Evidence map›Paper›PMID 30545312›Full record

Observational studyBMC emergency medicine2018

Man vs machine in emergency medicine - a study on the effects of manual and automatic vital sign documentation on data quality and perceived workload, using observational paired sample data and questionnaires.

Niclas Skyttberg, Rong Chen, Sabine Koch

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in BMC emergency medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 20% of its field
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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.

  1. Digital Health Data Quality Issues: Systematic Review.Journal of medical Internet research · 2023
    Pooled it
  2. Article
  3. Data completeness and quality of emergency triage in Ethiopian public tertiary hospitals: A multicenter study.African journal of emergency medicine : Revue africaine de la medecine d'urgence · 2025
    Article
  4. Article
  5. Review
  6. Article
  7. Review
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

3 authors at 1 institution in 1 country.

Niclas SkyttbergDepartment of Learning, Informatics, Management and Ethics, Health Informatics Centre, 171 77, Stockholm, Sweden. niclas.skyttberg@ki.se.ORCID 0000-0003-3985-185X
Rong ChenDepartment of Learning, Informatics, Management and Ethics, Health Informatics Centre, 171 77, Stockholm, Sweden.
Sabine KochDepartment of Learning, Informatics, Management and Ethics, Health Informatics Centre, 171 77, Stockholm, Sweden.
Statistics Sweden · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEmergency medicine is characterized by a high patient flow where timely decisions are essential. Clinical decision support systems have the potential to assist in such decisions but will be dependent on the data quality in electronic health records which often is inadequate. This study explores the effect of automated documentation of vital signs on data quality and workload.

methodsAn observational study of 200 vital sign measurements was performed to evaluate the effects of manual vs automatic documentation on data quality. Data collection using questionnaires was performed to compare the workload on wards using manual or automatic documentation.

resultsIn the automated documentation time to documentation was reduced by 6.1 min (0.6 min vs 7.7 min, p <  0.05) and completeness increased (98% vs 95%, p <  0.05). Regarding workflow temporal demands were lower in the automatic documentation workflow compared to the manual group (50 vs 23, p <  0.05). The same was true for frustration level (64 vs 33, p <  0.05). The experienced reduction in temporal demands was in line with the anticipated, whereas the experienced reduction in frustration was lower than the anticipated (27 vs 54, p < 0.05). DISCUSSION: The study shows that automatic documentation will improve the currency and the completeness of vital sign data in the Electronic Health Record while reducing workload regarding temporal demands and experienced frustration. The study also shows that these findings are in line with staff anticipations but indicates that the anticipations on the reduction of frustration may be exaggerated among the staff. The open-ended answers indicate that frustration focus will change from double documentation of vital signs to technical aspects of the automatic documentation system.

Indexed as

AutomationData AccuracyEmergency MedicineVital SignsWorkloadEmergency Medical TechniciansFemaleHealth Care SurveysHumansMaleTriageAutomated documentationData qualityEmergency departmentEmergency medicineVital signs

Identifiers

PMID30545312
PMCPMC6293611
OpenAlexW2905273567

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.