Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review

J Miles, J Turner, R Jacques, J Williams… - Diagnostic and prognostic …, 2020 - Springer
Background The primary objective of this review is to assess the accuracy of machine
learning methods in their application of triaging the acuity of patients presenting in the …

[HTML][HTML] Triage and diagnostic accuracy of online symptom checkers: systematic review

E Riboli-Sasco, A El-Osta, A Alaa, I Webber… - Journal of Medical …, 2023 - jmir.org
Background In the context of a deepening global shortage of health workers and, in
particular, the COVID-19 pandemic, there is growing international interest in, and use of …

The virtual doctor: an interactive clinical-decision-support system based on deep learning for non-invasive prediction of diabetes

S Spänig, A Emberger-Klein, JP Sowa… - Artificial intelligence in …, 2019 - Elsevier
Artificial intelligence (AI) will pave the way to a new era in medicine. However, currently
available AI systems do not interact with a patient, eg, for anamnesis, and thus are only used …

Patient characteristics, triage utilisation, level of care, and outcomes in an unselected adult patient population seen by the emergency medical services: a prospective …

C Magnusson, J Herlitz, C Axelsson - BMC Emergency Medicine, 2020 - Springer
Background Crowding in the emergency department (ED) is a safety concern, and pathways
to bypass the ED have been introduced to reduce the time to definitive care. Conversely, a …

Out-of-hours primary care in 26 European countries: an overview of organizational models

L Steeman, M Uijen, E Plat, L Huibers, M Smits… - Family …, 2020 - academic.oup.com
Background Various models exist to organize out-of-hours primary care (OOH-PC). We
aimed to provide an up-to-date overview of prevailing organizational models in the …

Explainable artificial intelligence (XAI) in medical decision systems (MDSSs): Healthcare systems perspective

The healthcare sector is very interested in machine learning (ML) and artificial intelligence
(AI). Nevertheless, applying AI applications in scientific contexts is difficult due to …

Self-referred walk-in patients in the emergency department–who and why? Consultation determinants in a multicenter study of respiratory patients in Berlin, Germany

F Holzinger, S Oslislo, M Möckel, L Schenk… - BMC health services …, 2020 - Springer
Background Emergency department (ED) consultations are on the rise, and frequently
consultations by non-urgent patients have been held accountable. Self-referred walk-in …

Patient compliance with NHS 111 advice: analysis of adult call and ED attendance data 2013–2017

J Lewis, T Stone, R Simpson, R Jacques, C O'Keeffe… - PloS one, 2021 - journals.plos.org
The NHS 111 telephone advice and triage service is a vital part of the management of
urgent and emergency care (UEC) services in England. Demand for NHS 111 advice has …

Use of urgent, emergency and acute care by mental health service users: A record-level cohort study

J Lewis, S Weich, C O'Keeffe, T Stone, J Hulin, N Bell… - Plos one, 2023 - journals.plos.org
Background People with serious mental illness experience worse physical health and
greater mortality than the general population. Crude rates of A&E attendance and acute …

[HTML][HTML] Develo** an alternative care pathway for emergency ambulance responses for adults with epilepsy: A Discrete Choice Experiment to understand which …

E Holmes, P Dixon, A Mathieson, L Ridsdale… - … : European Journal of …, 2024 - Elsevier
Introduction To identify service users' preferences for an alternative care pathway for adults
with epilepsy presenting to the ambulance service. Methods Extensive formative work …