible for medical staff to document data in a way that would support future treatment, e.g., by the general practitioner.
At Actimi, we are addressing this gap with targeted solutions developed as part of the DaDriv research project.
Challenge: Heterogeneous Documentation and Lack of Standards
Stroke patients pass through multiple care settings in a short period of time—from acute hospital care to rehabilitation and then to outpatient follow-up. This generates a wide range of information: nursing reports, medical discharge letters, therapy progress notes, medication plans. In practice, however, these are:
not standardized
often paper-based or handed over orally
and only partially or belatedly accessible to the general practitioner.
The result: Critical information is missing—posing potential risks to the quality of care.
The Potential of the ePA and the Need for Supporting Infrastructure
The introduction of the electronic patient record (ePA) was a meaningful step toward enabling cross-sector communication. However, the ePA alone is not sufficient:
There is a need for systems that capture data in a structured way, consolidate it meaningfully, and make it available at the right time and place—ideally along the actual disease course and tailored to individual patient needs.
Modular Architecture as a Response to Complex Conditions
We therefore pursue a modular approach to meet the diverse requirements of different conditions and care scenarios. Our platform is based on the international HL7 FHIR standard and is designed to integrate heterogeneous data sources in a structured way—be it clinical data, nursing documentation, or patient-generated input.
AI and LLMs as Assistive Systems – Used Thoughtfully
A forward-looking component is the use of artificial intelligence (AI) and large language models (LLMs) to analyze unstructured content such as discharge letters or nursing notes and transform them into structured therapy summaries.
The potential is significant—particularly for the automated generation of interactive therapy plans—but there are also regulatory and trust-related hurdles. For this reason, we currently follow a "human-in-the-loop" approach, where clinical staff validate the AI-generated results.
Use Case: Interactive Care Plans in the DaDriv Project
As part of the BMBF-funded DaDriv project, we are already applying these principles in practice:
For stroke patients, we are developing interactive, digital therapy plans that connect stakeholders across hospitals, rehabilitation centers, outpatient providers, and nursing services. These are based on Actimi’s modular platform architecture—augmented by AI-powered tools for analyzing patient trajectories and condensing key information.
Conclusion
A structured, digital overview of patient data is not a vision for the future—it is a necessary foundation for delivering high-quality, cross-sectoral care. Projects like DaDriv demonstrate how modular platforms supported by intelligent assistive systems can pave the way—always with the goal of providing better and more seamless care for patients.
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