Monitoring of characteristics of the patients visiting an emergency center in Cameroon through the development of hospital patient database

Jeong, Joong Sik; Kim, Yun Jeong; Kong, So Yeon; Shin, Sang Do; Ro, Young Sun; Wi, Dae Han; Kim, Sang Chul; Sun, Kyong Min; Kim, Suhee; Kim, Sola; Kang, Saee Byel; Bitang, Louis Joss; Hollong, Bonaventure; Wallis, Lynne

doi:10.1016/j.afjem.2021.12.002

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…In total, 39 studies were seen to contain ‘AFEM-CC quality indicators exact match’ data for process clinical and/or outcome quality indicators. 18–56 Only 8% (3 of 39) of these studies explicitly mentioned quality in the stated research objectives. The 39 studies contained patient-level data across 15 distinct quality domains: five clinical care domains, six assessment and documentation domains, four outcomes.…”

Section: Resultsmentioning

confidence: 99%

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Clinical emergency care quality indicators in Africa: a scoping review and data summary

et al. 2023

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ObjectivesEmergency care services are rapidly expanding in Africa; however, development must focus on quality. The African Federation of Emergency Medicine consensus conference (AFEM-CC)-based quality indicators were published in 2018. This study sought to increase knowledge of quality through identifying all publications from Africa containing data relevant to the AFEM-CC process clinical and outcome quality indicators.DesignWe conducted searches for general quality of emergency care in Africa and for each of 28 AFEM-CC process clinical and five outcome clinical quality indicators individually in the medical and grey literature.Data sourcesPubMed (1964—2 January 2022), Embase (1947—2 January 2022) and CINAHL (1982—3 January 2022) and various forms of grey literature were queried.Eligibility criteriaStudies published in English, addressing the African emergency care population as a whole or large subsegment of this population (eg, trauma, paediatrics), and matching AFEM-CC process quality indicator parameters exactly were included. Studies with similar, but not exact match, data were collected separately as ‘AFEM-CC quality indicators near match’.Data extraction and synthesisDocument screening was done in duplicate by two authors, using Covidence, and conflicts were adjudicated by a third. Simple descriptive statistics were calculated.ResultsOne thousand three hundred and fourteen documents were reviewed, 314 in full text. 41 studies met a priori criteria and were included, yielding 59 unique quality indicator data points. Documentation and assessment quality indicators accounted for 64% of data points identified, clinical care for 25% and outcomes for 10%. An additional 53 ‘AFEM-CC quality indicators near match’ publications were identified (38 new publications and 15 previously identified studies that contained additional ‘near match’ data), yielding 87 data points.ConclusionsData relevant to African emergency care facility-based quality indicators are highly limited. Future publications on emergency care in Africa should be aware of, and conform with, AFEM-CC quality indicators to strengthen understanding of quality.

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Section: Resultsmentioning

confidence: 99%

“…In total, 39 studies were seen to contain ‘AFEM-CC quality indicators exact match’ data for process clinical and/or outcome quality indicators 18–56. Only 8% (3 of 39) of these studies explicitly mentioned quality in the stated research objectives.…”

Section: Resultsmentioning

confidence: 99%

Clinical emergency care quality indicators in Africa: a scoping review and data summary

et al. 2023

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“…HIT usage poses a challenge in Sub-Saharan Africa, resulting in inadequate patient data. A dependable hospital patient database is crucial for delivering superior healthcare and facilitating seamless communication between healthcare professionals [ 47 ].…”

Section: Introductionmentioning

confidence: 99%

Hyperparameter optimization for cardiovascular disease data-driven prognostic system

Saputra,

Lawrencya,

Saini

et al. 2023

Vis. Comput. Ind. Biomed. Art

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Prediction and diagnosis of cardiovascular diseases (CVDs) based, among other things, on medical examinations and patient symptoms are the biggest challenges in medicine. About 17.9 million people die from CVDs annually, accounting for 31% of all deaths worldwide. With a timely prognosis and thorough consideration of the patient’s medical history and lifestyle, it is possible to predict CVDs and take preventive measures to eliminate or control this life-threatening disease. In this study, we used various patient datasets from a major hospital in the United States as prognostic factors for CVD. The data was obtained by monitoring a total of 918 patients whose criteria for adults were 28-77 years old. In this study, we present a data mining modeling approach to analyze the performance, classification accuracy and number of clusters on Cardiovascular Disease Prognostic datasets in unsupervised machine learning (ML) using the Orange data mining software. Various techniques are then used to classify the model parameters, such as k-nearest neighbors, support vector machine, random forest, artificial neural network (ANN), naïve bayes, logistic regression, stochastic gradient descent (SGD), and AdaBoost. To determine the number of clusters, various unsupervised ML clustering methods were used, such as k-means, hierarchical, and density-based spatial clustering of applications with noise clustering. The results showed that the best model performance analysis and classification accuracy were SGD and ANN, both of which had a high score of 0.900 on Cardiovascular Disease Prognostic datasets. Based on the results of most clustering methods, such as k-means and hierarchical clustering, Cardiovascular Disease Prognostic datasets can be divided into two clusters. The prognostic accuracy of CVD depends on the accuracy of the proposed model in determining the diagnostic model. The more accurate the model, the better it can predict which patients are at risk for CVD.

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Preventability of injury‐related morbidity & mortality at four hospitals in Cameroon: A systematic approach to trauma quality improvement

Zheng,

Mbuh,

Oke

et al. 2024

World j. surg.

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BackgroundAdverse events reviews are a fundamental component of trauma quality improvement (QI) that facilitate the correction of systemic issues in care. Although injury‐related mortality in Cameroon is substantial, to our knowledge, opportunities for QI have not been formally assessed. Thus, a formal review of adverse events in Cameroonian trauma patients was implemented as a first step toward identifying targets for systems modification.MethodsA QI committee composed of multidisciplinary experts at four hospitals in Cameroon was formed to review adverse events including deaths among trauma patients from 2019 to 2021. Events were discussed at newly established morbidity and mortality conferences and committee meetings to identify contributing factors and overall preventability.ResultsDuring 50 meetings, 95 adverse events were reviewed, including 58 deaths (61%). Other adverse events were delays in diagnosis/treatment (22%) and surgical site infections (17%). Overall, 34 deaths (59%) were classified as preventable, 21% potentially preventable, and 21% not preventable. Over half (52%) of the 46 preventable or potentially preventable deaths occurred in the emergency department (ED); while brain injury (57%), respiratory failure (41%), and hemorrhage (39%) were the most frequent physiologic factors associated with mortality. Contributory factors identified include lack of a structured approach to patient management, absence of continuous training for personnel, and locally adapted protocols.ConclusionsBasic improvements in evaluation and management of life‐threatening issues in the ED can significantly reduce the high rate of preventable trauma‐related deaths across Cameroon. Formal trauma QI methods can be utilized in low‐resource environments to determine mortality root causes and identify intervention targets.

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Monitoring of characteristics of the patients visiting an emergency center in Cameroon through the development of hospital patient database

Cited by 5 publications

References 13 publications

Clinical emergency care quality indicators in Africa: a scoping review and data summary

Clinical emergency care quality indicators in Africa: a scoping review and data summary

Hyperparameter optimization for cardiovascular disease data-driven prognostic system

Preventability of injury‐related morbidity & mortality at four hospitals in Cameroon: A systematic approach to trauma quality improvement

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