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Title: Automatic generation of natural language nursing shift summaries in neonatal intensive care : BT-Nurse
Authors: Hunter, James
Freer, Yvonne
Gatt, Albert
Reiter, Ehud
Sripada, Somayajulu
Sykes, Cindy
Keywords: Natural language processing (Computer science)
Neonatal intensive care
Medical informatics
Issue Date: 2012
Publisher: Elsevier BV
Citation: Hunter, J., Freer, Y., Gatt, A., Reiter, E., Sripada, S., & Sykes, C. (2012). Automatic generation of natural language nursing shift summaries in neonatal intensive care: BT-Nurse. Artificial Intelligence in Medicine, 56(3), 157-172.
Abstract: automatically generate helpful natural language nursing shift summaries solely from an electronic patient record system, in a neonatal intensive care unit (NICU). Methods: A system was developed which automatically generates partial NICU shift summaries (for the respiratory and cardiovascular systems), using data-to-text technology. It was evaluated for 2 months in the NICU at the Royal Infirmary of Edinburgh, under supervision. Results: In an on-ward evaluation, a substantial majority of the summaries was found by outgoing and incoming nurses to be understandable (90%), and a majority was found to be accurate (70%), and helpful (59%). The evaluation also served to identify some outstanding issues, especially with regard to extra content the nurses wanted to see in the computer-generated summaries. Conclusions: It is technically possible automatically to generate limited natural language NICU shift summaries from an electronic patient record. However, it proved difficult to handle electronic data that was intended primarily for display to the medical staff, and considerable engineering effort would be required to create a deployable system from our proof-of-concept software.
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