(1) Introduction: The objective of this study was to evaluate the patient experience with the Mawid application during the COVID-19 pandemic in Al Hassa, Saudi Arabia. (2) Methodology: A quantitative cross-sectional survey was designed to evaluate the patient experience with the Mawid app during the COVID-19 pandemic in Al Hassa, Saudi Arabia. A total of 146 respondents completed the questionnaire. (3) Results: More than half of the participants (65.8%) opined that application was easy to use. Furthermore, 65.1% of the participants considered it to be very easy and easy to search for the required information; and 63.7% of the respondents reflected that it was easy to book an appointment. There was a statistically significant difference between the ease of searching for the required information (p-value = 0.006); the ease of undoing an unwanted move and gender (p-value = 0.049); the ease of searching for the required information and educational level (p-value = 0.048); the ease of booking an appointment and educational level (p = 0.049); and the ease of searching for the required information and the labor sector of the respondents (p value= 0.049) among the genders. No significant differences were identified among the age groups. (4) Conclusions: Overall, most participants suggested that the Mawid app was easy to use and had a potentially useful set of features to help mitigate and manage the COVID-19 pandemic in Al Hassa, Saudi Arabia.

Context: Airborne particulate matter (PM) attracts heightened attention due to its implication in various diseases, especially cardiovascular diseases. Although numerous epidemiological studies have been published worldwide in developing countries on risks associated with exposure to PM, such studies are still scarce in developing countries such as Saudi Arabia. Objective: To examine the association between the concentration of airborne particulate matter (PM) and hospital admissions resulting from cardiovascular diseases (CVD) in the Eastern Region of Saudi Arabia, specifically in the cities of Dammam and Khobar. Methodology: The daily concentrations of PM₁₀ and PM_2.5 were obtained from 10 monitoring stations distributed around the two hospitals. There was an examination of the discharge data of patients diagnosed with cardiac arrhythmias, acute myocardial infarction, and heart failure as their primary diagnoses. The data were obtained from two big governmental hospitals in the Eastern Region. The primary cause of hospital admission of 259 patients was identified as acute cardiac condition. Results: For PM₁₀ and PM_2.5, the 24 h mean was calculated as 101.2 and 37.1 µg/m³, respectively; such means are considered higher than the Air Quality Guidelines (AQGs). We found evidence of an increased risk of cardiovascular events for long-term exposure to PM_2.5–10 concentrations, and a correlation with the IHD hospital admission within 6 days of the peak PM₁₀ or PM_2.5 concentration. In addition, the increased PM_2.5 concentration also had a correlation with hospital admissions; however, analysis shows an increase in mortality at lag1, lag2, and lag3 prior to hospital admission. Conclusions: Hospital admissions for several cardiovascular diseases acutely increase in response to higher ambient PM concentrations. It is recommended that residents need to use personal protection, especially those residents with cardiovascular disease, while the government needs to strengthen the governance of air pollution in areas with lighter air pollution.

Type of the article: Research ArticleAbstractThis study examines how patients’ health insurance claims were denied by different insurance providers at a Saudi Academic Medical Center (AMC), exploring the reasons for these rejections, their relationship to claim characteristics, and the factors that predict health insurance claim rejections. A descriptive study design was employed, involving a retrospective review of all insurance claims submitted by both inpatients and outpatients between January and December 2023 at a tertiary care AMC in Saudi Arabia. Following data screening using the UCAF 2.0 form, all denied insurance claims cases (n = 1,117) were subjected to qualitative analysis. The majority of rejected health insurance claims were submitted by female patients (56.9%) and outpatients (93.6%). Among the insurance companies studied, “Tawuniya” rejects the most insurance claims (n = 730). Variables such as age, gender, and insurance company were significantly associated with the reasons for denying claims (p < 0.05). Furthermore, variables such as age, cost, department type (inpatient/outpatient), and the month of claims are significant predictors of claim rejections (p < 0.05). However, gender, insurance companies, and clinical diagnosis were not significant (p > 0.05). The primary reasons for insurance claim denials in Saudi Arabia are missing medical data, system errors, and non-coverage of specific conditions. This study will help insurance companies and patients identify trends and reasons for claim rejections, enabling them to implement more effective preventive and corrective measures.AcknowledgmentsThe authors expressed their gratitude to Imam Abdulrahman Bin Faisal University for granting permission [IRB-2024-03-188] to conduct this study.

