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Journal of World Future Medicine, Health and Nursing
Published by Yayasan Adra Karima Hubbi
ISSN : 29880459     EISSN : 29887550     DOI : 10.70177/health
Core Subject :
Journal of World Future Medicine, Health and Nursing is a leading international journal focused on the global exchange of knowledge in medicine, health, and nursing, as well as advancing research and practice across health disciplines. The journal provides a forum for articles reporting on original research, systematic and scholarly reviews focused on health science, clinical practice and education from around the world. Journal of World Future Medicine, Health and Nursing publishes national and international research in an attempt to present a reliable and respectable information source for the researchers. Journal of World Future Medicine, Health and Nursing has been published since 2023, published three times a year January, May and September,. The articles submitted for publication are subjected to double-blind reviewing process. The journal publishes original articles in English.
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Articles 11 Documents
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Search results for , issue "vol. 4 no. 4 (2026)" : 11 Documents clear
COMPUTATIONAL INSIGHT INTO MORINGA OLEIFERA AS EGFR-TARGETING CANDIDATES FOR HNC MANAGEMENT Adnan Abdullah; Asti Widuri; Rizka Fahriani; Muhammad Farid; Arini Rizqa Fathanah
Journal of World Future Medicine, Health and Nursing Vol. 4 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v4i4.3804

Abstract

Head and neck cancer (HNC) remains a major cause of cancer-related morbidity and mortality worldwide. Although epidermal growth factor receptor (EGFR)-targeted therapies have improved treatment outcomes, their clinical effectiveness is often limited by drug resistance and adverse effects, highlighting the need for alternative therapeutic candidates. This study evaluated the potential of Moringa oleifera Lam. phytochemicals as prospective EGFR inhibitors using an integrated in silico approach. Molecular docking was performed with AutoDock Vina implemented in PyRx against the EGFR crystal structure (PDB ID: 4HJO). Ligand structures retrieved from PubChem were prepared and optimized using Open Babel prior to docking, while pharmacokinetic properties were predicted using the pkCSM web server. Among the screened compounds, brassicasterol and campesterol exhibited the strongest predicted binding affinity (?8.6 kcal/mol), followed by campestanol (?8.4 kcal/mol) and ergosterol (?8.2 kcal/mol), exceeding the docking scores of both the native ligand (?7.2 kcal/mol) and gefitinib under the same computational conditions. Interaction analysis indicated that ligand binding was primarily stabilized through hydrophobic contacts with key EGFR residues, including LEU694, VAL702, and LEU820. ADMET prediction suggested favorable intestinal absorption, acceptable physicochemical properties, blood–brain barrier permeability, and low predicted mutagenicity, although several phytosterols demonstrated potential CYP3A4 inhibitory activity. These findings provide preliminary computational evidence supporting the potential of selected Moringa oleifera phytosterols as candidate EGFR-targeting compounds. However, the results are based solely on computational predictions and require further validation through in vitro and in vivo studies before therapeutic applications can be inferred.
PUBLIC POLICY ANALYSIS OF DECENTRALIZED NATIONAL ELECTRONIC HEALTH RECORDS USING BLOCKCHAIN TECHNOLOGY Mulyaningsih Mulyaningsih; Nurul Huda; Nina Anis
Journal of World Future Medicine, Health and Nursing Vol. 4 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v4i4.4068

Abstract

National electronic health record (EHR) systems face persistent challenges related to interoperability, data security, and fragmented governance structures, particularly within centralized architectures that limit real-time data exchange across healthcare institutions. This study aims to analyze public policy implications of implementing decentralized national EHR systems using blockchain technology, with emphasis on governance transformation, regulatory alignment, and system performance. A qualitative-dominant mixed-methods approach is employed through comparative policy analysis, secondary data evaluation, and stakeholder interpretation. Results indicate that blockchain-based EHR systems significantly improve interoperability (88% vs 62%), reduce data breach incidence (3 vs 18 cases per million records), and enhance patient data accessibility (score 91 vs 65). Inferential findings confirm statistically significant improvements in system efficiency and governance performance. The study concludes that decentralized blockchain-enabled EHR systems offer substantial advantages over centralized models, not only in technical performance but also in reinforcing transparency, trust, and data sovereignty within national health governance frameworks, while requiring adaptive regulatory reforms for sustainable implementation.
EFFECTIVENESS OF WOOLWICH MASSAGE ON BREAST MILK PRODUCTION BASED ON MATERNAL AND INFANT INDICATORS AMONG POSTPARTUM MOTHERS: A QUASI-EXPERIMENTAL STUDY Pariqa Annisa; Rina Oktaviana; Sri Putriani Sinaga
Journal of World Future Medicine, Health and Nursing Vol. 4 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v4i4.4089

