'Errors' and omissions in paper-based early warning scores: The association with changes in vital signs-a database analysis

This data was imported from PubMed:

Authors: Clifton, D.A., Clifton, L., Sandu, D.-M., Smith, G.B., Tarassenko, L., Vollam, S.A. and Watkinson, P.J.

Journal: BMJ Open

Volume: 5

Issue: 7

Pages: e007376

eISSN: 2044-6055

DOI: 10.1136/bmjopen-2014-007376

OBJECTIVES: To understand factors associated with errors using an established paper-based early warning score (EWS) system. We investigated the types of error, where they are most likely to occur, and whether 'errors' can predict subsequent changes in patient vital signs. METHODS: Retrospective analysis of prospectively collected early warning system database from a single large UK teaching hospital. RESULTS: 16,795 observation sets, from 200 postsurgical patients, were collected. Incomplete observation sets were more likely to contain observations which should have led to an alert than complete observation sets (15.1% vs 7.6%, p<0.001), but less likely to have an alerting score correctly calculated (38.8% vs 30.0%, p<0.001). Mis-scoring was much more common when leaving a sequence of three or more consecutive observation sets with aggregate scores of 0 (55.3%) than within the sequence (3.0%, p<0.001). Observation sets that 'incorrectly' alerted were more frequently followed by a correctly alerting observation set than error-free non-alerting observation sets (14.7% vs 4.2%, p<0.001). Observation sets that 'incorrectly' did not alert were more frequently followed by an observation set that did not alert than error-free alerting observation sets (73.2% vs 45.8%, p<0.001). CONCLUSIONS: Missed alerts are particularly common in incomplete observation sets and when a patient first becomes unstable. Observation sets that 'incorrectly' alert or 'incorrectly' do not alert are highly predictive of the next observation set, suggesting that clinical staff detect both deterioration and improvement in advance of the EWS system by using information not currently encoded within it. Work is urgently needed to understand how best to capture this information.

This data was imported from Scopus:

Authors: Clifton, D.A., Clifton, L., Sandu, D.M., Smith, G.B., Tarassenko, L., Vollam, S.A. and Watkinson, P.J.

Journal: BMJ Open

Volume: 5

Issue: 7

eISSN: 2044-6055

DOI: 10.1136/bmjopen-2014-007376

Objectives: To understand factors associated with errors using an established paper-based early warning score (EWS) system. We investigated the types of error, where they are most likely to occur, and whether 'errors' can predict subsequent changes in patient vital signs. Methods: Retrospective analysis of prospectively collected early warning system database from a single large UK teaching hospital. Results: 16 795 observation sets, from 200 postsurgical patients, were collected. Incomplete observation sets were more likely to contain observations which should have led to an alert than complete observation sets (15.1% vs 7.6%, p<0.001), but less likely to have an alerting score correctly calculated (38.8% vs 30.0%, p<0.001). Mis-scoring was much more common when leaving a sequence of three or more consecutive observation sets with aggregate scores of 0 (55.3%) than within the sequence (3.0%, p<0.001). Observation sets that 'incorrectly' alerted were more frequently followed by a correctly alerting observation set than error-free nonalerting observation sets (14.7% vs 4.2%, p<0.001). Observation sets that 'incorrectly' did not alert were more frequently followed by an observation set that did not alert than error-free alerting observation sets (73.2% vs 45.8%, p<0.001). Conclusions: Missed alerts are particularly common in incomplete observation sets and when a patient first becomes unstable. Observation sets that 'incorrectly' alert or 'incorrectly' do not alert are highly predictive of the next observation set, suggesting that clinical staff detect both deterioration and improvement in advance of the EWS system by using information not currently encoded within it. Work is urgently needed to understand how best to capture this information.

This data was imported from Web of Science (Lite):

Authors: Clifton, D.A., Clifton, L., Sandu, D.-M., Smith, G.B., Tarassenko, L., Vollam, S.A. and Watkinson, P.J.

Journal: BMJ OPEN

Volume: 5

Issue: 7

ISSN: 2044-6055

DOI: 10.1136/bmjopen-2014-007376

This data was imported from Europe PubMed Central:

Authors: Clifton, D.A., Clifton, L., Sandu, D.M., Smith, G.B., Tarassenko, L., Vollam, S.A. and Watkinson, P.J.

Journal: BMJ open

Volume: 5

Issue: 7

Pages: e007376

eISSN: 2044-6055

To understand factors associated with errors using an established paper-based early warning score (EWS) system. We investigated the types of error, where they are most likely to occur, and whether 'errors' can predict subsequent changes in patient vital signs.Retrospective analysis of prospectively collected early warning system database from a single large UK teaching hospital.16,795 observation sets, from 200 postsurgical patients, were collected. Incomplete observation sets were more likely to contain observations which should have led to an alert than complete observation sets (15.1% vs 7.6%, p<0.001), but less likely to have an alerting score correctly calculated (38.8% vs 30.0%, p<0.001). Mis-scoring was much more common when leaving a sequence of three or more consecutive observation sets with aggregate scores of 0 (55.3%) than within the sequence (3.0%, p<0.001). Observation sets that 'incorrectly' alerted were more frequently followed by a correctly alerting observation set than error-free non-alerting observation sets (14.7% vs 4.2%, p<0.001). Observation sets that 'incorrectly' did not alert were more frequently followed by an observation set that did not alert than error-free alerting observation sets (73.2% vs 45.8%, p<0.001).Missed alerts are particularly common in incomplete observation sets and when a patient first becomes unstable. Observation sets that 'incorrectly' alert or 'incorrectly' do not alert are highly predictive of the next observation set, suggesting that clinical staff detect both deterioration and improvement in advance of the EWS system by using information not currently encoded within it. Work is urgently needed to understand how best to capture this information.

The data on this page was last updated at 04:55 on June 17, 2019.