Acceptance and Usability of a Web Application for Patient Care Level Classification in German Clinical Nursing Care: A Pilot Study

The German Federal Ministry of Health introduced the Pflegepersonalregelung 2.0 (PPR 2.0) to address the nursing staffing crisis. It establishes a framework to determine personnel requirements, ensuring adequate staffing. However, the required daily classification of patient care levels imposes a significant administrative burden on nursing staff. Digitizing this process may reduce documentation time and enhance efficiency, but effectiveness depends on usability and acceptance. This study evaluates the acceptance and usability of a direct digitization of the analog PPR 2.0 classification catalog into a digital user interface—the PPR 2.0 Calculator. A mixed-methods approach was used, combining quantitative assessment using the Technology Acceptance Model 3 (TAM 3) and the System Usability Scale (SUS), with qualitative insights from a semistructured interview. Fifteen nursing staff members from a pediatric rheumatology clinic in Germany participated. The PPR 2.0 Calculator was rated highly usable, with strong scores for Perceived Ease of Use (4.00) and Computer Self-Efficacy (4.09). Participants required minimal technical support, indicating an intuitive interface. However, Perceived Usefulness (2.82) and Job Relevance (2.53) scores were lower, suggesting limited value in daily workflows. The SUS score (65.50) was slightly below the benchmark of 68, indicating good usability with moderate room for improvement. Digitizing the analog PPR 2.0 catalog resulted in good usability, but significant challenges regarding practical relevance and workflow integration remained. Directly adopting the catalog content negatively affected perceived usefulness and job relevance, revealing limitations in the classification framework itself. Refinement of the PPR 2.0 framework is needed to reflect real-world clinical nursing tasks. Seamless integration with existing infrastructures and structured documentation is also critical. Future improvements should go beyond simple digitization and explore automated classification features.