Abstract

Breast milk production is one of the major determinants of successful exclusive breastfeeding during the early postpartum period. Insufficient milk production often leads to early breastfeeding discontinuation and the introduction of infant formula. This study aimed to determine the effectiveness of Woolwich Massage on breast milk production based on both maternal and infant indicators among postpartum mothers. A quasi-experimental study with a pretest–posttest control group design was conducted at BPM Sasyuni, Kampar Regency, Indonesia, from January to March 2026. Thirty postpartum mothers were recruited using consecutive sampling and equally assigned to an intervention group (n = 15) and a control group (n = 15). Breast milk production was assessed using maternal indicators (breast fullness, breast softening, spontaneous milk leakage, let-down reflex, and absence of breast engorgement) and infant indicators (urination frequency, bowel movement frequency, infant satisfaction, sleep duration, and weight gain). The paired t-test revealed a significant increase in breast milk production scores in the intervention group (mean difference = 5.13 ± 0.99; p < 0.001), while the independent t-test demonstrated significantly greater improvement than the control group (p < 0.001). These findings suggest that Woolwich Massage is an effective, safe, and low-cost non-pharmacological intervention that can be integrated into routine postpartum midwifery care to support successful exclusive breastfeeding.    
IMPLEMENTING DIGITAL TWINS FOR CANCER THERAPY SIMULATION: GENETIC-BASED MEDICAL PERSONALIZATION Siri Lek; Lucas Lima; Pedro Silva; Dito Anurogo
Journal of World Future Medicine, Health and Nursing Vol. 4 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v4i4.4202

Abstract

because substantial genetic heterogeneity, dynamic tumor evolution, and variable treatment responses limit the effectiveness of standardized clinical protocols. This study aimed to evaluate the implementation of Digital Twins for cancer therapy simulation and to examine their contribution to genetic-based medical personalization and clinical decision support. A mixed-methods sequential explanatory design was employed involving 480 patients treated at tertiary oncology centers. Quantitative analyses included descriptive statistics, structural equation modeling, survival analysis, mediation and moderation analyses, whereas qualitative evidence was obtained through semi-structured interviews, clinical observations, multidisciplinary case reviews, and document analysis. Findings demonstrated that Digital Twin-assisted treatment planning significantly improved simulation accuracy, treatment response prediction, therapeutic precision, physician decision confidence, and multidisciplinary coordination while reducing anticipated adverse treatment events. Results indicate that Digital Twin technology provides a comprehensive precision oncology framework by integrating computational intelligence, genomic medicine, and real-time clinical information to optimize individualized cancer therapy, improve evidence-based decision-making, and enhance long-term patient outcomes while supporting the broader implementation of personalized medicine.
THE IMPACT OF CLIMATE CHANGE ON EMERGING INFECTIOUS DISEASE PATTERNS: GLOBAL MITIGATION STRATEGIES FOR 2030 Fitriah Handayani; Omar Khan; Khalil Zaman
Journal of World Future Medicine, Health and Nursing Vol. 4 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v4i4.4212

