Natural language word embeddings as a glimpse into healthcare language and associated mortality surrounding end of life

Objectives To clarify real-world linguistic nuances around dying in hospital as well as inaccuracy in individual-level prognostication to support advance care planning and personalised discussions on limitation of life sustaining treatment (LST). Design Retrospective cross-sectional study of real-world clinical data. Setting Secondary care, urban and suburban teaching hospitals. Participants All inpatients in 12-month period from 1 October 2018 to 30 September 2019. Methods Using unsupervised natural language processing, word embedding in latent space was used to generate phrase clusters with most similar semantic embeddings to 'Ceiling of Treatment' and their prognostication value. Results Word embeddings with most similarity to 'Ceiling of Treatment' clustered around phrases describing end-of-life care, ceiling of care and LST discussions. The phrases have differing prognostic profile with the highest 7-day mortality in the phrases most explicitly referring to end of life-'Withdrawal of care' (56.7%), 'terminal care/end of life care' (57.5%) and 'un-survivable' (57.6%). Conclusion Vocabulary used at end-of-life discussions are diverse and has a range of associations to 7-day mortality. This highlights the importance of correct application of terminology during LST and end-of-life discussions.

Automated summary

This study aimed to explore linguistic nuances around dying in hospital and the accuracy of prognostication, highlighting the importance of terminology in discussions about limitation of life-sustaining treatment and end-of-life care. The findings showed diverse vocabulary used in these discussions, with varying associations to 7-day mortality, emphasizing the need for correct application of terminology for effective communication.