The objective of this work was to formulate co-loaded bilayer tablets containing ezetimibe (EZB) and atorvastatin (ATC). ATC loaded in the immediate-release (IR) layer is an HMG CoA reductase inhibitor, while EZB, added in the sustained-release (SR) layer, is a lipid-lowering agent. This study was conducted to evaluate the effects of polymer on the formulation and characterization of bilayer tablets, as well as the therapeutic impact of the concurrent use of both drugs having a sequential release pattern. To obtain the optimized results, four different formulations with variable compositions were developed and evaluated for different parameters. The drug release studies were carried out using a type II dissolution apparatus, using phosphate buffer solution (PBS) of 1.2 pH for IR of EZB for an initial 2 h, followed by 24 h studies for ATC in PBS 6.8 pH. The IR layer showed rapid drug release (96%) in 2 h, while 80% of the ATC was released in 24 h from the SR layer. Locally obtained, 6-week-old female albino rats were selected for in vivo studies. Both preventive and curative models were applied to check the effects of the drug combination on the lipid profile, atherosclerosis and physiology of different organs. Studies have shown that the administration of both drugs with different release patterns has a better therapeutic effect (p < 0.05), both in preventing and in curing hyperlipidemia. Conclusively, through the sequential release of ATC and EZB, a better therapeutic response could be obtained.

Postpartum depression (PPD) is a significant global health concern, impacting the well-being of mothers and their infants during the postpartum period. Digital health solutions, particularly mobile applications, have emerged as promising tools for PPD management. This study systematically evaluates the landscape of PPD apps, identifying 115 relevant applications available on iOS and Android platforms. Through a comprehensive analysis, we assess their availability, popularity, key features, and technological advancements. Many of these apps integrate artificial intelligence (AI), machine learning, and wearable devices to enhance risk assessment, personalized support, and real-time monitoring. While these apps offer improved access to mental health resources, early screening, and self-management tools, considerable variability exists in their quality, functionality, and adherence to privacy and security standards. The lack of standardized evaluation frameworks raises concerns about their reliability and clinical effectiveness. Ensuring evidence-based practices, rigorous validation, and quality assurance is critical to optimizing their impact. Our findings emphasize the need for further research, standardized app evaluation frameworks, and implementation studies to facilitate clinical integration. Additionally, expanding multilingual support and telehealth features can improve accessibility for diverse populations. Strengthening mHealth solutions through these advancements can enhance maternal mental health care, benefiting both patients and healthcare providers.

Electronic health information systems (EHIS) are considered a backbone for healthcare planning and quality services. This study was designed to explore the acceptance, reliability, and satisfaction of the end users' experience with the hospital electronic health information system. We also investigated the perception of data quality by the users who were directly involved in data entry. We conducted a questionnaire based cross-sectional survey to collect quantitative data from different EHIS users. The questionnaire contained six sections: demographic user information; general HIS assessment; accessibility and availability of computer terminals in the hospital; EHIS and the patient care; user satisfaction with the HIS and perception of data quality. Desktop computers were available throughout the hospital, but the hospital was lacking handheld computers or computers on wheels. Participants of the study were satisfied with the data entry and retrieval process but they were lacking job training related to troubleshooting. EHIS users were not aware and prepared for the downtime of the system and procedures were also not clear to them. Regarding the perception of data quality, most of the participants responded that data is of adequate quality. There is a need for proper technical support and enhance the hospital's networking speed for better response. Laptops and hand-held computers are the need of time for data entry in critical situations and during wards visit. This can also enhance the quality of data, and reduce the chances of data loss.

Background: Costing information can help to improve the quality of medical care budgeting. It can also improve the efficient allocation of resources and patient outcomes. In recent years, cost analysis studies have increased awareness of hospital cost structures that are considered important to improving the quality and efficiency of healthcare services. The objective of this study was to estimate the inpatient unit cost of healthcare services in a secondary hospital, Saudi Arabia. Methodology: A cross-sectional retrospective approach was applied to categorize the inpatients discharged from the hospital from January 2018 to December 2018. A top-down costing method for cost estimation was applied because it is less time-consuming, cheaper, and at the same time, provides an accurate method for the regular hospital costing exercise. Results: The hospital departments were divided into three cost centers, namely, overhead center, intermediate cost center and final care cost center. We found that the overhead cost center that includes the supporting departments of the hospital holds 40.17% of the total hospital cost. Intermediate cost center and final care cost center consumed 25.50% and 34.33% respectively. Among inpatients wards, the Surgical ward had the highest operational cost (39.27%) as well as had the highest per patient cost. Human resource consumed highest resources (75%) of the hospital in terms of salaries. Conclusion: The cost structure of the hospital was not remarkable and needs revolutionary changes to adopt the new payment mechanism envisioned in 2030 Saudi vision. However, this study can be used as benchmark to conduct costing studies in other hospitals of the country.

Background: Healthcare systems strive to provide high-quality services while optimizing resource utilization and cost-effectiveness. Achieving these goals necessitates using scientific tools or methodologies to assess costs before implementing cost-reduction measures. Diagnosis related group DRGs) which is also called case-mix is a costing method in health. This study focused on a secondary hospital located in the Jazan region of Saudi Arabia. Our goal was to conduct a patient-level healthcare service cost analysis using the Case-mix system. The purpose of this current study was to identify the cost of DRG for each inpatient and compare it with previous price of services of inpatients. Materials & Methods: To achieve this, we conducted a retrospective cross-sectional analysis, classifying healthcare service costs they incurred throughout the year 2018 for inpatients diagnosis and methods. Results: Our findings revealed a notable disparity between DRG-based costing, amounting to SAR 138,147,926, and the average costing, which stood at SAR 118,799,359. The DRG-based costing method demonstrated excellent reliability and better representation of patient services. As a result, we recommend its use for reimbursement purposes.