Automatic generation of natural language nursing shift summaries in neonatal intensive care: BT-Nurse

James Hunter, Yvonne Freer, Albert Gatt, Ehud Reiter, Somayajulu Sripada, Cindy Sykes

Research output: Contribution to journalArticlepeer-review

57 Citations (Scopus)


Our objective was to determine whether and how a computer system could automatically generate helpful natural language nursing shift summaries solely from an electronic patient record system, in a neonatal intensive care unit (NICU).

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.

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.

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.
Original languageEnglish
Pages (from-to)157-172
Number of pages16
JournalArtificial Intelligence in Medicine
Issue number3
Early online date12 Oct 2012
Publication statusPublished - Nov 2012


  • natural language generation
  • natural language processing
  • data to text
  • neonatal intensive care
  • health informatics


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