Digital alerting to improve sepsis detection and patient outcomes in NHS Trusts: a multi-methods study
Notice bibliographique
Résumé
Background: Identifying clinical deterioration is a global health priority. Sepsis is a leading cause of deterioration, responsible for around 46,000 deaths annually in the United Kingdom. Early warning scores based on patients' vital signs can be embedded into electronic patient records to digitally alert clinicians to those at risk. Rapid identification and treatment - particularly with targeted intravenous antibiotics - are critical to improving outcomes in sepsis patients. Research question: This study aimed to evaluate the effectiveness of digital alerts in improving outcomes for patients with sepsis. Using routine electronic patient record data from four United Kingdom National Health Service acute trusts, we investigated how digital alert systems influence patient outcomes and explored mechanisms and mediators of their effectiveness. Objectives: Map the types of digital alerts currently in use across United Kingdom hospitals for identifying patients at risk of sepsis (Workstream 1). Evaluate the impact of digital alerts on patient outcomes (Workstream 2). Examine how the implementation process affects alert performance, guided by the consolidated framework for implementation research (Workstream 3). Provide recommendations on alert effectiveness and implementation strategies using systems modelling and mediation analysis (Workstream 4). Methods: A mixed-methods approach was employed. A national survey assessed the use of digital sepsis alerts in English National Health Survey hospitals (Workstream 1). Qualitative interviews and focus groups explored the implementation process and its influence on alert performance (Workstream 3). A natural experiment with multilevel interrupted time series analysis examined the impact of sepsis screening tools and digital alerts on outcomes, primarily in-hospital mortality (Workstream 2). Routinely collected clinical data were processed following National Institute for Health Research-Health Information Collaborative standards. Combining quantitative and qualitative data enabled us to link implementation processes with patient outcomes. Results: All four trusts experienced reduced mortality rates among patients with serious infections following the introduction of digital sepsis screening tools. After adjustment for patient case-mix, admission patterns and pre-existing trends, one trust showed a statistically significant decrease in mortality linked to digital alert implementation. In two trusts, older patients experienced greater mortality reduction than younger ones following alert introduction. Qualitative findings highlighted factors contributing to more effective use of digital alerts: deployment in general wards rather than intensive care units; use by clinicians familiar with similar technologies; availability of 24/7 emergency outreach teams; robust technological infrastructure and alerts that were user-friendly, non-intrusive and not part of multiple competing alert systems. Conclusions: The effectiveness of digital sepsis screening tools varies and may depend on patient's age and care setting. Our findings suggest that digital alerts should leverage a wider range of electronic patient record data and be tailored to specific patient groups. Different trusts and patient populations may require distinct indicators, thresholds and treatment protocols. These findings align with healthcare practitioners' calls for more sophisticated, patient-centred sepsis screening tools targeted at relevant clinical teams. Future work and limitations: The study involved four National Health Service Trusts with strong data collaboration, but noted limitations include reliance on simple algorithms and varied case-mix and implementation processes. Future research should focus on robust evaluation methods, leveraging granular electronic patient record data and establishing a public registry of digital alert tools. Funding: This synopsis presents independent research funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme as award number NIHR129082.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,031 | 0,064 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,005 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».