  Adapting pretrained language models to novel domains, such as clinical
applications, traditionally involves retraining their entire set of parameters.
Parameter-Efficient Fine-Tuning (PEFT) techniques for fine-tuning language
models significantly reduce computational requirements by selectively
fine-tuning small subsets of parameters. In this study, we propose a two-step
PEFT framework and evaluate it in the clinical domain. Our approach combines a
specialised PEFT adapter layer designed for clinical domain adaptation with
another adapter specialised for downstream tasks. We evaluate the framework on
multiple clinical outcome prediction datasets, comparing it to clinically
trained language models. Our framework achieves a better AUROC score averaged
across all clinical downstream tasks compared to clinical language models. In
particular, we observe large improvements of 4-5% AUROC in large-scale
multilabel classification tasks, such as diagnoses and procedures
classification. To our knowledge, this study is the first to provide an
extensive empirical analysis of the interplay between PEFT techniques and
domain adaptation in an important real-world domain of clinical applications.
