Publication Search

The development of Artificial Intelligence (AI) has driven significant transformation in hospital management, particularly in operational efficiency, service quality, and patient safety. This study aims to analyze the implementation of AI in hospital management based on recent scientific evidence from 2020 to 2026. The method used was a systematic review guided by the PRISMA 2020 framework. Literature was retrieved from PubMed, ScienceDirect, SpringerLink, Google Scholar, and ProQuest. From 360 identified articles, a stepwise selection process was conducted, resulting in 15 articles that met the inclusion criteria. The findings indicate that AI contributes to improved operational efficiency through patient flow optimization, operating room management, workforce scheduling, and electronic medical record management. AI also enhances service quality through predictive data analytics and supports patient safety through risk detection and early warning systems. In conclusion, AI has strong strategic potential to support modern hospital management. However, its implementation still faces several challenges, including human resource readiness, data security, algorithmic bias, system interoperability, and investment requirements. Therefore, AI implementation should be carried out in a planned, ethical manner and evaluated from a health economics perspective.

Background: Accurate detection of renal cysts on CT urography requires high diagnostic precision, while manual interpretation by radiologists is susceptible to inter-observer variability and potential delays in clinical decision-making. These challenges underscore the need for a reliable automated detection system to support radiological assessment. Objective: This study aims to develop and evaluate the performance of the Neo-ZasAI application based on the YOLOv8 algorithm for the automatic identification of renal cysts. Methods: Employing a Research and Development design using the ADDIE model, the study encompassed needs analysis, model design, software development, system implementation using 200 CT urography images, and diagnostic performance evaluation. Classification results generated by Neo-ZasAI were compared with radiologist readings through confusion matrix analysis and ROC–AUC assessment. Results: The findings indicate that Neo-ZasAI achieved an accuracy of 97,5%, sensitivity of 96%, specificity of 99%, positive predictive value of 98,9%, and negative predictive value of 96,1%. The ROC analysis yielded an AUC of 0.988 (p < 0.001), demonstrating excellent discriminative capability and high concordance with radiologist interpretations as the diagnostic gold standard. Conclusion: These results suggest that Neo-ZasAI is capable of performing rapid, consistent, and accurate renal cyst detection and is thus feasible for implementation as a clinical decision support system in radiology, with potential integration into PACS workflows and further development to enhance model generalizability.

Diabetes Mellitus is one of the chronic diseases with high morbidity and mortality rates, making data-driven analysis necessary to understand patient mortality patterns. This study aims to analyze the mortality rate of Diabetes Mellitus patients based on age and length of hospitalization using a data mining approach with the K-Means Clustering method. The study employs a quantitative approach using secondary data obtained from the medical records of Diabetes Mellitus patients at Ibnu Sina Regional General Hospital, Gresik Regency, in December 2022. The dataset consists of 266 patient records with variables including age, length of stay, and final patient status. Data analysis was conducted through preprocessing stages, including data cleaning, transformation, and normalization, followed by the clustering process using the K-Means algorithm with the assistance of the RapidMiner application. The results show that patient data are divided into three clusters based on age ranges: 0–40 years, 41–55 years, and 56–90 years. The cluster with the age range of 56–90 years has the highest number of patient deaths compared to the other clusters. Meanwhile, the length of hospitalization does not show a significant effect on patient mortality. This study is expected to serve as a consideration for hospitals and health institutions in efforts to prevent and manage Diabetes Mellitus, particularly among the elderly population.

This study aims to analyze the spatial patterns of ambulance transport demand in Semarang City based on patients’ origin subdistricts, origin villages, and destination healthcare facilities. The analysis employed the K-Means Clustering algorithm as a data mining method to group areas according to similarities in the volume of ambulance requests. The dataset consisted of ambulance transport service records from January 2024 to September 2025, obtained from the Semarang City Health Office. The analytical procedures included data cleaning, normalization, determination of the optimal number of clusters using the Elbow Method, and cluster formation using K-Means. The results show two main clusters for subdistricts and destination healthcare facilities. High-demand subdistricts were generally densely populated areas such as Banyumanik and Pedurungan, with an average of 1,256 requests, while RSUP Dr. Kariadi emerged as the dominant referral facility with 3,893 requests. Meanwhile, village-level origins formed three clusters, with average demands of 549 (high), 190 (medium), and 36 (low). These findings are expected to support strategic planning for equitable ambulance fleet distribution and improved efficiency of patient transportation services in Semarang City.

