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Management policy used when administering clinical emergency patients by Indonesia’s Public Safety Center (PSC) is regulated in Minister of Health Regulation No. 19 of the year 2016. Therefore, clinical emergency management assessment for Indonesia’s 119 PSCs is crucial for continous subsequent improvements. By reviewing literatures, conducting group discussions with experts from the PSCs and analyzing confirmatory factors, 8 variables with 28 valid and reliable indicators to assess the clinical emergency management of PSCs are found. The results of the validity and reliability test of confirmatory factor analysis were presented to 88 PSC responsdents. On all variables, namely aspects of policy, planning, emergency implementation, communication and rescue systems, emergency transportation systems, referral systems, management reviews and emergency services, it can be stated that the question items or indicators in each variable are valid and reliable with confirmatory factor analysis. All obstacles and challenges in implementing clinical emergency management in PSC will be an improvement and development of clinical emergency management. Keywords: assessment indicators; clinical emergency management; sustainable management development
The tuberculosis treatment success rate in Indonesia in 2023 did not reach the 90% target. Treatment success impacts the reduction of infection spread and drug resistance cases, making early prediction of treatment success crucial. This study aims to develop a machine-learning model to predict treatment success. Data from Indonesia's Tuberculosis Information System (SITB) cohort was used. The study included productive-age patients (15-64 years) diagnosed with drug-sensitive tuberculosis who received treatment from January 1, 2020, to December 31, 2023. Data was randomly split into training (80%) and testing (20%) sets for model validation, with cross-validation performed. The algorithms used include decision tree, random forest, multilayer perception, extreme gradient boosting, and logistic regression. A consensus was reached for decision-making variables required in performing machine learning-based modeling of SITB data to predict treatment success using modeling of SITB data to predict treatment success using the Delphi method. The results of the study show that the random forest machine learning algorithm had the best performance and highest accuracy in predicting treatment success. This machine learning–based prediction tool can provide early predictions with SHAP (SHapley Additive ExPlanations) interpretation, helping healthcare workers make informed decisions more easily.