Abstract

Climate change increasingly reshapes environmental conditions that influence the emergence, transmission, and geographic distribution of infectious diseases, creating significant challenges for global public health systems. This study aimed to examine the impact of climate change on emerging infectious disease patterns and to evaluate the effectiveness of global mitigation strategies through the integration of climate adaptation, environmental governance, healthcare preparedness, and One Health implementation. A mixed-methods sequential explanatory design was employed using epidemiological and environmental datasets from 72 countries. Quantitative analyses included descriptive statistics, structural equation modeling, spatial regression, time-series analysis, mediation and moderation analyses, whereas qualitative evidence was collected through semi-structured interviews, policy reviews, institutional observations, and document analysis. Findings revealed that climate vulnerability significantly increased emerging infectious disease incidence, while environmental degradation accelerated vector expansion and zoonotic spillover risks. Healthcare preparedness partially mediated the relationship between climate adaptation policies and disease control, whereas One Health implementation strengthened mitigation effectiveness through enhanced surveillance and intersectoral collaboration.  
PREDICTING SERVICE GAPS IN INDONESIAN MIGRANT WORKER PROTECTION USING ARTIFICIAL INTELLIGENCE AND THE CIPPO EVALUATION MODEL Sudiharto Sudiharto; Adius Kusnan; Sulfikar Sallu
Journal of World Future Medicine, Health and Nursing Vol. 4 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v4i4.4223

Abstract

Indonesian migrant workers continue to face various challenges throughout the migration cycle, including high placement costs, limited access to information, exploitation, legal problems, and inadequate reintegration services upon returning home. This study aimed to evaluate the effectiveness of Indonesian migrant worker protection services using the Context, Input, Process, Product, and Outcome (CIPPO) evaluation model and to develop an artificial intelligence-based prediction model for identifying service gaps. This study employed a qualitative evaluative approach using the CIPPO framework. Data were collected from 57 informants comprising government officials, prospective migrant workers, migrant workers abroad, and returned migrant workers through in-depth interviews, observations, and document analysis. The evaluation revealed that the Context dimension achieved 40.43%, Input 77.92%, Process 100%, Product 59%, and Outcome only 11.1%. The findings indicate that although program implementation was administratively effective, its impact on improving protection services remained limited. The artificial intelligence models successfully predicted service deficiencies and identified critical areas requiring policy intervention. Integrating artificial intelligence with the CIPPO evaluation model provides a comprehensive and evidence-based approach for predicting service gaps and strengthening Indonesian migrant worker protection policies through data-driven decision-making and digital governance strategies.    
THE ROLE OF AI-BASED EXPERT SYSTEMS IN OPTIMIZING PHARMACEUTICAL MANAGEMENT IN SMART HOSPITALS Pande Ayu Naya Kasih; Ethan Tan; Ryan Teo
Journal of World Future Medicine, Health and Nursing Vol. 4 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v4i4.4265

Abstract

Artificial intelligence-based expert systems are increasingly used in smart hospitals to improve medication safety, inventory efficiency, and pharmaceutical decision-making. This study aimed to evaluate the role of an integrated AI expert system in optimizing clinical and logistical pharmacy management. A multi-site mixed-methods quasi-experimental design was conducted in four smart hospitals, with two intervention hospitals and two comparison hospitals observed across six-month baseline and intervention periods. Quantitative data included 122,480 prescriptions, 1,186 pharmaceutical products, stockout records, expired medicines, emergency purchases, medication errors, and processing times, while qualitative data were obtained from surveys, interviews, and focus groups involving healthcare professionals. Results showed that AI implementation reduced stockouts by 48.24%, decreased medication errors from 3.82% to 2.06%, shortened prescription-processing time from 13.60 to 8.90 minutes, and lowered expired medicine value and emergency purchasing. The study concludes that AI-based expert systems can optimize pharmaceutical management by integrating predictive analytics, patient-specific safety rules, and human oversight within coordinated smart-hospital workflows, while preserving accountability, transparency, and professional judgment across diverse clinical and operational settings.  
UTILIZING MACHINE LEARNING ALGORITHMS TO PREDICT PATIENT SURGES IN FUTURE EMERGENCY DEPARTMENTS Justam Justam; Luis Santos; Josefa Flores
Journal of World Future Medicine, Health and Nursing Vol. 4 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v4i4.4278

