Project info

Target:

Under the leadership of the IAT, an interdisciplinary consortium from the social sciences, nursing sciences, health economics and medical informatics is investigating, in cooperation with practical institutions of outpatient and inpatient long-term care, whether and how AI applications can contribute to the burdening or relieving of nursing work and which prerequisites and framework conditions must be given so that nursing processes can be designed in a health-promoting and relieving way through the use of AI services. Since target systems of technically supported work require a design process for the desired realisation, ETAP takes a look at the introduction and change process as processes to be designed equally socially, organisationally, qualification-wise and technically.

Course of action

In a co-creation approach, the partial automation of fall risk screening and related documentation will be implemented with the participation of nursing staff in long-term outpatient and inpatient care. The aim is to test the interaction of the nursing staff with these AI applications in the context of their everyday nursing care in order to measure effects on their work situation and to survey the benefits within the framework of a socio-technical system analysis (STSA). Criteria for assessing usefulness (solving a factual problem) usability (solving the human-technology interaction problem) and utilisation (solving acceptance problems) together form the analytical framework for a benefit assessment.

Within the framework of the planned project, an AI-based movement monitoring system will be implemented in outpatient and inpatient long-term care, in which the partial automation for (1) fall risk screening, mobility status and (2) documentation will be tested with regard to possible burden and relief effects for nursing staff. For this purpose, a longitudinal, prospective intervention study with a control group will be conducted and supplemented by focus groups and expert interviews. Effects on the nursing staff's workload and relief will be recorded before and after the co-creation process as well as after 12 months of operation of the AI system. These effects are examined in more detail for two subject areas in outpatient and inpatient care facilities:

(1) Care planning: Partially automated care planning is to be supported by AI-supported monitoring of risks, using the example of a fall risk screening, and by the recognition of abilities to describe the mobility status of people in need of care. 

(2) Documentation: Parts of the care documentation of falls, fall risk and protocols for functional immobility will be AI-supported and automated. 

The further development or adaptation of the technology used will be based on the formative evaluation results and in close coordination with the requirements of the practice partners and taking into account the ethical and social implications of the use of technology.

The research focus "Health Economy & Quality of Life" is responsible for the coordination of the project and for the ELSI accompanying research. The research focus "Work and Change" is responsible for the longitudinal analysis of the loading and unloading effects in the context of the introduction of the AI service, the development of recommendations for action and the transfer of the results to science and practice.