A machine learning system to optimise triage in an adult ophthalmic emergency department: a model development and validation study

Brandao-de-Resende, Camilo; Melo, Mariane; Lee, Elsa; Jindal, Anish; Neo, Yan N.; Sanghi, Priyanka; Freitas, Joao R.; Castro, Paulo V.I.P.; Rosa, Victor O.M.; Valentim, Guilherme F.S.; Higino, Maria Luisa O.; Hay, Gordon R.; Keane, Pearse A.; Vasconcelos-Santos, Daniel V.; Day, Alexander C.

doi:10.1016/j.eclinm.2023.102331

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…On the other hand, prehospital triage has also been postulated and assessed in scientific projects [10][11][12][13], as well as using machine learning models to optimize care in emergency services e.g., chest pain, trauma brain injuries, or ophthalmic problems [14][15][16]. The results of the mentioned solutions have increasingly strong evidence based in science, but further studies are required [17].…”

Section: Introductionmentioning

confidence: 99%

Drinking from the Holy Grail—Does a Perfect Triage System Exist? And Where to Look for It?

Ingielewicz,

Rychlik,

Sieminski

2024

JPM

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The Emergency Department (ED) is a facility meant to treat patients in need of medical assistance. The choice of triage system hugely impactsed the organization of any given ED and it is important to analyze them for their effectiveness. The goal of this review is to briefly describe selected triage systems in an attempt to find the perfect one. Papers published in PubMed from 1990 to 2022 were reviewed. The following terms were used for comparison: “ED” and “triage system”. The papers contained data on the design and function of the triage system, its validation, and its performance. After studies comparing the distinct means of patient selection were reviewed, they were meant to be classified as either flawed or non-ideal. The validity of all the comparable segregation systems was similar. A possible solution would be to search for a new, measurable parameter for a more accurate risk estimation, which could be a game changer in terms of triage assessment. The dynamic development of artificial intelligence (AI) technologies has recently been observed. The authors of this study believe that the future segregation system should be a combination of the experience and intuition of trained healthcare professionals and modern technology (artificial intelligence).

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Section: Introductionmentioning

confidence: 99%

Drinking from the Holy Grail—Does a Perfect Triage System Exist? And Where to Look for It?

Ingielewicz,

Rychlik,

Sieminski

2024

JPM

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Enhancing Ophthalmic Triage: identification of new clinical features to support healthcare professionals in triage

Jindal,

Brandao-de-Resende,

Neo

et al. 2024

Eye

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Objective: To investigate which features from a patient's history are either high or low risk concerning ocular emergencies, which could be seamlessly integrated into the training of healthcare professionals in order to facilitate e cient ophthalmic triage. MethodsProspective, 12,584 visits from 11,733 adult patients attending an Accident and Emergency department at a single tertiary centre were analysed. Data was collected by ophthalmic nurses working in triage between August 2021 and April 2022 using an online form. Multivariate analysis (MVA) was conducted to identify which features from the patients' history would be associated with urgent care. ResultsThere were 5731 (45.5%) patients were appropriate for same day eye emergency examination (SDEE), 1416 (11.3%) urgent care and 5437 (43.2%) elective (GP/optometrist). The MVA top ten features that were statistically signi cant (p<0.05) that would warrant SDEE with odds ratio (95% CI) were: bilateral eye injury 36.5 [15.6-85.5], unilateral eye injury 25.8 [20.9-31.7], vision loss 4.8 [2.9-7.8], post-operative ophthalmic (<4 weeks) 4.6 [3.8-5.7], contact lens wearer 3.9 [3.3-4.7], history of uveitis 3.9 [3.3-4.7], photophobia 2.9 [2.4-3.6], unilateral dark shadow/curtain in vision 2.4 [1.8-3.0], unilateral injected red eye 2.0 [1.8-2.2] and rapid change in visual acuity 1.8 [1.5-2.2]. ConclusionThis comprehensive study covered almost 100 ophthalmic acute presentations and identi ed features that can be readily incorporated into triage and healthcare professional training, which could facilitate appropriate pathway selection for patients. Further research is required to evaluate the real-world impact application of these ndings on patient attendance in both primary and secondary care.res from a patient's history are either high or low risk concerning ocular emergencies, which could be seamlessly integrated into the training of healthcare professionals in order to facilitate e cient ophthalmic triage.

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Usability of an artificially intelligence-powered triage platform for adult ophthalmic emergencies: a mixed methods study

Jindal,

Sumodhee,

Brandao-de-Resende

et al. 2023

Sci Rep

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There is growing demand for emergency-based eyecare services where the majority of those attending do not require urgent ophthalmic management. The Royal College of Ophthalmologists have recommended upskilling and supporting of allied health professionals to support eyecare delivery, where machine learning algorithms could help. A mixed methods study was conducted to evaluate the usability of an artificial intelligence (AI) powered online triage platform for ophthalmology. The interface, usability, safety and acceptability were investigated using a Think Aloud interview and usability questionnaires. Twenty participants who actively examine patients in ophthalmic triage within a tertiary eye centre or primary care setting completed the interview and questionnaires. 90% or more of participants found the platform easy to use, reflected their triage process and were able to interpret the triage outcome, 85% found it safe to use and 95% felt the processing time was fast. A quarter of clinicians reported that they have experienced some uncertainty when triaging in their career and were unsure of using AI, after this study 95% of clinicians were willing to use the platform in their clinical workflow. This study showed the platform interface was acceptable and usable for clinicians actively working in ophthalmic emergency triage.

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A machine learning system to optimise triage in an adult ophthalmic emergency department: a model development and validation study

Cited by 3 publications

References 13 publications

Drinking from the Holy Grail—Does a Perfect Triage System Exist? And Where to Look for It?

Drinking from the Holy Grail—Does a Perfect Triage System Exist? And Where to Look for It?

Enhancing Ophthalmic Triage: identification of new clinical features to support healthcare professionals in triage

Usability of an artificially intelligence-powered triage platform for adult ophthalmic emergencies: a mixed methods study

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