Evidence map›Paper›PMID 37259025›Full record

ArticleBMC emergency medicine2023

Emergency department-based injury surveillance information system: a conceptual model.

Nader Mirani, Haleh Ayatollahi, Davoud Khorasani-Zavareh, Kimia Zeraatkar

Abstract read
In one paragraph

Article in BMC emergency medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. 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

4 authors.

Nader MiraniDepartment of Health Tourism, Zanjan University of Medical Sciences, Zanjan, Iran.
Haleh AyatollahiHealth Management and Economics Research Center, Health Management Research Institute, Iran University of Medical Sciences, 1996713883, Tehran, Iran. ayatollahi.h@iums.ac.ir.ORCID 0000-0003-3974-3648
Davoud Khorasani-ZavarehSafety Promotion and Injury Prevention Research Center, Department of Health in Emergencies and Disasters, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Kimia ZeraatkarEducation Development Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInjury data play a pivotal role in monitoring public health issues and Injury Surveillance Information Systems (ISIS) are useful for continuous data collection and analysis purposes. Since emergency department (ED) is usually the first place of referral for the injured people, the aim of this study was to develop a conceptual model for an ED-based ISIS.

methodsThis study was completed in 2020 and the Delphi technique (three rounds) was used to determine the main components of an ED-based ISIS. The participants were selected using the purposive sampling method. A 5-point Likert scale questionnaire was used for data collection and data were analyzed using descriptive statistics.

resultsIn the first, second, and third rounds of the Delphi study, 60, 44, and 28 experts participated, respectively. In the first and second rounds, most of the items including the personal data, clinical data, data sources, and system functions were found important. In the third round of the Delphi study, 13 items which did not reach a consensus in the previous rounds were questioned again and five items were removed from the final model.

conclusionAccording to the findings, various data elements and functions could be considered for designing an ED-based ISIS and a number of data sources should be taken into count to be integrated with this system. Although the conceptual model presented in the present study can facilitate designing the actual system, the final system needs to be implemented and used in practice to determine how it can meet users' requirements.

Indexed as

Emergency Service, HospitalInformation SystemsConsensusDelphi TechniqueHumansSurveys and QuestionnairesEmergency departmentInjuryInjury surveillance information system

Identifiers

PMID37259025
PMCPMC10232345

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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.