The visiting regulations that have been mandatory in the healthcare sector since the start of the coronavirus pandemic pose enormous challenges for hospitals and care facilities and have defined a completely new management task: Visit management. In order to control the possible entry of infections by visitors and external parties, best-practice concepts are being developed as part of the nationwide research network Applied Surveillance and Testing ( B-FAST).

The aims of the B-FAST sub-project Visit Management, which is financed by federal funds as part of the University Medicine Network, are the scientific evaluation of admission concepts and corresponding prospective recommendations. The project also aims to answer questions relating to the dynamic adaptation of visitor admission to local and regional outbreak events while maintaining dignified palliative care, as well as the coordination of visitor flows and hygiene training. In order to achieve this, observations, semi-structured interviews, structured online questionnaires for hospitals and care facilities, Monte Carlo simulations and discrete event simulations were carried out.

Cooperation partners:

• Prof. Dr. Brunner (Faculty of Economics, Faculty of Medicine)
• Prof. Dr. Messmann, Dr. Römmele (University Hospital Augsburg)
• Dr. Temizel (University Hospital Augsburg)

Publications and working papers:

Bartenschlager CC, Römmele C, Temizel S (2021): Survey on visit management: How care facilities organized visits during the Covid-19 pandemic. Potential of digital systems hardly used. CAREkonkret 32/33.

Bartenschlager CC, Temizel S, Ebigbo A, Gruenherz V, Messmann H, Brunner JO, Römmele C: A simulation based cost-effectiveness analysis of SARS-CoV-2 infection prevention strategies for visitors of health care institutions.

Bartenschlager CC, Frey R, Freitag M, Classen J, Messmann H, Brunner JO, Römmele C: Managing hospital visitor admission during Covid-19: A discrete-event simulation by the data of a German University Hospital.

Temizel S, Bartenschlager CC, Frey R, Freitag M, Messmann H, Brunner JO, Römmele C: Status quo of visitor management in German hospitals during Covid-19: A nationwide survey.

Additional materials:

A detailed analysis of the results of the online survey in care facilities can be found here.

Cooperation partners:

• Prof. Dr. Brunner (Faculty of Economic Sciences, Schools of Medicine)
• Bavarian State Ministry of Health and Care
• Prof. Dr. Heller (University Hospital Augsburg)

Publications:

Römmele, C., Neidel, T., Heins, J., Heider, S., Otten, V., Ebigbo, A., Weber, T., Müller, M., Spring, O., Braun, G., Wittmann, M., Schoenfelder, J., Heller, A. R., Messmann, H., & Brunner, J. O. (2020). Bed capacity management in times of the COVID-19 pandemic: A simulation-based forecast of normal and intensive care unit beds using descriptive data from Augsburg University Hospital. The Anaesthesiologist, 1-8. Advance online publication. https://doi.org/10.1007/s00101-020-00830-6

Polotzek, K.; Karch, A.; Karschau, J.; von Wagner, M.; Lünsmann, B.; Menk, M.; Römmele, C.; Schmitt, J.;...; Freitag, M.; Schoenfelder, J.; Heins, J.; Heider, S.; Brunner, J.O. (2021). COVID-19 pandemic: regional management of patients. Dtsch Arztebl 2021; 118(3): A-84 / B-74

The pandemic caused by the novel coronavirus (SARS-CoV-2) poses enormous problems for the healthcare system as a whole and for hospital capacities in particular. One of the biggest challenges for hospitals and emergency departments is managing patient flows, as the differential diagnosis of a possible COVID-19 infection in symptomatic patients is difficult due to the broad clinical presentation of COVID-19 disease. A prompt and objective decision-making aid to determine whether a COVID-19 disease is present could avoid an unnecessary burden on COVID bed capacities with COVID-19 negative patients.

