Electronic Medical Records (EMRs) have been proposed to improve the quality of services in healthcare organisations. However, sometimes, the design contexts of these systems tend to be different from the use contexts. This and other factors have been reported to cause failures of EMR adoptions. By focusing on factors from the Unified Theory of Acceptance and Use of Technology (UTAUT) model, we use interviews and questionnaire as data collection instruments to study the adoption of an EMR which was locally developed in rural Uganda; to generate lessons that would sustain the use of the EMR.We found out that all of the following factors, from the UTAUT model, significantly affected the usage of the system and, consequently, facilitated the adoption of the EMR at Kisiizi Hospital: expected improvement in job performance, the easiness with which the system can be learned and used, support and influence from management and peers, and the availability of organisational and technical infrastructures to support the use of the system. All of these were largely due to the fact that physicians from Kisiizi Hospital initiated and drove the system development and implementation processes, making sure that correct requirements were captured, and championing the use of the system by staff at the hospital. The in-context explanations for the findings are also provided.
BackgroundNeonatal sepsis is a significant cause of neonatal death and has been a major challenge worldwide. The difficulty in early diagnosis of neonatal sepsis leads to delay in treatment. The early diagnosis of neonatal sepsis has been predicted to improve neonatal outcomes. The use of machine learning techniques with the relevant screening parameters provides new ways of understanding neonatal sepsis and having possible solutions to tackle the challenges it presents. This work proposes an algorithm for predicting neonatal sepsis using electronic medical record (EMR) data from Mbarara Regional Referral Hospital (MRRH) that can improve the early recognition and treatment of sepsis in neonates.Methods A retrospective analysis was performed on datasets composed of de-identified electronic medical records collected between 2015 to 2019. The dataset contains records of 482 neonates hospitalized in Mbarara Regional Referral Hospital, Uganda. The proposed algorithm implements Support Vector Machine (SVM), Logistic regression (LR), K-nearest neighbor (KNN), Naïve Bayes (NB), and Decision tree (DT) algorithms, which were trained, tested, and compared based on the acquired data. The performance of the proposed algorithm was evaluated by comparing it with the physician's diagnosis. The experiment used a Stratified K-fold cross-validation technique to evaluate the performance of the models. Statistical significance of the experimental results was carried out using the Wilcoxon Signed-Rank Test. ResultsThe results of this study show that the proposed algorithm (with the lowest Sensitivity of 95%, lowest Specificity of 95%) outperformed the physician diagnosis (Sensitivity = 89%, Specificity = 11%). SVM model with radial basis function, polynomial kernels, and DT model (with the highest AUROC values of 98%) performed better than the other models in predicting neonatal sepsis as their results were statistically significant.ConclusionsThe study provides evidence that the combination of maternal risk factors, neonatal clinical signs, and laboratory tests effectively diagnose neonatal sepsis. Based on the study result, the proposed algorithm can help identify neonatal sepsis cases as it exceeded clinicians' sensitivity and specificity. A prospective study is warranted to test the algorithm's clinical utility, which could provide a decision support aid to clinicians.
About 2.9 million neonates die every year worldwide, and most of these deaths occur in low-resource settings. Neonatal sepsis occurs when there is a bacterial invasion in the bloodstream; the immune system begins a systemic inflammatory response syndrome (SIRS) damaging to the body and can quickly advance to severe sepsis, multi-organ failure, and finally, death. Sepsis in neonates can progress more rapidly than in adults; therefore, a timely diagnosis is critical. The standard gold test for diagnosing neonatal sepsis is blood culture, which takes at least 72 hours. Hence, identifying key predictor variables and models that work best can help reduce neonatal morbidity and mortality. The matching articles were identified by searching the PubMed, IEEE, and Cochrane bibliography databases. For the inclusion of articles, the abstract and titles were first screened based on some predetermined criteria and then, the full-text articles were screened. Thirty-one studies met the full inclusion criteria. The duration of ROM was found to be more significant than other maternal risk factors. Heart rate and heart rate variability were found to be more significant than other neonatal clinical signs. C reactive protein and I/T ratio were found to be more significant than other laboratory tests. The main limitation is the variation in the performance measures used in the studies, which made it difficult to perform a quantitative assessment. A combination of predictor variables has been shown to strengthen neonatal sepsis prediction, as shown by some of the reviewed studies. Predictive algorithms that combine multiple variables are urgently needed to improve models for early detection, prognosis, and treatment of neonatal sepsis.
BackgroundNeonatal sepsis is a significant cause of neonatal death and has been a major challenge worldwide. The difficulty in early diagnosis of neonatal sepsis leads to delay in treatment. The early diagnosis of neonatal sepsis has been predicted to improve neonatal outcomes. The use of machine learning techniques with the relevant screening parameters provides new ways of understanding neonatal sepsis and having possible solutions to tackle the challenges it presents. This work proposes an algorithm for predicting neonatal sepsis using electronic medical record (EMR) data from Mbarara Regional Referral Hospital (MRRH) that can improve the early recognition and treatment of sepsis in neonates.Methods A retrospective analysis was performed on datasets composed of de-identified electronic medical records collected between 2015 to 2019. The dataset contains records of 482 neonates hospitalized in Mbarara Regional Referral Hospital, Uganda. The proposed algorithm implements Support Vector Machine (SVM), Logistic regression (LR), K-nearest neighbor (KNN), Naïve Bayes (NB), and Decision tree (DT) algorithms, which were trained, tested, and compared based on the acquired data. The performance of the proposed algorithm was evaluated by comparing it with the physician's diagnosis. The experiment used a Stratified K-fold cross-validation technique to evaluate the performance of the models. Statistical significance of the experimental results was carried out using the Wilcoxon Signed-Rank Test. ResultsThe results of this study show that the proposed algorithm (with the lowest Sensitivity of 0.95, lowest Specificity of 0.95) outperformed the physician diagnosis (Sensitivity = 0.89, Specificity = 0.11). SVM model with radial basis function, polynomial kernels, and DT model (with the highest AUROC values of 0.98) performed better than the other models in predicting neonatal sepsis as their results were statistically significant.ConclusionsThe study provides evidence that the combination of maternal risk factors, neonatal clinical signs, and laboratory tests effectively diagnose neonatal sepsis. Based on the study result, the proposed algorithm can help identify neonatal sepsis cases as it exceeded clinicians' sensitivity and specificity. A prospective study is warranted to test the algorithm's clinical utility, which could provide a decision support aid to clinicians.
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