Clinical handover refers to healthcare workers transferring responsibility and accountability for patient care, e.g., between shifts or wards. Safety and quality health standards call for this process to be systematically structured across the organisation and synchronous with its documentation. This paper evaluates information extraction as a way to help comply with these standards. It implements the handover process of first specifying a structured handover form, whose hierarchy of headings guides the handover narrative, followed by the technology filling it out objectively and almost instantly for proofing and sign-off. We trained a conditional random field with 8 feature types on 101 expert-annotated documents to 36-class classify. This resulted in good generalization to an independent set of 50 validation and 50 test documents that we now release: 77.9% F1 in filtering out irrelevant information, up to 98.4% F1 for the 35 classes for relevant information, and 52.9% F1 after macro-averaging over these 35 classes, whilst these percentages were 86.2, 100.0, and 70.2 for the leave-one-document-out cross-validation across the first set of 101 documents. Also as a result of this study, the validation and test data were released to support further research.
Zhou, L., & Suominen, H. (2015). Information extraction to improve standard compliance: The case of clinical handover. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9457, pp. 644–649). Springer Verlag. https://doi.org/10.1007/978-3-319-26350-2_57