Based on the laboratory values of previously confirmed and excluded COVID-19 patients collected at Augsburg University Hospital, classic machine learning algorithms and a newly developed algorithm, which we call COVIDAL, were trained. We are already using the algorithms in an Excel-based solution and a browser-based tool, the COVIDAL APP. As analyses show that a broader database can significantly improve the sensitivity and specificity of the algorithms, we are now pursuing a multi-center approach. By integrating relevant data sets from various hospitals in Germany and the LEOSS register, we hope to further improve the results. These will be validated in a head-to-head comparison with several intensive care physicians and infectious disease specialists familiar with the treatment of COVID-19. The project will enable better management of patient flows in the ward and provide physicians with a real-time decision-making aid in the differential diagnostic assessment of a possible COVID-19 infection.

Cooperation partners:

• Prof. Dr. Brunner (Faculty of Economics, Faculty of Medicine)
• Prof. Dr. Hoffmann (University Hospital Augsburg)
• Prof. Dr. Heller (University Hospital Augsburg)
• Prof. Dr. Messmann (University Hospital Augsburg)

Working Paper:

Bartenschlager CC, Ebel SS, Kling S, Brunner JO, Heller AR, Hoffmann R, Messmann H, Römmele C (2021): A sensitive combined machine learning tool for the prediction of Covid-19 infections in a German University Hospital. Working paper, University of Augsburg.

Bartenschlager CC, Kling S, Ebel SS, Brunner JO, Heller AR, Hoffmann R, Messmann H, Römmele C et al. (2021): Balancing performance, turnaround time and costs: A readily applicable AI alternative to POC, PCR and physicians' classification in German hospitals. Working paper, University of Augsburg.

The corona pandemic has brought the discussion about possible triage into the focus of public interest in Germany. Although there are already some suggestions in the literature on how symptomatic patients should be triaged in the emergency department, for example with regard to the urgency of treatment, only a few consider the problem from a data perspective or focus on patients' clinical pathways. Using the data from the LEOSS registry, we validate an existing algorithm, try to identify corresponding optimization potential and implement it with the help of analytics and AI technologies. In addition, we use Monte Carlo simulations to investigate issues relating to the triage of corona patients.

Cooperation partners:

• Prof. Dr. Brunner (Faculty of Economics, Schools of Medicine)
• Prof. Dr. Messmann (University Hospital Augsburg)
• Prof. Dr. Heller (University Hospital Augsburg)

Working Paper:

Bartenschlager CC, Grieger M, Heller AR, Messmann H, Brunner JO, Neidel T, Römmele C et al. (2021): Covid-19 triage in the emergency department 2.0: How analytics and AI transform a human-made algorithm for the prediction of clinical pathways. Working Paper, University of Augsburg.

FFP2 mask or surgical mask? Rapid test or PCR test? In this project, we are trying to answer these and similar questions with the help of statistical analyses, Monte Carlo simulations and decision-theoretical approaches in a hospital environment, e.g. in an endoscopic unit. Various integrated key figures from a medical perspective, such as sensitivity or the number of additional infections, as well as from a business perspective, such as cost aspects, are used for evaluation.

Cooperation partners:

• Prof. Dr. Brunner (Faculty of Economics, Schools of Medicine)
• Prof. Dr. Messmann (University Hospital Augsburg)

Publications:

Ebigbo A, Römmele C, Bartenschlager CC, Temizel S, Kling E, Brunner JO, Messmann H (2020): Cost-effectiveness analysis of SARS-CoV-2 infection-prevention strategies including pre-endoscopic virus testing and use of high-risk personal protective equipment. Endoscopy.

Kahn M, Schuierer L, Bartenschlager CC, Zellmer S, Frey R, Freitag M, Dhillon C, Heier M, Ebigbo A, Denzel C, Temizel S, Messmann H, Wehler M, Hoffmann R, Kling E, Römmele C (2021): Performance of antigen testing for diagnosis of COVID-19 - a direct comparison of a lateral flow device to nucleic acid amplification based tests. Accepted for publication in BMC Infectious Diseases.