Evidence map›Paper›PMID 40578877›Full record

ArticleBMJ open2025

Developing and integrating a destination decision support algorithm into an innovative electronic communication platform to improve injury care service coordination in Rwanda: the Rwanda912 study protocol.

Rwanda912 RIGHT Group

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Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author.

Rwanda912 RIGHT Group

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionDelays in getting injured patients to the hospital in a timely manner can increase avoidable death and disability. Like many low-income or middle-income countries, Rwanda experiences delays related to a lack of efficient prehospital communication and formal guidelines to triage patients for hospital care. This study describes the protocol to develop, roll-out and evaluate the effectiveness of a destination decision support algorithm (DDSA) integrated in an electronic communication platform, '912Rwanda'. The DDSA will facilitate the linkage of patients to health facilities able to treat their condition(s). METHODS AND ANALYSIS: Work will be conducted in the prehospital emergency service 'Service d'Aide Médicale Urgente' and health facilities in Kigali city and Musanze district, which serve predominantly urban and rural populations, respectively. We will develop interfaces to capture facility and patient-relevant data, which feed into a guideline-based electronic DDSA to match patients to hospitals. We will assess existing trauma care processes using qualitative and quantitative methodologies. This will be followed by a series of consensus workshops to develop at-scene triage guidelines and agree on variables to capture in the interfaces. The DDSA will be developed based on outputs from these workshops and will be tested against historical ambulance data and expert opinion until acceptable thresholds of performance are achieved. User interfaces will be developed and tested using human-computer interface design principles. DISCUSSION: The combined collaborative approach of bringing together experts and software developers, and with deep engagement of Rwandan stakeholders, including leadership of Rwanda Ministry of Health through its technical arm, Rwanda Biomedical Center, should lead to an ambulance communication system which is used, sustained and effective. ETHICS AND DISSEMINATION: The project was approved by the Rwanda National Research Ethics Committee. Annual reports will be disseminated to relevant stakeholders, followed by the public. Publications will be open access as per the funding policy. TRIAL REGISTRATION NUMBER: ISRCTN97674565. Registered on 29 July 2024. https://doi.org/10.1186/ISRCTN97674565.

Indexed as

AlgorithmsDecision Support TechniquesEmergency Medical ServicesTriageWounds and InjuriesHumansResearch DesignRwandaACCIDENT & EMERGENCY MEDICINEClinical Decision-MakingeHealthHealth informaticsInformation technology

Identifiers

PMID40578877
PMCPMC12207105

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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.