Shape the Future of Swedish Healthcare with AI-Technology How to Implement Large Language Models as a Tool to Streamline Clinicians' Administrative Tasks
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Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
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Sammanfattning
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.
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Ämne/nyckelord
large language models, artificial intelligence, healthcare, administration, text-based tasks, patient-related