Grademix Configurator in Long-Term Care
In long-term care, there is an urgent need to determine staff resources in a standardised way and based on objective planning principles, considering the complexity of the resident's situation.
Factsheet
- Lead school School of Health Professions
- Additional schools School of Engineering and Computer Science
- Institute(s) Nursing
- Research unit(s) Innovation in the Field of Health Care and Human Resources Development
- Strategic thematic field Thematic field "Caring Society"
- Funding organisation Innosuisse
- Duration (planned) 01.12.2022 - 30.09.2025
- Project management Prof. Dr. Sabine Hahn
- Head of project Prof. Dr. Sabine Hahn
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Project staff
Prof. Dr. Sabine Hahn
Dr. Christoph Golz
Niklaus Stefan Bernet
Fabienne Josefine Renggli
Ramona Linda Blättler
Prof. Dr. Mascha Kurpicz-Briki
Solomiia Tymoshchuk
Stefka Goldschmid
Carlo Riederer
Manuel Fischer - Partner BESA QSys AG
Situation
In long-term care in Switzerland, staffing needs are estimated based on experience. In doing so, those responsible try to consider relevant influencing factors, such as the grade mix or the care needs. This subjective assessment can lead to sub-optimal team compositions, mistakes and over- or under-demanding of nursing staff. Therefore, there is a need for a standardised and objective planning basis that determines the staffing needs by taking relevant factors into account. The Grade Mix Configurator for long-term care, developed in this project, meets this need by combining three algorithms. These algorithms process routine data to assess complexity, national guidelines on staffing ratios, defined quality levels and guidelines from a grade-mix framework model. The result is visualised in a comprehensible dashboard for responsible management and creates a comprehensible basis for evidence-based planning of effective staffing requirements. This innovation will revolutionise systematic quality improvement and the resource-oriented deployment of nurses in Switzerland. In a preliminary study conducted by BESA QSys AG and BFH, one third of the participating nursing homes already showed interest in the planned product.
Course of action
The scientific challenges are the integration and processing of routine data from within the home, the processing of the various data sources in algorithms and the scientific assurance of the benefits by evaluating the expected added value. With their many years of expertise in the areas of grade mix, complexity of care situations, quality of care and development, development of algorithms and user-friendly data visualisation, the research partners of the BFH have sound competences for this innovation. BESA QSys AG has the necessary practical partnerships and the data basis for the implementation of the project. This project is the first to address the scientific challenge by processing routine data from BESA QSys (1), the standardised assessment of complexity (2), in conjunction with the nurse to resident ratio (number of nurses per qualification level in compliance with national and cantonal guidelines) (3), to create the grade mix configurator and test it in practice, taking into account a predetermined quality level (4) and with reference to the grade mix framework model (5) by means of several algorithms, using BFH-TI data analysis and processing technologies, create the Grade Mix Configurator and test it in practice.