This study examines the effectiveness of the Early Warning System (EWS) in anticipating and responding to mental health crises in conflict-affected regions of the Middle East through a systematic review of 47 scholarly articles published between 2014 and 2024. The meta-regression findings indicate a significant contribution of EWS implementation to the reduction of post-traumatic stress disorder (PTSD) symptoms with a coefficient of β = -0.67 (p < .001), as well as depressive symptoms with a coefficient of β = -0.59 (p < .001) among populations directly affected by armed conflict. Among 12,456 respondents analysed, 73.8% reported a reduction in anxiety symptoms following the implementation of EWS, with an effect size of d = 0.82 (95% CI [0.76, 0.88]). Digitally based early warning systems demonstrated a significantly higher level of effectiveness (OR = 2.34, 95% CI [1.98, 2.70]) than conventional systems, which are more manual and reactive. Moderator analysis indicated that age (β = -0.31, p < .01) and the duration of exposure to conflict (β = 0.44, p < .001) play important roles in moderating the relationship between EWS interventions and various mental health indicators. These findings expand upon the conclusions of Fu et al. (2020) and Salesi (2023), which previously explored psychosocial interventions in conflict zones, by adding a new dimension—examining digital technology and predictive algorithms within EWS frameworks. The study explicitly demonstrates that integrating machine learning models into EWS can enhance the predictive accuracy of potential mental health crises to 84.6%, representing a novel contribution that has not been comprehensively documented in prior academic literature

Infertility in women is a reproductive health issue that requires early intervention to prevent long-term effects. With the advancement of technology, electronic medical records data can be utilized to assist in the diagnosis and classification of infertility risks. This study aims to classify the risk of infertility in female patients using the Naive Bayes algorithm based on medical record data, which includes factors such as age, health history, and medical test results. The data used in this study were obtained from hospitals and health clinics focused on managing infertility patients. The methods applied include data preprocessing, applying the Naive Bayes algorithm for classification, and evaluating the model using accuracy, precision, recall, and F1-score metrics. The results of the study show that the Naive Bayes algorithm provides fairly accurate classification in predicting infertility risks. The analysis-generated graph shows the distribution of infertility risks, with 60% of patients having a positive risk (1) and 40% having a negative risk (0). This study also suggests implementing the classification results in the form of counseling for patients to increase awareness and encourage early preventive actions. Thus, the Naive Bayes algorithm can be an effective tool in assisting healthcare providers in data-driven decision-making to address infertility risks in female patients.

Hair loss is one of the common health problems experienced by many people and often causes psychological impacts, particularly on self-confidence. The factors contributing to hair loss are diverse, ranging from genetics, diet, and stress to lifestyle. The lack of public knowledge about these risk factors, as well as the low level of digital literacy in the use of predictive technology, makes it difficult for people to take early preventive measures. This community service activity aims to provide education and simple training on predicting hair loss risk using the Support Vector Machine (SVM) algorithm for residents of Rantau Prapat Village. The implementation methods include a pre-test to measure initial understanding, interactive counseling on hair loss risk factors, practical simulation of risk prediction using SVM based on a simple dataset, and evaluation through a post-test. The results of the activity showed a significant increase in participants’ understanding, from an average of 45.2% in the pre-test to 81.6% in the post-test, with a participant satisfaction level reaching 92%. This counseling not only improved health literacy but also introduced the practical application of artificial intelligence in the health sector.

Speech delay in children is a developmental issue commonly encountered in society, which can affect various aspects of a child's life, including communication, social interaction, and academic development. Early detection of speech delay is crucial for providing appropriate interventions to minimize its long-term impact on the child. This study aims to introduce the use of machine learning algorithms in detecting speech delay symptoms in children. Three machine learning algorithms applied in this study are Naïve Bayes, C4.5, and K-Nearest Neighbor (K-NN). These algorithms are used to classify speech delay symptoms based on health data, medical history, and environmental factors such as speaking habits and eating patterns. The outreach was conducted at Puskesmas Kota Rantauprapat with the involvement of parents and healthcare providers as participants. The experimental results showed that all three algorithms performed well in terms of accuracy, though with varying error rates. Naïve Bayes achieved relatively high accuracy but had a higher false positive rate compared to C4.5 and K-NN. C4.5 provided more stable results and was easier to interpret due to its decision tree structure. Meanwhile, K-NN performed better with data that had irregular distribution. This outreach is expected to assist both the community and healthcare providers in early detection of speech delay in children, providing a more efficient and affordable means for early intervention, which ultimately leads to better outcomes for children with speech delay.

