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All Journal International Archives of Medical Sciences and Public Health International Archives of Medical Sciences and Public Health Proceedings Series on Health & Medical Sciences
Almanfaluthi, Muhammad
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Religion Environmental Science Health Professions Medicine & Pharmacology Nursing Public Health

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Empowering Nurses as Key Players in the Regional Fight Against Antimicrobial Resistance Almanfaluthi, Muhammad
Proceedings Series on Health & Medical Sciences Vol. 6 (2025): Proceedings of the 5th International Nursing and Health Sciences Universitas Muhammad
Publisher : UM Purwokerto Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/pshms.v6i.1422

Abstract

This study explores the differences in knowledge, attitude, and practice (KAP) across gender, experience, residence, and ethnicity among 316 nurses. Understanding these variations is essential to tailor effective interventions that promote better knowledge acquisition and application in the long battle against antimicrobial resistance. A cross-sectional study was conducted with 316 participants (89 males, 227 females) to assess KAP metrics. Data were analyzed using incidence rate ratios (IRR) to explore relationships between demographic factors and KAP scores. Correlation analyses were performed to determine the strength of relationships between knowledge, attitude, and practice across various subgroups, including gender, residence (rural/urban), experience levels, and ethnicity. Significant gender differences were found, with females scoring higher in knowledge (IRR=1.03, p=0.024) and attitude (IRR=1.03, p=0.030) than males. Urban participants had significantly higher knowledge scores than rural participants (IRR=1.19, p<0.001). Experience levels also impacted knowledge, with participants having 3-6 years of experience showing lower scores than those with over ten years of experience (IRR=0.89, p<0.001). Attitude positively correlated with knowledge and practice (p<0.05). The findings reveal notable disparities in KAP across gender, experience, and residence. Females and urban participants demonstrate higher knowledge and attitude, while experience correlates with greater knowledge. Interventions should target rural areas and less experienced individuals and focus on improving attitudes to drive better knowledge and practice outcomes. Tailored educational programs addressing these disparities are recommended for more equitable KAP improvements.
Nurse’s Lean Management Practices and Hospital Discharge Time Improvement Sabilla, Berlianda Nur; Almanfaluthi, Muhammad
Proceedings Series on Health & Medical Sciences Vol. 6 (2025): Proceedings of the 5th International Nursing and Health Sciences Universitas Muhammad
Publisher : UM Purwokerto Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/pshms.v6i.1423

Abstract

Lean management is a dedicated, effective system for resource optimization, time efficiency, waste reduction, and care improvement. We aimed to evaluate the implementation of lean management in our hospital setting. We expect significant improvement in time efficiency, particularly in discharge time, administrative collection, and payment mechanism. We randomly collected 494 patients’ medical records regarding time departure after treatment—308 of 494 representing the period before lean implementation. We performed additional nested randomization stratified by hospital class services (first class, second class, third class, and VIP). The results confirmed substantial time reduction (Mann Whitney test < 0.01 with high effect sizes r-rank biserial = .98) in discharge time in the nursing administration and cashier sections before and after lean implementation. Moreover, these findings were consistent across class services (nested-Mann-Whitney <.0.01). The downward waiting time was visible if the patient was hospitalized in the first, second, third, or VIP class. Implementing lean management reduces to 78% waiting time compared to the period before implementation (95%CI IRR negative binomial regression 77-79%). Lean management practice among nurses slashes patient discharge waiting time by 78%, proving its power to streamline hospital operations and boost efficiency across all service classes.
EVOLUTION OF ARTIFICIAL INTELLIGENCE IN PATIENT SAFETY ACROSS SOUTHEAST ASIA: A BIBLIOMETRIC ANALYSIS Almanfaluthi, Muhammad; Dewi, Arlina
International Archives of Medical Sciences and Public Health Vol. 7 No. 1 (2026): International Archives of Medical Sciences and Public Health
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53806/iamsph.v7i1.1447

Abstract

This study evaluated the evolution of artificial intelligence (AI) and machine learning in patient safety across Southeast Asia (SEA) to identify regional research dynamics and emerging frontiers. A quantita-tive bibliometric analysis was conducted using 262 primary documents retrieved from the Scopus database (English-language; published up to December 31, 2025). Document selection was guided by an adaptation of the PRISMA-ScR framework. Key indicators included the Mann-Kendall trend test, Pettitt’s change-point test, and thematic cooccurrence network mapping. Statistical analysis revealed a significant structural shift in 2015 (p < 0.01), marking exponential publication growth and extensive international collaboration (62.2% of documents featuring multi-country co-authorship). Results highlighted a divergence in regional strategies; high-income nations produced high-impact clinical algorithms, while emerging economies prioritized capacity building for resource-constrained systems. Thematic mapping demonstrated a major transitional shift in the literature from traditional, image-based neural networks for diagnostic accuracy toward natural language processing and large language models aimed at addressing clinical documentation and prescribing errors. These publication dynamics map the current academic focus, highlighting the need for future implementation driven clinical validation and regionally adapted regulatory frameworks.
EVOLUTION OF ARTIFICIAL INTELLIGENCE IN PATIENT SAFETY ACROSS SOUTHEAST ASIA: A BIBLIOMETRIC ANALYSIS Almanfaluthi, Muhammad; Dewi, Arlina
International Archives of Medical Sciences and Public Health Vol. 7 No. 1 (2026): International Archives of Medical Sciences and Public Health
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53806/iamsph.v7i1.1447

Abstract

This study evaluated the evolution of artificial intelligence (AI) and machine learning in patient safety across Southeast Asia (SEA) to identify regional research dynamics and emerging frontiers. A quantita-tive bibliometric analysis was conducted using 262 primary documents retrieved from the Scopus database (English-language; published up to December 31, 2025). Document selection was guided by an adaptation of the PRISMA-ScR framework. Key indicators included the Mann-Kendall trend test, Pettitt’s change-point test, and thematic cooccurrence network mapping. Statistical analysis revealed a significant structural shift in 2015 (p < 0.01), marking exponential publication growth and extensive international collaboration (62.2% of documents featuring multi-country co-authorship). Results highlighted a divergence in regional strategies; high-income nations produced high-impact clinical algorithms, while emerging economies prioritized capacity building for resource-constrained systems. Thematic mapping demonstrated a major transitional shift in the literature from traditional, image-based neural networks for diagnostic accuracy toward natural language processing and large language models aimed at addressing clinical documentation and prescribing errors. These publication dynamics map the current academic focus, highlighting the need for future implementation driven clinical validation and regionally adapted regulatory frameworks.
Co-Authors Arlina Dewi Sabilla, Berlianda Nur