Patient-friendly Clinical Notes: Towards a new Text Simplification Dataset (2022)

Abstract: Automatic text simplification can help patients to better understand their own clinical notes. A major hurdle for the development of clinical text simplification methods is the lack of high quality resources. We report ongoing efforts in creating a parallel dataset of professionally simplified clinical notes. Currently, this corpus consists of 851 document-level simplifications of German pathology reports. We highlight characteristics of this dataset and establish first baselines for paragraph-level simplification.

## introduction many hospitals worldwide give patients access to their own clinical notes with the goal to strengthen patient autonomy and increase transparency of the care process @xcite . yet, clinical notes are seldomly written with the patient in mind. being a communication tool for doctors, clinical notes must use a precise and unambiguous medical vocabulary. with limited health literacy, these notes are therefore practically inaccessible to most patients @xcite . the urgency of making clinical notes accessible to patients is underlined by initiatives like "what's my diagnosis?" where medical doctors and students volunteer to translate patient notes into a simple language @xcite . approaches to automatic text simplification (ts) have the potential to assist with this time consuming manual process @xcite . however, there is a lack of resources to develop ts methods for clinical notes. most commonly used resources for ts include, on the one hand, professionally simplified news articles such as newsela @xcite and onestopenglish @xcite , and on the other hand, large scale but potentially noisy alignments of wikipedia @xcite . in the medical domain, datasets cover consumer health lexicons @xcite , laymen summaries of scientific articles @xcite and medical subsets of wikipedia @xcite @xcite . in addition, there is a lack of parallel document-level ts datasets (with the notable exception of newsela and onestopenglish). this makes it difficult to study document-level phenomena of ts such as sentence reordering, deletion and explanation generation @xcite @xcite . contributions. as a response to the lack of resources, we report initial results towards a parallel dataset of patient-friendly clinical notes. currently, the dataset covers 851 german surgical pathology reports with document-level simplifications by medical professionals. we describe a lightweight simplification protocol that encourages an intuitive simplification style and medical correctness, while not demanding linguistic training on the side of the editors. furthermore, we conduct first analyses of the dataset and establish baselines for automatic simplification. overall, we find that the source text is dense, with partly incomplete sentences and a highly specialized vocabulary, while manually produced simplifications are on average longer with a more constrained vocabulary. we observe a variety of simplification operations in the references including lexical substitution, syntactic simplification, explanations and content selection. we believe that this dataset can be an interesting testbed for research on document-level ts. our goal with this dataset is to increase the diversity of resources that ts research has available in terms of the text types, domains and languages. sharing this dataset with the research community while respecting ethical, legal and privacy standards is currently underway. reports typically consist of four sections (background, macroscopy, microscopy and conclusion) and each section is one input for the paragraph-level simplification model. we color-code summarization/deletion, explanation and lexical simplification/paraphrasing. for each section, we also give the rouge, bleu and sari scores. the example is continued in appendix ## dataset creation and analysis we describe our design decisions for the creation of a parallel corpus of clinical notes. an example report is given in ## simplification baselines we next establish a first baseline for pathology report simplification using paragraph-level sequenceto-sequence methods @xcite . ## conclusion and future work we present ongoing work towards a dataset of professionally simplified clinical notes. currently, the corpus consists of 851 parallel documents totaling close to 790k tokens. quantitative and qualitative analyses show potential challenges for paragraphlevel and document-level ts research. despite a moderately sized training set, fine-tuning general language models led to promising results. in future work, we will increase the size of the dataset and conduct a formal analysis of the simplification operations in the data to better understand the challenges for ts on clinical notes. human evaluations with a focus on factual correctness, as well as user studies with end-users such as patients and patient advocacy groups are also envisioned.