Financial management in hospitals is a crucial aspect to ensure the sustainability of quality health services. However, the complexity of financial data, which involves various budget components, often creates challenges for hospital management in conducting accurate analysis and budget planning. Therefore, a data-driven approach is required to present financial information in a structured and comprehensible manner. This study examines the application of the K-Means Clustering method to classify hospital financial data based on expenditure characteristics and patterns, with a case study at RSUD Rantau Prapat as part of a community service program. The financial data were analyzed through pre-processing stages, determination of the optimal number of clusters using the Elbow Method, and the implementation of the K-Means algorithm to generate more representative budget groups. The results indicate that clustering hospital financial data into three main categories—routine operational costs, medical service costs, and administrative/personnel costs—provides clearer insights into budget distribution. This supports hospital management in identifying budget allocation priorities, detecting potential inefficiencies, and improving the overall efficiency of financial governance. The limitation of this study lies in the data scope, which only involved a single hospital, thus restricting its generalizability. Future research is recommended to expand the scope to multiple hospitals and integrate alternative clustering methods to obtain more comprehensive results.

Sleep is a fundamental human need that plays a crucial role in maintaining both physical and mental health. Poor sleep quality can trigger a variety of health problems, ranging from decreased concentration to an increased risk of chronic diseases. The complexity of factors influencing sleep quality—such as stress levels, heart rate, blood pressure, physical activity, and lifestyle—makes its assessment difficult through direct observation alone. Therefore, data mining approaches are increasingly utilized to identify relevant patterns in sleep-related data. This study aims to compare the performance of the C4.5 (Decision Tree) algorithm and the Naïve Bayes algorithm in predicting sleep quality using the Sleep Health and Lifestyle dataset, which contains information from 374 respondents. The research method applied is a quantitative comparative approach employing classification techniques with 10-fold cross-validation to ensure robust evaluation. Model performance is assessed using accuracy, precision, and recall metrics to provide a comprehensive understanding of the effectiveness of each algorithm. The findings indicate that the C4.5 algorithm achieves an accuracy of 96.26% and offers advantages in terms of interpretability through its decision tree visualization, enabling easier understanding of variable relationships. In contrast, the Naïve Bayes algorithm demonstrates superior predictive performance, achieving an accuracy of 98.66% along with consistently high precision and recall across nearly all classes. These results suggest that Naïve Bayes is more effective for predictive tasks involving sleep quality, while C4.5 remains highly valuable when the goal is to interpret variable interactions and decision rules. Overall, this research highlights the potential of data mining techniques in health informatics, particularly in improving the understanding and prediction of sleep quality, which in turn can contribute to better prevention and management of sleep-related health issues.

Hypertension is one of the most prevalent non-communicable diseases and a major risk factor for heart disease, stroke, and kidney disorders. The high prevalence of hypertension cases in the community, particularly in the working area of Puskesmas Kota Rantau Prapat, highlights the urgent need for more effective early detection efforts to prevent severe complications in the future. However, the limited capacity of healthcare workers in utilizing data analysis technologies has resulted in hypertension risk detection being dominated by conventional methods, which are often less accurate and inefficient. To address this issue, this community service program was conducted through training on the application of the Random Forest algorithm to analyze patients’ medical history data in order to detect hypertension risks. The training method included an introduction to the fundamentals of machine learning, data pre-processing stages, implementation of the Random Forest algorithm, and interpretation of prediction results. The outcomes of the program demonstrated that healthcare workers were able to understand the use of data analysis technologies to support more accurate early detection of hypertension. Furthermore, the participants gained practical skills in utilizing medical datasets to produce predictions that can serve as a decision-support tool for preventive medical actions.Thus, this training contributed to enhancing the capacity of community healthcare workers in integrating machine learning-based technologies into preventive healthcare services. This program is expected to serve as an initial step toward developing more effective, efficient, and sustainable data-driven health systems.

