Predicting patient deterioration with physiological data using AI: systematic review protocol
Introduction The second iteration of the National Early Warning Score has been adopted widely within the UK and internationally. It uses routinely collected physiological measurements to standardise the assessment and response to acute illness. Its use is associated with reduced mortality but has li...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2025-08-01
|
| Series: | BMJ Health & Care Informatics |
| Online Access: | https://informatics.bmj.com/content/32/1/e101417.full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849395784310587392 |
|---|---|
| author | Chris Plummer Edward Meinert Victoria Riccalton Lynsey Threlfall Cen Cong |
| author_facet | Chris Plummer Edward Meinert Victoria Riccalton Lynsey Threlfall Cen Cong |
| author_sort | Chris Plummer |
| collection | DOAJ |
| description | Introduction The second iteration of the National Early Warning Score has been adopted widely within the UK and internationally. It uses routinely collected physiological measurements to standardise the assessment and response to acute illness. Its use is associated with reduced mortality but has limited positive and negative predictive accuracy. There is a growing body of research demonstrating the effectiveness of artificial intelligence (AI) in predicting clinical deterioration, but there is limited evidence to show which aspect of AI is best suited to this task. This systematic review aims to establish which AI or machine learning algorithm is best suited to analysing physiological data sets to predict patient deterioration in a hospital setting.Methods and analysis A systematic review will be conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) and the PICOS (Population, Intervention, Comparator, Outcome and Study) frameworks. Eight databases (PubMed, Embase, CINAHL, Cochrane Library, Web of Science, Scopus, IEEE Xplore and ACM Digital Library) will be used to search for studies published from 2007 to the present that meet the inclusion criteria. Two reviewers will screen the studies identified and extract data independently, with any discrepancies resolved by discussion. The review is expected to be completed by January 2026, and the results will be presented in publication by June 2026.Ethics and dissemination Ethical approval is not required as data will be obtained from published sources. Findings from this study will be disseminated via publication in a peer-reviewed journal. |
| format | Article |
| id | doaj-art-41a1757e7dfc43328ff8699f0c5d382d |
| institution | Kabale University |
| issn | 2632-1009 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Health & Care Informatics |
| spelling | doaj-art-41a1757e7dfc43328ff8699f0c5d382d2025-08-20T03:39:31ZengBMJ Publishing GroupBMJ Health & Care Informatics2632-10092025-08-0132110.1136/bmjhci-2024-101417Predicting patient deterioration with physiological data using AI: systematic review protocolChris Plummer0Edward Meinert1Victoria Riccalton2Lynsey Threlfall3Cen Cong4Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK6 Faculty of Life Sciences and Medicine, King’s College London, London, UKTranslational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE1 7RU, UKTranslational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE1 7RU, UKTranslational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE1 7RU, UKIntroduction The second iteration of the National Early Warning Score has been adopted widely within the UK and internationally. It uses routinely collected physiological measurements to standardise the assessment and response to acute illness. Its use is associated with reduced mortality but has limited positive and negative predictive accuracy. There is a growing body of research demonstrating the effectiveness of artificial intelligence (AI) in predicting clinical deterioration, but there is limited evidence to show which aspect of AI is best suited to this task. This systematic review aims to establish which AI or machine learning algorithm is best suited to analysing physiological data sets to predict patient deterioration in a hospital setting.Methods and analysis A systematic review will be conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) and the PICOS (Population, Intervention, Comparator, Outcome and Study) frameworks. Eight databases (PubMed, Embase, CINAHL, Cochrane Library, Web of Science, Scopus, IEEE Xplore and ACM Digital Library) will be used to search for studies published from 2007 to the present that meet the inclusion criteria. Two reviewers will screen the studies identified and extract data independently, with any discrepancies resolved by discussion. The review is expected to be completed by January 2026, and the results will be presented in publication by June 2026.Ethics and dissemination Ethical approval is not required as data will be obtained from published sources. Findings from this study will be disseminated via publication in a peer-reviewed journal.https://informatics.bmj.com/content/32/1/e101417.full |
| spellingShingle | Chris Plummer Edward Meinert Victoria Riccalton Lynsey Threlfall Cen Cong Predicting patient deterioration with physiological data using AI: systematic review protocol BMJ Health & Care Informatics |
| title | Predicting patient deterioration with physiological data using AI: systematic review protocol |
| title_full | Predicting patient deterioration with physiological data using AI: systematic review protocol |
| title_fullStr | Predicting patient deterioration with physiological data using AI: systematic review protocol |
| title_full_unstemmed | Predicting patient deterioration with physiological data using AI: systematic review protocol |
| title_short | Predicting patient deterioration with physiological data using AI: systematic review protocol |
| title_sort | predicting patient deterioration with physiological data using ai systematic review protocol |
| url | https://informatics.bmj.com/content/32/1/e101417.full |
| work_keys_str_mv | AT chrisplummer predictingpatientdeteriorationwithphysiologicaldatausingaisystematicreviewprotocol AT edwardmeinert predictingpatientdeteriorationwithphysiologicaldatausingaisystematicreviewprotocol AT victoriariccalton predictingpatientdeteriorationwithphysiologicaldatausingaisystematicreviewprotocol AT lynseythrelfall predictingpatientdeteriorationwithphysiologicaldatausingaisystematicreviewprotocol AT cencong predictingpatientdeteriorationwithphysiologicaldatausingaisystematicreviewprotocol |