Researchers from FAU and other research institutions from the states of North Rhine-Westphalia and Rhineland-Palatinate are investigating how to ensure and improve medical care in rural areas during the coronavirus pandemic in a model project funded by the BMBF. Mathematically-based optimisation and decision-making systems are intended to support ambulance services, emergency pharmacy services and setting up vaccination centres.
Outpatient medical care, especially in rural areas, is facing major challenges due to an ageing society, the centralisation of medical facilities and dwindling resources. Since 2017, a consortium of researchers from FAU, RWTH Aachen University, TU Kaiserlautern and the Fraunhofer Institute ITWM has been investigating how emergency pharmacy services, emergency medical locations and ambulance services can be better planned in the BMBF-funded project ‘Health: Facility Location, Covering, and Transport’ (HealthFaCT). Researchers are developing algorithm-based decision-making systems in this project to support control centres, health authorities and pharmacy associations. ‘The optimisation models must be practical and ensure fair access to medical care for citizens,’ says Prof. Dr. Frauke Liers from the Department of Data Science at FAU, who is coordinating the collaborative project. A joint manuscript was recently submitted for the project.
Medical care during the coronavirus pandemic
The importance of carefully planned medical care has been demonstrated in recent months during the coronavirus pandemic. ‘Increased numbers of patients, special hygiene and protection measures and compensating for personnel shortages due to quarantine measures and illnesses in hospital and care facilities have exacerbated the situation even further,’ explains Liers. Several months ago, in an effort to alleviate some of the pressure caused by these difficult conditions, the BMBF granted the HealthFaCT project extra funding in a follow-up project: HealthFaCT Cor(onavirus).
One of the research priorities in this project is safeguarding the supply of medicines, which proved to be a challenge even before the coronavirus pandemic: The demand for medicines in an ageing society is increasing, while pharmacy density, which was already low, is likely to decrease even further. Optimisation models for fair emergency planning and site planning have been developed to prevent gaps in supply. The pandemic now requires additional challenges to be addressed – from quarantine-related staff shortages to the unpredictable closure of pharmacies.
HealthFaCT-Cor aims to ensure the 24-hour supply of medicines and to avoid overloading pharmacists. This is to be achieved by allocating emergency pharmacy services to pharmacies in a way which does not threaten local supply of the population or create excessive demand for the remaining pharmacies if some have to close. The Aachen researchers were supported by the pharmacy association Apothekerkammer Nordrhein.
A completely new challenge in the coronavirus pandemic is the planning of vaccination centres, which researchers from Kaiserslautern started in autumn 2020 – even before vaccines had been approved. With support from the Robert Koch Institute, the researchers adapted the strategic planning of emergency medical locations from the previous project. The result was a sound mathematical model for planning vaccine distribution which shows good compromises regarding travelling distance for vaccine recipients and required locations.
Transportation of infected and uninfected patients
FAU’s contribution to the collaborative project is the optimisation of ambulance services. ‘The particular difficulty in this area lies in the fact that emergencies lead to delays in scheduled transports,’ explains Frauke Liers. ‘These delays can be problematic, for example when appointments for surgery or dialysis are scheduled.’ In cooperation with the Integrated Nuremberg Control Centre and using historical operational data, researchers at FAU have developed stable algorithms that can be used for planning and significantly reduce patient waiting times compared to historical data.
For ambulance planning under pandemic conditions, infections models were used and ambulances were divided into pools: those intended exclusively for patients with known Covid-19 infections and those transporting all other patients. A limited number of ambulances were allocated flexibly to pools. The advantage of this strategy is that not all ambulances need to be equipped with several sets of protective equipment. Researchers also considered common scenarios in the model such as the increased need for ambulances after ward visits.
The results of HealthFaCT and HealthFaCT-Cor are expected to make a valuable contribution both to daily planning in the healthcare sector and in crisis management.