Hospital 4.0 – Lean digital-supported logistic processes in hospitals
The goal of the Hospital 4.0 research project is the further development of innovative logistics systems in hospitals through the use of existing digital technologies. Within the framework of the two reference processes, warehouse logistics and bed logistics, a reference model of Vision Hospital Logistics 2030 and pilots will be developed to demonstrate the potential of digitization.
As an essential element of the health system, hospitals provide socially important health services. Medical core processes are supported by numerous logistics processes, which are, in particular, characterized by high requirements regarding quality, individualization to each patient, real-time response capability and cost efficiency. In today’s hospitals, these logistics processes are often inefficient and wasteful due to historically grown structures and paper-based requirements. As a result, logistics-related tasks make up a large proportion of the time available to doctors and nursing staff. From this development, the actual value-adding core business – namely diagnostics, treatment, and care of patients – suffers severely. This is where the use of digital technologies becomes increasingly important. In line with the vision of an industry 4.0 and the digitalization of the health care system, digital technologies, by enabling to access relevant information in real time and connecting all actors and resources involved in clinical operations, promise considerable potentials for high-quality and a more efficient health care. The aim of “Hospital 4.0”, which is funded by the Federal Ministry of Education and Research (BMBF) as part of the funding priority “Technology-based service systems”, is the development and exemplary implementation of innovative logistics systems in hospitals by means of existing digital technologies. Thereby, the project strives to take a holistic perspective on logistics processes in hospitals as well as their special requirements and challenges, develop new methods for analyzing and benchmarking logistics systems, design new concepts for increasing value creation through the targeted use of existing technologies, and demonstrate their value. Based on these findings, an organizational learning concept should be developed leading to increased quality and efficiency of hospital logistics processes and, thus, to improved patient care. As a starting point, the project develops a preliminary, future-oriented reference model of the “hospital 2030”. Based on a comprehensive analysis of existing logistics systems and technological innovations, digital technologies will be tested with respect to their suitability. Therefore, both the warehouse/picking and bed logistics processes at the hospital in Augsburg and Bayreuth are used to measure and evaluate the current level of digitization. Thereby, established methods such as value stream analysis and value added heat maps are applied. The comparison of the digitization level with the reference model results in the identification of potentials for technology-based improvements of both processes. Based on this, recommendations for actions can be derived and implemented prototypically. Further, the complete set of derived actions, developed methods, conducted surveys, and potential analyses are compiled in a learning concept. Within the scope of the project, process and organizational blueprints emerge, which could be proven as best practices by benchmarking and tested in the participating hospitals. These are made available to all German hospitals for planning and optimizing the use of digital technologies in their own processes. Additionally designed and developed software modules extend existing hospital and logistics information systems. This enables the provision of necessary data and information (e. g., through the extension of location information on the basis of tracking hardware) and an appropriate planning of resource allocation based on this information (e. g., by means of simulation or optimization models).