Child developmental delay is a public health issue that needs to be identified early to prevent long-term impacts on children’s quality of life. In Rantau Prapat Sub-district, cases are still found among toddlers with undernutrition, incomplete immunizations, and suboptimal developmental stimulation, which may pose risks of growth and developmental delays. This study aims to apply the Naive Bayes method in identifying child developmental delays based on health data collected through medical records and questionnaires. The research method includes data collection, pre-processing (cleaning, transformation, and normalization), classification using the Naive Bayes algorithm, and model validation with the k-fold cross-validation technique. The results showed that out of 150 toddler data samples, 30.7% experienced developmental delays, with the dominant influencing factors being nutritional status and immunization completeness. The Naive Bayes algorithm achieved an accuracy rate of 87.3% with a precision of 84.1%, recall of 85.7%, and F1-score of 84.9%. These findings demonstrate that Naive Bayes can be used as a decision support system in the early identification process of child developmental delays. Therefore, the results of this study are expected to assist healthcare workers, particularly midwives, in improving the quality of early detection and delivering more targeted interventions for children in the Rantau Prapat area.

Typhoid fever is an infectious disease caused by the bacterium Salmonella typhi, commonly found in developing countries, including Indonesia. Prompt and accurate treatment is crucial to prevent serious complications in patients. One way to assist in diagnosing typhoid fever is by applying machine learning methods to classify patient data. The Naive Bayes method is one of the machine learning algorithms frequently used in medical data classification due to its strong ability to handle large and complex datasets. This article discusses the application of the Naive Bayes method for classifying typhoid patient data at Rantauprapat General Hospital (RSUD Rantauprapat). By utilizing medical data that includes clinical symptoms, laboratory test results, and patients’ medical histories, the Naive Bayes model can provide fairly accurate predictions regarding the likelihood of a person having typhoid fever. The research findings indicate that Naive Bayes is reliable in predicting typhoid diagnoses with adequate accuracy, thereby supporting healthcare professionals in making faster and more precise decisions. It is expected that the implementation of this method can accelerate the diagnostic process and improve the quality of healthcare services at RSUD Rantauprapat, as well as in other regions.

This study comprehensively examines the impact of social media on the formation and intensification of collective trauma in the Middle East through a digital meta-analytical approach synthesizing 47 empirical studies, encompassing a total of 31,842 participants, published between 2015 and 2024. The results reveal a strong and statistically significant correlation between the intensity of social media use and levels of collective trauma, with a correlation coefficient of r = 0.67 and a p-value of < 0.001, indicating a consistent and substantive relationship. Furthermore, regression analysis indicates that exposure to violent content through social media accounts for 43.2 percent of the variance in communal post-traumatic stress symptoms, affirming the role of digital media as a significant catalyst in amplifying collective psychological responses to conflict in the Middle East. Daily social media use exceeding five hours was found to significantly increase the risk of experiencing collective trauma by 2.8 times, with an odds ratio of 2.84 and a 95 percent confidence interval ranging from 2.31 to 3.49. Platforms such as Facebook and Twitter demonstrated a more substantial influence in widely disseminating traumatic experiences, with a beta coefficient of 0.58, compared to Instagram, which had a relatively lower influence with a beta value of 0.34, indicating that the structural and technological logic of each platform mediates the psychological transmission effect. Thematic analysis across studies revealed three primary mechanisms through which trauma is transmitted via social media: first, the amplification of traumatic narratives, accounting for 41.3 percent of identified patterns; second, the normalization of violence at 32.7 percent; and third, the reinforcement of collective identity based on shared traumatic experiences at 26.0 percent, thereby creating a digital ecosystem prone to the social accumulation of negative emotional states. These findings substantially expand the scope of prior research, such as that conducted by Atallah in 2017 and Nasciutti and Rahbari-Jawoko in 2021, which focused more narrowly on individual trauma, by highlighting a broader collective dimension and emphasizing the specific roles of various digital platforms in reinforcing these psychosocial dynamics. This study also identifies a novel pattern of both theoretical and practical significance, namely that algorithmic content recommendation contributes significantly to the formation of closed psychological echo chambers of trauma, intensifying exposure to traumatic content and deepening the affective impact of Middle Eastern conflict within digital spaces, with a significance level of p < 0.001. Accordingly, these findings underscore the urgent need for strategically designed and contextually grounded digital interventions to mitigate the burden of collective trauma in communities affected by protracted armed conflict in the Middle East.

