Development

SAiMM doesn’t interfere with clinical processes – it measures, evaluates, and monitors their maturity, security, and governance.
This makes it clear how powerful, compliant, and trustworthy AI systems really are in practice.

Early Warning Systems & Risk Scores: Sepsis Monitoring in Intensive Care Units. A hospital deploy an AI-based early warning system that analyzes vital signs and laboratory results. Problem: Clinical staff often fail to respond until deterioration becomes apparent. SAiMM Impact: Evaluate validation logic, risks of bias, integration (HL7/FHIR), and human oversight (mortality in sepsis cases (-), response times (-)).

Radiology & Pathology: AI image analysis in a university networkSAiMM Impact: evaluates the quality, interoperability, and bias control of an AI system for triage and diagnostic support. Clinics can demonstrate in a regulatory manner that AI makes both economic and clinical sense (diagnosis errors (-), diagnosis time (-), acceptance rate (+)).

Clinical Decision Support & Medication Safety: Drug Interaction Testing in Internal Medicine. SAiMM Impact: assesses explainability and human oversight – i.e., whether medical staff can understand and correct AI decisions (medication errors (-), adherence to critical therapies (+), trust in AI-supported decision support (+)).

Bed occupancy & capacity control: Maximum care provider with AI-based bed management. SAiMM Impact: evaluates the system’s forecasting accuracy, data quality, and governance. Results: Length of stay (-), utilization (+), emergency room backlog (-). Benefits: Hospitals gain clarity on the maturity of their capacity management and whether funding or scaling is justified.

Wearables & Remote Monitoring: Telemedicine Platform for Heart Failure. Wearables provide data. SAiMM Impact: evaluates their integration, interoperability, and bias (e.g., sensory skin color sensitivity). Results: Rehospitalizations (-), response times (-), patient satisfaction (+). Long-term: SAiMM defines maturity levels for wearable integration – a key criterion for EHDS certifications.

Ressource Management & Logistics: AI systems plan materials and personnel. SAiMM Impact: evaluates forecast accuracy, data protection, and governance. Results: Resource efficiency (+), operating room utilization stable at ca. 90%, Overtime (-).

Patient involvement & digital copilots: Oncology Follow-Up App. SAiMM Impact: evaluates acceptance, ethics, and data protection in patient-centered AI solutions. Results: Patient retention (+), complications (-), trust in digital care (+).

Cross-clinic AI collaborations: Federated Learning (e.g. Germany, Switzerland, Finland). SAiMM Impact: evaluates data protection architecture, model maturity, and interoperability. Results: Development time (-), data protection incidents (0), EHDS compatibility demonstrated.

Clinical Benefits (Measurable medical impact on outcomes, safety, and workflows)

Data Quality & Interoperability (HL7, FHIR, DICOM, semantic standards, data flows)

Model Maturity (Validation, monitoring, bias and drift control)

Compliance & Governance (EU AI Act, MDR, KHZG – auditable)

Acceptance & Patient Orientation (Explainability, transparency, trust, ethics)

Economic Viability & Financing (ROI, efficiency, eligibility for funding)

Qualification & Training (AI competence and structured staff training)