ArticleBMJ health & care informatics2026
Enhancing the accuracy of a multivariable prediction model to identify medical patients suitable for same day emergency care services.
Article in BMJ health & care informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Article
- Point-of-care diagnostics are central to acute medicine delivery.Future healthcare journal · 2026Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo assess the performance of the Glasgow admission prediction score (GAPS) and ambulatory score (AmbS) for identifying emergency department (ED) attendances suitable for medical same day emergency care (SDEC) services and to derive and validate a novel tool for this purpose, the SDEC Triage Tool (SDEC-T).
methodsA retrospective diagnostic study using routine healthcare data from three hospitals in a diverse urban setting (Birmingham, UK). All unplanned ED attendances by adults requiring internal medicine assessment were included. The primary outcome was suitability for SDEC, defined as discharged alive with a length of stay under 12 hours (LOS<12). The SDEC-T was derived using multivariable analysis, informed by stakeholder workshops.
results152 877 attendances were included (median age: 58 years; 54.3% female; 68.4% White ethnicity); LOS <12 was achieved in 45.0% (n=68 752). The GAPS and AmbS had moderate predictive accuracy, with areas under the receiver operating characteristic curve (AUROCs) of 0.741 (95% CI 0.738 to 0.744) and 0.733 (95% CI 0.730 to 0.736), respectively. The SDEC-T comprised elements of the GAPS, AmbS, National Early Warning Score 2 (NEWS2) and presenting complaint and achieved an AUROC of 0.850 (95% CI 0.845 to 0.854) on internal validation. Stakeholders considered the tool acceptable and suitable for deployment across settings. DISCUSSION: The SDEC-T demonstrated improved discrimination using routinely available variables, balancing predictive performance with clinical practicality. Its design supports implementation across hospitals with varying digital maturity.
conclusionsIn a diverse patient cohort, the SDEC-T outperformed existing tools for identifying patients suitable for medical SDEC services.
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