Shape the Future of Swedish Healthcare with AI-Technology How to Implement Large Language Models as a Tool to Streamline Clinicians' Administrative Tasks

Abstract

The demand for healthcare is continuously increasing in line with an ageing population. It is a recognized problem that clinicians have a significantly high administrative workload as a consequence of digitalization, which takes time away from valuable direct patient care. Clinicians perform multiple text-based administrative tasks, and it can be argued that large language models (LLMs) have potential to streamline these tasks. In recent years, LLMs have received great attention, led by the public introduction of ChatGPT by OpenAI in November 2022. Thus, the purpose of this study is to explore how LLMs can be used to relieve the administrative text-based workload for clinicians at Sahlgrenska University Hospital. The study is delimited to look at patient-related administrative text-based tasks performed by physicians and nurses at the neurology, ophthalmology and radiology department at Sahlgrenska University Hospital. This study is based on a strong empirical surface built upon a wide data collection of 46 semi-structured interviews where 37 have been conducted with healthcare professionals at the three mentioned departments and 9 have been conducted with 10 experts within the field of AI in healthcare. In addition, data have been collected by distributing self-completion forms at the hospital to measure the time clinicians spend on certain administrative text-based tasks, and through field notes from a number of observations at the hospital. The obtained data has been analyzed through thematic analysis. The result of the study identifies that there is a vast potential to use LLMs to streamline patient-related administrative text-based tasks in healthcare. However, there are boundaries that need to be addressed. Technological concerns have been identified due to the novelty of the technology. Ethical concerns have been identified, mainly the risk that LLMs generate biased and incorrect information, and that the information can not be validated. The three practical cases of this study clearly show that there is a need to streamline the clinicians' patient-related administrative text-based tasks. While it can be concluded that there is potential to use LLMs for this, it should be noted that it has to be further researched in a practical setting. This research further concludes that there are clear differences in the clinicians' needs across the three different departments, which adds complexity to the process of prioritizing use cases to put into practice at the hospital.