Prevalence of electronic screening for sepsis in National Health Service acute hospitals in England
Sepsis is a worldwide public health problem. Rapid identification is associated with improved patient outcomes—if followed by timely appropriate treatment.ObjectivesDescribe digital sepsis alerts (DSAs) in use in English National Health Service (NHS) acute hospitals.MethodsA Freedom of Information r...
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Veröffentlicht in: | BMJ health & care informatics 2023-05, Vol.30 (1), p.e100743 |
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Zusammenfassung: | Sepsis is a worldwide public health problem. Rapid identification is associated with improved patient outcomes—if followed by timely appropriate treatment.ObjectivesDescribe digital sepsis alerts (DSAs) in use in English National Health Service (NHS) acute hospitals.MethodsA Freedom of Information request surveyed acute NHS Trusts on their adoption of electronic patient records (EPRs) and DSAs.ResultsOf the 99 Trusts that responded, 84 had an EPR. Over 20 different EPR system providers were identified as operational in England. The most common providers were Cerner (21%). System C, Dedalus and Allscripts Sunrise were also relatively common (13%, 10% and 7%, respectively). 70% of NHS Trusts with an EPR responded that they had a DSA; most of these use the National Early Warning Score (NEWS2). There was evidence that the EPR provider was related to the DSA algorithm. We found no evidence that Trusts were using EPRs to introduce data driven algorithms or DSAs able to include, for example, pre-existing conditions that may be known to increase risk.Not all Trusts were willing or able to provide details of their EPR or the underlying algorithm.DiscussionThe majority of NHS Trusts use an EPR of some kind; many use a NEWS2-based DSA in keeping with national guidelines.ConclusionMany English NHS Trusts use DSAs; even those using similar triggers vary and many recreate paper systems. Despite the proliferation of machine learning algorithms being developed to support early detection of sepsis, there is little evidence that these are being used to improve personalised sepsis detection. |
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ISSN: | 2632-1009 2632-1009 |
DOI: | 10.1136/bmjhci-2023-100743 |