Evidence map›Paper›PMID 38848554›Full record

Observational studyJMIR mHealth and uHealth2024

Use of Mobile Technologies to Streamline Pretriage Patient Flow in the Emergency Department: Observational Usability Study.

Panzhang Wang, Lei Yu, Tao Li, Liang Zhou, Xin Ma

Abstract readObservational Study
In one paragraph

Observational study in JMIR mHealth and uHealth, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Use of a Kiosk-Model Self-Triage System for COVID-19 Triage.Risk management and healthcare policy · 2025
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Panzhang Wang *Department of Medical Informatics, Shanghai Sixth People's Hospital, Shanghai, China.ORCID 0000-0002-2743-4904
Lei Yu *Department of Medical Informatics, Shanghai Sixth People's Hospital, Shanghai, China.ORCID 0000-0003-1417-5218
Tao Li *Department of Medical Informatics, Shanghai Sixth People's Hospital, Shanghai, China.ORCID 0009-0007-7959-7004
Liang ZhouDepartment of Medical Informatics, Shanghai Sixth People's Hospital, Shanghai, China.ORCID 0000-0003-4374-2242
Xin MaDepartment of Orthopedics, Shanghai Sixth People's Hospital, Shanghai, China.ORCID 0009-0008-5575-2011

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn emergency departments (EDs), triage nurses are under tremendous daily pressure to rapidly assess the acuity level of patients and log the collected information into computers. With self-service technologies, patients could complete data entry on their own, allowing nurses to focus on higher-order tasks. Kiosks are a popular working example of such self-service technologies; however, placing a sufficient number of unwieldy and fixed machines demands a spatial change in the greeting area and affects pretriage flow. Mobile technologies could offer a solution to these issues.

objectiveThe aim of this study was to investigate the use of mobile technologies to improve pretriage flow in EDs.

methodsThe proposed stack of mobile technologies includes patient-carried smartphones and QR technology. The web address of the self-registration app is encoded into a QR code, which was posted directly outside the walk-in entrance to be seen by every ambulatory arrival. Registration is initiated immediately after patients or their proxies scan the code using their smartphones. Patients could complete data entry at any site on the way to the triage area. Upon completion, the result is saved locally on smartphones. At the triage area, the result is automatically decoded by a portable code reader and then loaded into the triage computer. This system was implemented in three busy metropolitan EDs in Shanghai, China. Both kiosks and smartphones were evaluated randomly while being used to direct pretriage patient flow. Data were collected during a 20-day period in each center. Timeliness and usability of medical students simulating ED arrivals were assessed with the After-Scenario Questionnaire. Usability was assessed by triage nurses with the Net Promoter Score (NPS). Observations made during system implementation were subject to qualitative thematic analysis.

resultsOverall, 5928 of 8575 patients performed self-registration on kiosks, and 7330 of 8532 patients checked in on their smartphones. Referring effort was significantly reduced (43.7% vs 8.8%; P<.001) and mean pretriage waiting times were significantly reduced (4.4, SD 1.7 vs 2.9, SD 1.0 minutes; P<.001) with the use of smartphones compared to kiosks. There was a significant difference in mean usability scores for "ease of task completion" (4.4, SD 1.5 vs 6.7, SD 0.7; P<.001), "satisfaction with completion time" (4.5, SD 1.4 vs 6.8, SD 0.6; P<.001), and "satisfaction with support" (4.9, SD 1.9 vs 6.6, SD 1.2; P<.001). Triage nurses provided a higher NPS after implementation of mobile self-registration compared to the use of kiosks (13.3% vs 93.3%; P<.001). A modified queueing model was identified and qualitative findings were grouped by sequential steps.

conclusionsThis study suggests patient-carried smartphones as a useful tool for ED self-registration. With increased usability and a tailored queueing model, the proposed system is expected to minimize pretriage waiting for patients in the ED.

Indexed as

Emergency Service, HospitalMobile ApplicationsTriageAdultEfficiency, OrganizationalFemaleHumansMaleMiddle AgedSmartphoneambulatorycrowdingemergencyhealth care managementhospitalmHealthmobile healthovercrowdedovercrowdingpretriagequeueingself-servicesmartphonetriageurgentworkflow

Identifiers

PMID38848554
PMCPMC11193078

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.