The Next-Gen Global Health 6.0 initiative offers an integrative model employing genomics, nano-immunotherapies, and artificial intelligence (AI) to address the escalating complexity of global health issues, particularly the convergence of infectious and chronic diseases. This framework advances precision medicine by integrating real-time genomic surveillance with AI algorithms, enabling timely prediction and response to outbreaks, as well as tailored therapeutic approaches. Nano-immunotherapies play a critical role in modulating immune responses with high specificity, especially in chronic infections and diseases resistant to conventional treatments. Through these synergistic technologies, the Next-Gen Global Health 6.0 approach aims to transcend traditional healthcare boundaries, offering scalable, data-driven interventions that are adaptable to varying resource levels worldwide. Emphasizing accessibility and equity, this framework highlights the necessity for innovative health policies and interdisciplinary collaboration to optimize deployment in underserved regions, ultimately contributing to sustainable, resilient healthcare systems prepared for evolving global health challenges.

Background: Iterative Reconstruction (IR) method was first applied to CT in 1960 and successfully used for the first time in clinical research by reconstructing 128 x 128 images according to image metrics and using high-resolution images of 512 x 512 for special research activities such as image evaluation. artifacts and noise. In 2008, the development of IR can improve image quality and reduce the amount of radiation in clinical CT diagnosis. Iterative reconstruction promises to improve image quality while reducing radiation dose. This has been demonstrated in CT of the thorax, coronary arteries, abdomen, spine and neck, paranasal sinuses, and head. Sinogram-Afirmed Iterative Reconstruction (SAFIRE) is one of the iterative algorithm reconstruction methods that uses noise modeling techniques, Sinogram-Afirmed Iterative Reconstruction (SAFIRE), promises to improve Cranial CT (CCT). In this new technique, raw data-based iteration for artifact reduction is combined with image-based iteration using smooth regularization that estimates the variance of image noise in various directions at each image pixel and adjusts it with a spatial variance regularization function simultaneously. Methods: This study is a literature review, where literature exploration is carried out on various databases with keywords such as, Reference sources used in compiling this article include google scollar, as well as articles in English and Indonesian scientific journals. Results: Itarative Reconstruction (IR) Head CT Scan, SAFIRE includes reducing or adding noise to the image results, artifacts, acquisition time, increasing SNR and CNR and reducing dose in the examination. Conclusion: Analysis of Safire results in Head CT-Scan Imaging has an Itarative Reconstruction (SAFIRE) procedure, the role of safire in head CT scans is to optimize noise in the Reconstructed image and can reduce artifacts in the image and increase SNR, CNR in the Reconstructed results so that it can provide better information.    

Artificial intelligence (AI) is transforming paediatric diabetes management, offering innovative solutions for monitoring, treatment, and prediction. This mini-review explores how AI is being utilised to improve the care of children with diabetes mellitus, focusing on its application in glucose monitoring systems, predictive algorithms, and personalised treatment plans. The study synthesises recent advancements in AI technologies, examining their impact on enhancing the accuracy of diagnosis, reducing the burden on healthcare providers, and improving patient outcomes. Through a systematic review of the literature, key AI tools and models that have shown promise in paediatric diabetes care are identified. The findings highlight the potential of AI to revolutionise diabetes management, with implications for both clinical practice and future research. However, challenges remain in ensuring the ethical implementation and integration of these technologies into existing healthcare systems. The paper concludes with recommendations for advancing AI applications in this field, emphasising the need for continued innovation and collaboration between healthcare professionals and AI developers.

Research This investigate consumer sentiment analysis to halal products using social media data with utilise intelligence artificial intelligence (AI). With background behind increasing estimated market value of halal products reach USD 2.02 Trillion in 2024, understanding deep about opinion consumer become very important. Research This adopt approach quantitative, using secondary data from social media platforms such as Twitter, Instagram, and Facebook. Through Natural Language Processing techniques and algorithms learning machine, sentiment analysis is performed For identify pattern positive, negative and neutral in perception consumers. Research results show that 60% of the total 10,000 reviews had positive sentiment, with halal food products receiving the highest positive sentiment. Factors influencing consumer sentiment include product quality, price, and transparency of information. In addition, the study found that the use of AI in sentiment analysis provides advantages in efficiency and accuracy, and is able to capture nuances in consumer opinions that are not Possible done by manual analysis. From the analysis this, can concluded that the marketing strategy of halal products must focus on improving quality and providing clear information about halal certification. This study not only provides insight for halal industry players, but also enriches the literature related to AI, sentiment analysis, and sharia economics.