Objective In this study, we assess the performance of public hospitals in Saudi Arabia. We detect the sources of inefficiency and estimate the optimal levels of the resources that provide the current level of health services. We enrich our analysis by employing locations and capacities of the hospitals. Design We employ data envelopment analysis (DEA) to measure the technical efficiency of 91 public hospitals. We apply the input-oriented Charnes, Cooper and Rhodes, and Banker, Charne, Cooper models under Constant and Variable Returns-to-Scale. The assessment includes four inputs, and six output variables taken from the Ministry of Health databases for 2017. We conducted the assessment via PIM-DEA V.3.2 software. Setting Ministry of health-affiliated hospitals in the Kingdom of Saudi Arabia. Results Findings identified 75.8% (69 of 91) of public hospitals as technically inefficient. The average efficiency score was 0.76, indicating that hospitals could have reduced their inputs by 24% without reduction in health service provision. Small hospitals (efficiency score 0.79) were more efficient than medium-sized and large hospitals. Hospitals in the central region were more efficient (efficiency score 0.83), than those located in other geographical locations. More than half of the hospitals (62.6%) were operating suboptimally in terms of the scale efficiency, implying that to improve efficiency, they need to alter their production capacity. Performance analysis identified overuse of physician’s numbers and shortage of health services production, as major causes of inefficiency. Conclusion Most hospitals were technically inefficient and operating at suboptimal scale size and indicate that many hospitals may improve their performance through efficient utilisation of health resources to provide the current level of health services. Changes in the production capacity are required, to facilitate optimal use of medical capacity. The inefficient hospitals could benefit from these findings to benchmarking their system and performance in light of the efficient hospital within their capacity and geographical location.

Community-acquired infections caused by Klebsiella pneumoniae (K. pneumoniae) have become a significant global health concern, particularly with the emergence of hypervirulent strains (hvKP). These strains are associated with severe infections, such as pyogenic liver abscesses, even in otherwise healthy individuals. Initially reported in Taiwan in the 1980s, hvKP has now spread worldwide. The pathogenicity of hvKP is attributed to an array of virulence factors that enhance its ability to colonize and evade host immune defenses. Additionally, the convergence of hypervirulence with antibiotic resistance has further complicated treatment strategies. As a member of the ESKAPE group of pathogens, K. pneumoniae exhibits high resistance to multiple antibiotics, posing a challenge for healthcare settings. This review provides a comprehensive overview of hvKP, highlighting its structural and pathogenic differences from classical K. pneumoniae strains, key virulence factors, mechanisms of antibiotic resistance, and the increasing threat of multidrug-resistant hvKP. Lastly, we discuss current treatment guidelines and emerging therapeutic strategies to combat this formidable pathogen.

Antibiotic resistance is a growing global health threat, with critical care settings representing one of the most vulnerable arenas due to the high burden of infection and frequent empirical antibiotic use. Rapid and precise diagnosis of infectious pathogens is crucial for initiating appropriate therapy, minimizing unnecessary antimicrobial exposure, and supporting effective stewardship programs. This review explores how innovative diagnostic technologies are reshaping infection management and antimicrobial stewardship in critical care. We examine the clinical utility of molecular assays, multiplex PCR, MALDI-TOF mass spectrometry, metagenomic sequencing, point-of-care (POC) diagnostics, and emerging tools like biosensors and AI-powered predictive models. These platforms enable earlier pathogen identification and resistance profiling, facilitating timely and targeted therapy while minimizing unnecessary broad-spectrum antibiotic use. By integrating diagnostics into stewardship frameworks, clinicians can optimize antimicrobial regimens, improve patient outcomes, and reduce resistance selection pressure. Despite their promise, adoption is challenged by cost, infrastructure, interpretation complexity, and inequitable access, particularly in low-resource settings. Future perspectives emphasize the need for scalable, AI-enhanced, and globally accessible diagnostic solutions. In bridging innovation with clinical application, diagnostic advancements can serve as pivotal tools in the global effort to curb antimicrobial resistance in critical care environments.

Background: The assessment of hospital efficiency is attracting interest worldwide, particularly in Gulf Cooperation Council (GCC) countries. The objective of this study was to review the literature on public hospital efficiency and synthesise the findings in GCC countries and comparable settings.Methods: We systematically searched six scientific databases, references and grey literature for studies that measured the efficiency of public hospitals in appropriate countries, and followed PRISMA guidelines to present the results. We summarised the included studies in terms of samples, methods/technologies and findings, then assessed their quality. We meta-analysed the efficiency estimates using Spearman’s rank correlations and logistic regression, to examine the internal validity of the findings.Results: We identified and meta-analysed 22 of 1128 studies. Four studies were conducted in GCC nations, 18 came from Iran and Turkey. The pooled technical-efficiency (TE) was 0.792 (SE ± 0.03). There were considerable variations in model specification, analysis orientation and variables used in the studies, which influenced efficiency estimates. The studies lacked some elements required in quality appraisal, achieving an average of 73%. Meta-analysis showed negative correlations between sample size and efficiency scores; the odd ratio was 0.081 (CI 0.005: 1.300; P value = 0.07) at 10% risk level. The choice of model orientation was significantly influenced (82%) by the studied countries’ income categories, which was compatible with the strategic plans of these countries.Conclusions: The studies showed methodological and qualitative deficiencies that limited their credibility. Our review suggested that methodology and assumption choices have a substantial impact on efficiency measurements. Given the GCC countries’ strategic plans and resource allocations, these nations need further efficiency research using high-quality data, different orientations and developed models. This will establish an evidence-based knowledge base appropriate for use in public hospital assessments, policy- and decision-making and the assurance of value for money.

Type 2 Diabetes mellitus is a major public health concern with an alarming global growth rate. According to the World Health Organization (WHO), Saudi Arabia ranks seventh in the world and second in the Middle East for the largest estimated burden of diabetic cases. Evidence shows that pharmacist-led care programs can be beneficial for the effective treatment of diabetes mellitus. Current study was aimed to evaluate the impact of Pharmacist-Based Diabetic Intervention (PDIM) for Type 2 Diabetes patients on knowledge of the disease, adherence to medications and self-care practices during the first wave of COVID-19. A multi-arm pre-post study was conducted among type 2 diabetic patients from April to October 2021 in Sakaka, Saudi Arabia. Patients were randomly divided into an intervention and a control group. The intervention group received the PDIM, whereas the control group only received the usual care. The pharmacist-based diabetes intervention model consisted of a diabetic educational module and medication improvement strategies. Furthermore, the intervention group also received specific telepharmacy services (calls, messages or emails) to address their medication-related problems, inquire about medication adherence and follow-up. At the end of six months, disease knowledge, self-care practices, and medication adherence score were analyzed. Furthermore, HbA1c and lipid profile were also compared. A total of 109 patients were included in the study. A significant difference was observed in the knowledge score between the intervention and control group (16.89 ± 2.01 versus 15.24 ± 2.03, p-value < 0.001). Similarly, self-care practices also improved in the intervention group as compared to the control group (4.39 ± 1.10 versus 3.16 ± 0.97, p-value < 0.001). Furthermore, the medication adherence and HbA1c significantly improved during between the group analysis (p < 0.05). Our study demonstrates that pharmacist-based diabetes intervention model is effective in improving patients’ knowledge of diabetes, self-care practices, medication adherence and glycemic control.