Open Access Public Health and Health Administration Review

Mapping SDG Synergies: Digitalization, Sustainable Development Goals, and Evolving Patient Expectations in Modern Healthcare

Kesavan Sreekantan Nair — 2026-08-24

The contemporary global healthcare landscape is undergoing an unprecedented paradigm shift driven by rapid technological innovation and shifting population demographics. As health systems worldwide strive to fulfill the United Nations’ Sustainable Development Goals (SDGs)—most notably SDG 3, which aims to ensure healthy lives and promote well-being for all at all ages—the integration of digital tools has transitioned from an optional enhancement to a core structural necessity. Modern healthcare delivery is no longer defined solely by clinical interactions within physical facilities; rather, it operates at a dynamic intersection where artificial intelligence (AI), mobile health applications, electronic health record (EHR) ecosystems, and remote patient monitoring converge. This digital transformation brings profound opportunities alongside critical vulnerabilities. While digital tools improve healthcare accessibility and structural efficiency, they also alter patient behaviors, driving new expectations for convenience, transparency, and personal agency. For health researchers, clinicians, and policymakers, understanding the interplay among the SDGs, the digitalization of health systems, and shifting patient expectations is vital for designing equitable, sustainable, and high-quality care frameworks. By focusing scholarship on these intersections, the scientific community can ensure that digital transformations strengthen equitable, sustainable, and patient-centered health systems worldwide.

An Empirical Study on the Prevalence and Multidrug Resistance Profiles of Salmonella and Campylobacter Species: A One Health Context

Muhammad Talha Aziz — 2026-08-26

Salmonella and Campylobacter are foodborne pathogens that pose public health risks, particularly in commercial poultry production, where antimicrobial resistance may compromise the treatment of infected birds and undermine food safety. The present cross-sectional study aimed to determine the prevalence, antimicrobial resistance profile, and multidrug resistance (MDR) pattern of Salmonella and Campylobacter in commercial broiler farms of Punjab, Pakistan. Samples (ecal contents, cloacal swabs, bootsocks, and fecal grabs) were taken from 70 farms, with a total of 350 samples collected and processed by microbiological and molecular techniques. The overall Salmonella prevalence was 22.0% (77 samples were positive), and the overall Campylobacter prevalence was 34.0% (119 samples were positive). Campylobacter prevalence was also found to be highest in bootsocks (48.6%), whereas the prevalence of Salmonella was highest in bootsocks (31.4%). The highest prevalence of both pathogens was found on small farms. The highest resistance level was seen for Salmonella isolates against tetracycline (80.5%) and nalidixic acid (71.4%), and 61.0% were considered MDR. The antimicrobial resistance rate for Campylobacter was high for tetracycline (89.1%), ciprofloxacin (81.5%), and nalidixic acid (76.5%), and the MDR rate was 93.3%. Pathogen prevalence was significantly associated with farm size, antimicrobial use, and biosecurity. These findings indicate a threat from One Health and underscore the importance of better biosecurity, responsible use of antimicrobials, surveillance, and targeted interventions for commercial broiler production.

Investigating the Confounding Role of Artificial Intelligence on the Relationship between Patient Safety and Infection Control Practices in Enhancing Patient Satisfaction in Healthcare

Ghulam Muhammad Kundi — 2026-08-30

This study aimed to investigate how AI mediates the relationship between infection control, patient safety, and patient satisfaction. The study used an exploratory sequential research design with two phases. In the first phase, 25 qualitative studies were analyzed using PRISMA (2009–2024), which were supplemented by quantitative analysis of 384 samples. A cross-sectional survey was conducted among physicians, nurses, and paramedics. The scales were found to be reliable and valid in PLS-SEM measurement models. The structural model was run to investigate the direct and indirect effects. The result suggests the significant direct effect of AI and infection control on satisfaction. However, patient safety and satisfaction were found to be insignificant. This implies full mediation by AI between Patient safety and satisfaction. AI's mediating role was confirmed between safety, infection control, and satisfaction. The study concludes that themes emerged from the review of the studies, and the findings of the quantitative data were consistent. Hence, this study validates the results of previous studies in the context of the Middle East. Through triangulation, the diverse sources used in this study have reduced bias, reinforced internal validity, and provided a deep insight into the mediating effect of AI in enhancing patient safety, infection control, and patient satisfaction. The significant role of experience and staff categories emphasizes the need for targeted interventions and training to foster a culture of safety and technological adoption across healthcare roles. Healthcare policymakers should consider these factors when implementing new technologies and designing training programs to ensure the benefits of AI, infection control, and patient safety.