Automatic generation of natural language nursing shift summaries in neonatal intensive care: BT-Nurse
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Introduction 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).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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Hunter et al. used natural-language generation to summarize EHR data and generate potential problems and care plans in a dynamic shift-change report in the neonatal ICU context.
“In the neonatal ICU context, Hunter et al63 used natural-language generation to summarize EHR data and generate potential problems and care plans in a dynamic shift-change report.”
From Potential uses of AI for perioperative nursing handoffs: a qualitative study (2023) · cites this paper as [63]
The abstract (p. 1) and system description (p. 2) confirm that BT-Nurse generated natural-language summaries from electronic NICU records for shift handover. The Data Interpretation section (p. 8) says it identified potential problems, but the paper does not say it generated care plans: the care-plan example is from an expert nurse’s summary (p. 2), while BT-Nurse’s summaries were intended to help nurses with care planning (Methodology, p. 12). (LLM, conf=0.89)
Experimental — accuracy not yet measuredNurse users largely accepted a prototype system for shift change in the neonatal intensive care unit which focused on summarizing data in natural language and included expert decision rules as a minor component.
“Nurse users largely accepted a prototype system for shift change in the neonatal intensive care unit which focused on summarizing data in natural language and included expert decision rules as a minor component.63”
From Potential uses of AI for perioperative nursing handoffs: a qualitative study (2023) · cites this paper as [63]
The abstract (p. 1) reports that nurses rated most generated summaries understandable, accurate and helpful, supporting a broadly positive reception of the NICU shift-handover prototype. Section 3.2.6 (p. 8) describes medical-knowledge if–then rules used in data interpretation, but the paper does not establish that expert decision rules were a minor component. The evaluation measured nurses’ ratings of partial summaries, not acceptance of a deployable system. (LLM, conf=0.82)
Experimental — accuracy not yet measuredHunter and Forbes's work suggests a distinct role for AI prediction from traditional CDS: facilitating problem-based report and assessment during handoffs.
“Hunter and Forbes's work56,63,64 suggests a distinct role for AI prediction from traditional CDS: facilitating problem-based report and assessment during handoffs.”
From Potential uses of AI for perioperative nursing handoffs: a qualitative study (2023) · cites this paper as [63]
The Conclusions (p. 15) say BT-Nurse generates shift summaries that include current status and potential problems, supporting its role in handoff reporting. The paper does not present BT-Nurse as an AI prediction system or establish a distinct predictive role from traditional clinical decision support; its Computerised shift handover section (p. 2) describes the summaries as complementing other handover communication. (LLM, conf=0.85)
Experimental — accuracy not yet measuredWorks [101,102] have demonstrated the possible uses of systems combining classical data mining techniques with natural language generation in both clinical and experimental contexts.
“Works such as [101,102] have demonstrated the possible uses of such systems in both clinical and experimental contexts.”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [102]
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Fuzzy sets across the natural language generation pipeline
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Evaluation of a Data-To-Text System for Verbalizing a Learning Analytics Dashboard
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Lying, Irony, and Default Interpretation
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Effects of Individual Differences in Working Memory on Plan Presentational Choices
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Textual summarization of events leading to health alerts
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Towards NLG for Physiological Data Monitoring with Body Area Networks
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Building a Persuasive Virtual Dietitian
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Lying, Implicating, and Presupposing
Meibauer, J. (2018) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198736578.013.14 · Cited by 10 · see citing works
MIME : NLG in pre-hospital care
Schneider, A. H., Mort, A. J., Mellish, C., Reiter, E., Wilson, P., Vaudry, P.-L., & Montréal, U. D. (2013) · Aberdeen University Research Archive (Aberdeen University) · Cited by 9 · see citing works
A Study on Automatic Generation of Chinese Discharge Summary
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Nursing clinical handover in neonatal care
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Lying and Deception in Politics
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Lying and Computational Linguistics
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Data Vocalization with CiceroDB.
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Classic Philosophical Approaches to Lying and Deception
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Development of Lying and Cognitive Abilities
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TBI-Doc: Generating Patient and Clinician Reports from Brain Imaging Data
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Linguistic Approaches to Lying and Deception
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Personality-dependent content selection in natural language generation systems
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Commentary generation for financial markets
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Deep Multi-Objective Learning from Low-Dose CT for Automatic Lung-RADS Report Generation
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Vocalizing large time series efficiently
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Narrative Generation from Extracted Associations
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MIME- NLG Support for Complex and Unstable Pre-hospital Emergencies
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Machine learning model for temporal pattern recognition
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Lying, Testimony, and Epistemic Vigilance
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Understanding the Challenges Nurses Encounter with Monitoring Technologies in a NICU
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Lying, Metaphor, and Hyperbole
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A framework for generating anomaly analysis comments in DHI interpretation report
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Psycholinguistic Approaches to Lying and Deception
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Lying, Deception, and the Brain
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Generative AI in health sciences education and practice. Part 3: supporting written communication
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Automatic Reports from Spreadsheets: Data Analysis for the Rest of Us
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Fuzzy-Based Language Grounding of Geographical References: From Writers to Readers
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- Authors
- Hunter, J., Freer, Y., Gatt, A., Reiter, E., Sripada, S., & Sykes, C.
- Affiliations
- University of Aberdeen (GB)
- Venue
- Artificial Intelligence in Medicine
- Publisher
- Elsevier BV
- Published
- 2012-11
- Volume
- 56
- Issue
- 3
- Pages
- 157-172
- ISSN
- 0933-3657 (ISSN-L)
- OpenAlex
- W2154568233
- Cited by
- 127
- References
- 92
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 6 · 2023: 5 · 2022: 7 · 2021: 3 · 2020: 5 · 2019: 7 · 2018: 53 · 2017: 3 · 2016: 10 · 2015: 10 · 2014: 4 · 2013: 10
- FWCI
- 38.93
- Type
- journal article
- Language
- en
- Open access
- green
- www.um.edu.mt
- Licence
- Locations
- Artificial Intelligence in Medicine (publishedVersion)
- PubMed (publishedVersion)
- OAR@UM (University of Malta) (submittedVersion, other-oa)
- Funders
- Engineering and Physical Sciences Research Council (EP/D049520/1, EP/D05057X/1)
- Topics
- Health Sciences › Nursing › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- text generation, neonatal intensive care, electronic health records, clinical information systems, nursing documentation
- MeSH terms
- Communication, Humans, Information Systems, Intensive Care Units, Neonatal, Natural Language Processing, Nursing Staff, Hospital, Electronic Health Records, Patient Handoff
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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