Abstract

Emergency departments face increasing pressure from unpredictable patient surges that can intensify overcrowding, treatment delays, staff workload, and resource shortages. This study aimed to develop and evaluate machine learning algorithms for predicting emergency demand across multiple hospitals and forecast horizons. A quantitative multi-site predictive modeling design combined retrospective records from 1,427,860 emergency encounters with prospective validation data from 151,632 visits. Seasonal ARIMA, Poisson regression, support vector regression, random forest, long short-term memory, extreme gradient boosting, and hybrid ensemble models were compared using temporal and external validation. Results showed that the hybrid ensemble achieved the strongest twenty-four-hour performance, with a mean absolute error of 21.84 patients, mean absolute percentage error of 6.58%, and an R-squared value of 0.91. The model also produced high six-hour surge sensitivity, while recent arrivals, occupancy, ambulance activity, infectious disease indicators, bed availability, weather, air quality, and public events emerged as influential predictors. Predictive accuracy declined for longer horizons and less frequent clinical categories. The study concludes that machine learning can strengthen emergency preparedness when forecasts are explainable, continuously validated, and connected to predefined staffing, triage, bed-management, and ambulance-response protocols.  
CHANGES IN KNOWLEDGE AND ATTITUDES AFTER NUTRITION EDUCATION AND VEGETABLE NUGGETS: A TWO-SCHOOL QUASI-EXPERIMENT Sri Sunarti; Fitriyani A; Dwi Wulan Indriani; Hikmal Raihansyah; Nuralifah Amalia Ishmah; Reyhana Damara Pradatya
Journal of World Future Medicine, Health and Nursing Vol. 4 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v4i4.4291

Abstract

Low vegetable intake among Indonesian schoolchildren persists, yet evidence on interventions pairing classroom education with a familiar vegetable-based food remains limited. This study assessed changes in knowledge and attitudes following experiential nutrition education combined with vegetable nugget provision. A quasi-experimental pretest-posttest nonequivalent control-group study included 164 second-grade students selected by simple random sampling within two elementary schools in East Kalimantan, Indonesia (82 per group). For 14 days, the intervention group received nutrition education and vegetable nuggets, while the control group continued usual activities. Baseline knowledge and attitude distributions differed significantly (p < 0.001). Knowledge changed in both groups (p < 0.001), although the control group mainly shifted from poor to moderate. Attitudes changed in the intervention group (p < 0.001) but not the control group (p = 0.276). Improvement was more frequent in the intervention group for knowledge (51.2% vs. 26.8%; exact p = 0.001) and attitudes (92.7% vs. 22.0%; p < 0.001).
THE EFFECT OF REBRANDING MANAGEMENT ON THE QUALITY OF HOSPITAL SERVICES: LITERATURE REVIEW Pandu Kridalaksana; Qurratul Aini
Journal of World Future Medicine, Health and Nursing Vol. 4 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v4i4.4345

Abstract

hospitals facing increasing competition, changing patient expectations, and digital transformation. This study aimed to analyze the influence of hospital rebranding management on healthcare service quality and to identify strategic mechanisms linking rebranding practices with patient perceptions and loyalty. A systematic literature review was conducted using articles published from 2017 to 2026 and retrieved from Scopus, ScienceDirect, Emerald Insight, ProQuest, and Sinta. An initial 723 records were identified, followed by screening and eligibility assessment, resulting in 45 articles for detailed assessment and 27 primary studies for final synthesis. Narrative synthesis was used to examine the relationship between rebranding components and the five SERVQUAL dimensions: tangibles, reliability, responsiveness, assurance, and empathy. The findings indicate that effective rebranding extends beyond changes in names, logos, and visual identity to encompass service redesign, organizational culture, employee engagement, digital transformation, and patient-centered care. Rebranding was particularly associated with improvements in tangible and responsiveness dimensions, while assurance and empathy were essential for sustaining patient trust and loyalty. The novelty of this review lies in integrating conventional corporate rebranding with humanistic-digital hospital branding, positioning patient trust as a critical mechanism between brand transformation and perceived service quality. These findings imply that hospital managers should implement rebranding as an organization-wide transformation strategy rather than a cosmetic marketing initiative to achieve sustainable service quality and competitive advantage.

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