Prototyping and evaluation of a clinical decision support system in long-term care in German nursing homes

Demographic change in Germany is increasing the demand for professional long-term care. Given rising requirements and simultaneous staff shortages, the need for digital systems is growing to effectively support nursing staff and mitigate staffing bottlenecks. This article introduces a hybrid clinical decision support system (CDSS) designed to generate actionable intervention recommendations by combining structured data and narrative nursing documentation. The CDSS consists of three interacting components: (1) knowledge models that encode state-of-the-art nursing knowledge, (2) a rule-based expert system that applies these models to generate individualized nursing interventions within the application fields mobility and pain management, and (3) a large language model (LLM) that extracts indicators that are necessary for knowledge-based decision-making from free-text entries to fill documentation gaps. A prototype was implemented and descriptively evaluated in two German nursing homes. Overall, 72% of the CDSS recommendations were rated as "correct" by nursing professionals, meaning appropriate for the care situation. In a separate evaluation, the LLM achieved 76% agreement rate in extracting care-relevant information from narratives. These results suggest that a hybrid architecture integrating expert knowledge, rule-based logic, and language models may have the potential to enhance nursing planning in long-term care settings.