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

ABSTRACT:

Background

A substantial proportion of attendances to ophthalmic emergency departments are for non-urgent presentations. We developed and evaluated a machine learning system (DemDx Ophthalmology Triage System: DOTS) to optimise triage, with the aim of reducing inappropriate emergency attendances and streamlining case referral when necessary.

Methods

DOTS was built using retrospective tabular data from 11,315 attendances between July 1st, 2021, to June 15th, 2022 at Moorfields Eye Hospital Emergency Department (MEH) in London, UK. Demographic and clinical features were used as inputs and a triage recommendation was given ("see immediately", "see within a week", or "see electively"). DOTS was validated temporally and compared with triage nurses' performance (1269 attendances a

SUBMITTER: Brandao-de-Resende C 

PROVIDER: S-EPMC10711497 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature