Can AI support rural and urban primary care? A nationwide survey of German physicians
K Förster, L Maass, G Schmiemann, S Forberger, M Westphal, A ReinschlüsselAbstract
Primary care systems across Europe face increasing workforce shortages, particularly in rural areas, threatening equitable access to care. Digitalisation and artificial intelligence (AI) are frequently proposed as potential solutions to support healthcare delivery, yet empirical evidence on their current use and perceived impact in routine primary care remains limited. This study aims to assess how general practitioners (GPs) in Germany use and evaluate AI-based tools in clinical and administrative practice, and to examine their perceived contribution to workload and service delivery. A nationwide survey will be conducted among approximately 55,000 GPs, enabling subgroup analyses by geographic location, practice characteristics and sociodemographic factors. AI use is assessed through structured self-report measures distinguishing between administrative and clinical applications. In addition, perceived effects on workforce capacity are captured, including changes in workload, time use and perceived relief or burden. The study further explores differences between rural and urban settings to assess potential implications for health equity. Building on existing evidence from systematic reviews on AI in primary care, the analysis will identify patterns of adoption, perceived benefits and barriers to implementation. Particular attention is given to whether digital tools are associated with perceived improvements in efficiency and whether these effects differ across contexts. By providing the first nationwide overview of AI use in German primary care, this study contributes to understanding the role of digitalisation in addressing workforce shortages and maintaining access to care. The findings will inform policy development on the equitable and sustainable integration of AI in primary healthcare systems, highlighting conditions under which digital tools may support or risk exacerbating existing inequalities.