Data flows during public health emergencies in LMICs: A people-centered mapping of data flows during the 2018 ebola epidemic in Equateur, DRC

Abramowitz, Sharon; Stevens, Lys Alcayna; Kyomba, Gabriel; Mayaka, Serge Manitu; Grépin, Karen A

doi:10.1016/j.socscimed.2022.115116

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…For instance, emergency care in various LMICs has been shown to experience high patient loads and an urgent need for treatment, underscoring the importance of robust and efficient emergency care systems [24]. Additionally, public health emergencies, such as the 2018 Ebola epidemic in Equateur Province in the Democratic Republic of the Congo, highlight the challenges faced by LMICs, where data quality and integrated data management systems have been identified as concerns [25]. These findings not only emphasize the significance of our study within the Brazilian context but also underscore the need for adaptable and scalable solutions for LMICs at large.…”

Section: Plos Digital Healthmentioning

confidence: 99%

AI-based approach for transcribing and classifying unstructured emergency call data: A methodological proposal

Costa,

Pinna,

Joiner

et al. 2023

PLOS Digit Health

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Emergency care-sensitive conditions (ECSCs) require rapid identification and treatment and are responsible for over half of all deaths worldwide. Prehospital emergency care (PEC) can provide rapid treatment and access to definitive care for many ECSCs and can reduce mortality in several different settings. The objective of this study is to propose a method for using artificial intelligence (AI) and machine learning (ML) to transcribe audio, extract, and classify unstructured emergency call data in the Serviço de Atendimento Móvel de Urgência (SAMU) system in southern Brazil. The study used all “1-9-2” calls received in 2019 by the SAMU Novo Norte Emergency Regulation Center (ERC) call center in Maringá, in the Brazilian state of Paraná. The calls were processed through a pipeline using machine learning algorithms, including Automatic Speech Recognition (ASR) models for transcription of audio calls in Portuguese, and a Natural Language Understanding (NLU) classification model. The pipeline was trained and validated using a dataset of labeled calls, which were manually classified by medical students using LabelStudio. The results showed that the AI model was able to accurately transcribe the audio with a Word Error Rate of 42.12% using Wav2Vec 2.0 for ASR transcription of audio calls in Portuguese. Additionally, the NLU classification model had an accuracy of 73.9% in classifying the calls into different categories in a validation subset. The study found that using AI to categorize emergency calls in low- and middle-income countries is largely unexplored, and the applicability of conventional open-source ML models trained on English language datasets is unclear for non-English speaking countries. The study concludes that AI can be used to transcribe audio and extract and classify unstructured emergency call data in an emergency system in southern Brazil as an initial step towards developing a decision-making support tool.

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Section: Plos Digital Healthmentioning

confidence: 99%

AI-based approach for transcribing and classifying unstructured emergency call data: A methodological proposal

Costa,

Pinna,

Joiner

et al. 2023

PLOS Digit Health

View full text Add to dashboard Cite

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“…Rapid and wide sharing of clinical trial data, as well as epidemiological data during health emergencies makes a difference by enhancing response efforts (Abramowitz et al, 2018;Lucas-Dominguez et al, 2021). The recent Ebola epidemic (2014)(2015)(2016), and the Yellow fever epidemic (2016) have shown that the lack of timely access to the epidemiological and clinical trial data slow down research efforts and thereby the pandemic response (Abramowitz et al, 2018;Lucas-Dominguez et al, 2021). Not surprisingly, there are ongoing calls from researchers, funders, and the scientific community for the rapid sharing of clinical trial data (McBride et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Individual participant data sharing intentions and practices during the coronavirus disease-2019 pandemic: A rapid review

et al. 2023

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The coronavirus disease-2019 (COVID-19) pandemic has led to the irrational use of drugs in the absence of clinical management guidelines. Access to individual participant data (IPD) from clinical trials aids the evidence synthesis approaches. We undertook a rapid review to infer IPD sharing intentions based on data availability statements by the principal investigators (PIs) of drug and vaccine trials in the context of COVID-19. Searches were conducted on PubMed (NCBI). We considered randomized controlled trial (RCT) publications from January 1, 2020, to October 31, 2021. IPD sharing intentions were inferred based on the data availability statements in the full-text manuscript publications. We included 180 articles. Of these, 81.7% (147/180) of the publications have arrived from the findings of the RCTs alone, 12.8% (23/180) of the publications were protocol publications alone, and 5.6% (10/180) of the RCTs had both published protocol and publication from the trial findings. We have reported IPD sharing intentions separately in RCT protocol publications (n = 23 + 10) and publications from RCT findings (n = 147 + 10). Among RCT protocol publications, one-third (11/33) of the PIs intended to share IPD. In fact, over half of the PIs (52.2%, 82/157) in their published RCT findings intended to share IPD. However, information to share about IPD was missing for 57.6% (19/33) of RCT protocols and 38.2% (60/157) of published RCT findings. Stakeholders must work together to ensure that overarching factors, such as legislation that governs clinical trial practices, are streamlined to bolster IPD sharing mechanisms.

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Data flows during public health emergencies in LMICs: A people-centered mapping of data flows during the 2018 ebola epidemic in Equateur, DRC

Cited by 2 publications

References 24 publications

AI-based approach for transcribing and classifying unstructured emergency call data: A methodological proposal

AI-based approach for transcribing and classifying unstructured emergency call data: A methodological proposal

Individual participant data sharing intentions and practices during the coronavirus disease-2019 pandemic: A rapid review

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