Safety and Efficacy of Digital Check-in and Triage Kiosks in Emergency Departments: Systematic Review
Abstract
Background: Emergency departments (EDs) globally face unprecedented pressures due to aging populations, multimorbidity, and staff shortages. In response, health systems are adopting technological solutions such as digital kiosks to reduce wait times, improve patient flow, and alleviate overcrowding. These tools can automate patient check-in and assist with triage, helping to reduce variability in assessments and identify individuals with urgent needs sooner. However, it remains unclear whether the potential time-saving benefits of these innovations translate into improved patient outcomes and safety.
Objective: This systematic review aims to summarize the safety and efficacy impacts of digital check-in and triage kiosks compared with traditional nurse-led triage methods in EDs.
Methods: Comprehensive searches were conducted in MEDLINE, EMBASE, and Web of Science. A narrative synthesis was carried out to evaluate the impact on patient safety (eg, agreement rate, accuracy, sensitivity, and specificity) and efficacy (eg, operational efficiency and patient flow). The quality of the studies was assessed using the National Heart, Lung, and Blood Institute quality assessment tools.
Results: A total of 5 studies, comprising 47,778 patients and 310,249 ED visits, were included. Out of these 5 studies, 3 focused on self-check-in kiosks, one on self-triage kiosks, and another on technology combining both. Among 5 studies, 2 evaluated safety, reporting high sensitivity for predicting…
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 47,778 participants or records.
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.