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All Journal Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) JIF
Astri Hindarti
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EFUSI PLEURA DEXTRA EC. TB PARU: LAPORAN KASUS Fahmi Nofriandi; Astri Hindarti; Puan Sadila Islami
Jurnal Ilmiah Fisioterapi Vol 6 No 02 (2023): Agustus
Publisher : LPPM Universitas Abdurrab

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Pleural effusion is the accumulation of fluid in the pleural cavity due to transudation or excessive exudation from the pleural surface. In western countries, pleural effusion is mainly caused by congestive heart failure, cirrhosis of the liver, malignancy, and bacterial pneumonia, while in developing countries, such as Indonesia, it is commonly caused by tuberculosis infection. Good management is needed in tackling this pleural effusion, namely removing the fluid immediately and treating the cause so that the results will be satisfactory. This case reports a male twenty seven years complaining of shortness of breath since 1 week ago. Patients also complain of coughing, sweating at night, decreased body weight and fever. On physical examination of the thorax, right chest movement lags when breathing, palpation of tactile fremitus of the right lung is weak, dull percussion of the right lung is at ICS VII-X and vesicular auscultation is weakened at ICS VII-X right lung and crackles are found in both lung fields. Chest X-ray examination showed right pleural effusion. So that the diagnosis of pleural effusion dextra ec. pulmonary TB. The patient was managed with pleural puncture and OAT treatment
THE INFLUENCE OF ARTIFICIAL INTELLIGENCE IN DIAGNOSTIC SYSTEMS AND HEALTHCARE WORKERS’ DIGITAL COMPETENCE ON THE EFFICIENCY OF HOSPITAL SERVICES Astri Hindarti; Kosasih
Multidiciplinary Output Research For Actual and International Issue (MORFAI) Vol. 6 No. 3 (2026): Multidiciplinary Output Research For Actual and International Issue
Publisher : RADJA PUBLIKA

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This study aims to analyze the influence of Artificial Intelligence in diagnostic systems and the digital competence of healthcare workers on hospital service efficiency. The research employs a quantitative approach with an explanatory design to examine the causal relationships between variables. Data were collected through a structured questionnaire distributed to healthcare workers, including doctors, nurses, and administrative staff. The data were analyzed using IBM SPSS Statistics, applying validity and reliability tests, classical assumption tests, and multiple linear regression analysis. The results show that Artificial Intelligence in diagnostic systems has a positive and significant effect on hospital service efficiency. Similarly, the digital competence of healthcare workers also has a positive and significant influence on service efficiency. The simultaneous test (F-test) indicates that both variables jointly have a significant effect on hospital service efficiency. The coefficient of determination (R²) reveals that a substantial proportion of service efficiency can be explained by these two variables, while the remaining variance is influenced by other factors not examined in this study. These findings highlight that the integration of advanced technologies and the enhancement of human resource capabilities are essential in improving hospital service performance. The study suggests that hospitals should invest in the development of Artificial Intelligence systems as well as continuous training programs to strengthen the digital competence of healthcare workers. Such efforts are expected to support the achievement of efficient, effective, and high-quality healthcare services.
Co-Authors Fahmi Nofriandi Kosasih Puan Sadila Islami