Purpose: This study examines the economic impact of fraud, waste, and abuse (FWA) in health insurance systems and synthesizes evidence on governance, legislative, regulatory, and technology-based interventions aimed at improving financial sustainability. It further evaluates the implications of these interventions for healthcare system resilience and identifies research gaps to inform future policy and practice. Method: This study employed a systematic literature search followed by a narrative synthesis to examine the economic impact of health insurance FWA. A comprehensive search of Scopus, Web of Science, MEDLINE, Embase, PubMed, OpenAlex, and Dimensions was conducted for studies published up to 24 November 2025, complemented by backward and forward citation tracking using the Inciteful.xyz platform. Following screening and eligibility assessment, 30 studies were included in the final narrative synthesis. Results: The findings indicate that FWA imposes substantial direct and indirect economic costs on health insurance systems by increasing financial losses, reducing operational efficiency, weakening accountability, and threatening long-term fund sustainability. Evidence suggests that effective mitigation requires an integrated approach combining preventive monitoring, robust governance, legislative enforcement, whistleblower protections, and advanced analytical technologies. Artificial intelligence, machine learning, blockchain, and automated auditing systems demonstrated promising performance in improving fraud detection under experimental conditions; however, evidence supporting their effectiveness in large-scale operational healthcare settings remains limited, and implementation challenges related to scalability, interoperability, privacy, and governance should be considered. Publication trends indicate increasing international research interest in FWA, although evidence remains concentrated in a limited number of countries.

The occurrence of foodborne diseases is a considerable public health issue, especially in areas that are quickly becoming urbanized with intricate food delivery systems. In this paper, we present a machine learning-based model for predicting outbreaks, explainability, and spatial risk propagation, validated through a multiyear data set of an epidemiological nature from 12 cities in the Eastern Province of Saudi Arabia (2021–2025). The final data set includes 61 cases and 13 engineered features. In the current research, the proposed architecture uses XGBoost to predict outbreaks, alongside using the random forest for predicting severity and support vector machine (SVM) for comparisons. The XGBoost classifier demonstrates an evenly balanced performance (accuracy = 0.85, precision = 0.78, recall = 0.78) on the testing set. Due to the size of the dataset, the results are provided with the estimation of uncertainty (rather than the exact numbers). Using leakage-safe repeated stratified cross-validation, the mean AUC equals 0.64 [95% interval = (0.20, 1.00)], and the leave-one-year-out validation method is not stable (mean AUC 0.47). Differences between the models (e.g., better single-split cross-validation AUC for SVM) are within confidence intervals. Interpretability is improved by using the SHAP framework to measure feature importance, which shows that the main factors are hospitalization and the severity of symptoms. The graph module also helps in understanding the propagation of disease risk between cities, highlighting the importance of well-connected metropolitan areas as disease hubs. Moreover, the use of a locally deployed Mistral LLM makes the generated explanations more readable. The findings show that our approach presents an appropriate balance of predictiveness, interpretability, and spatial knowledge. With only 61 data points and 11 outbreaks reported, this research is clearly not meant to be an early warning system, but rather a proof-of-concept on how one might be designed. In order to ensure reproducibility, the preprocessing pipeline and synthetic dataset generator have been made available to the community.

Background: Osteoporosis is a major public health concern, and early detection through dual-energy X-ray absorptiometry (DXA) has been pivotal. However, evidence on how insurance coverage relates to the distribution of patients receiving DXA examinations in Saudi Arabia remains limited. This study examines the distribution of patients receiving DXA examinations across different insurance types and healthcare settings among adult patients in Saudi Arabia. Methods: This retrospective observational study used de-identified electronic health record data from a public tertiary academic center and a large private healthcare system in Saudi Arabia between 1 January 2023 and 28 February 2026. Dual-energy X-ray absorptiometry (DXA) was employed as an imaging method used to measure bone mineral density and support osteoporosis diagnosis and fracture-risk assessment. Adult patients aged ≥18 years with completed DXA examinations recorded in radiology service records were included. This study assessed the distribution of patients receiving DXA examinations across insurance types, healthcare setting, ordering department, and year of service. Insurance type was categorized as government coverage, private insurance, or self-pay. Descriptive statistics, chi-square tests, one-way ANOVA, Cramér’s V, and standardized residuals were used to compare distribution patterns across groups. Results: The data of 8930 DXA recipients were analyzed. Clear differences were observed in the distribution of patients by insurance type and healthcare setting (p < 0.001). Privately insured patients were predominantly treated in private facilities, whereas government-insured and self-pay patients were primarily concentrated in public healthcare facilities. Significant variations were also observed across healthcare departments and over time, demonstrating strong system-level and financial stratification in the distribution of patients receiving DXA examinations. Conclusions: The findings of this study demonstrate significant differences in the distribution of patients receiving DXA examinations according to insurance type and healthcare setting. Notably, self-pay patients were predominantly managed in public healthcare facilities, whereas privately insured patients primarily received DXA examinations in private healthcare settings. These observed patterns suggest that differences in healthcare organization and insurance financing may be associated with where patients receive DXA examinations; however, because this study included only individuals who underwent DXA examinations, the findings should not be interpreted as measures of screening uptake, access, or equity among all patients eligible for osteoporosis screening. Future healthcare strategies should focus on strengthening equitable insurance coverage, improving coordination between public and private healthcare sectors, standardizing referral pathways, and supporting integrated preventive care in line with Saudi Arabia’s Vision 2030 healthcare transformation.