Background: Global pandemics have occurred with increasing frequency over the past decade reflecting the sub-optimum operationalization of surveillance systems handling human health data. Despite the wide array of current surveillance methods, their effectiveness varies with multiple factors. Here, we perform a systematic review of the effectiveness of alternative infectious diseases Early Warning Systems (EWSs) with a focus on the surveillance data collection methods, and taking into consideration feasibility in different settings. Methods: We searched PubMed and Scopus databases on 21 October 2022. Articles were included if they covered the implementation of an early warning system and evaluated infectious diseases outbreaks that had potential to become pandemics. Of 1669 studies screened, 68 were included in the final sample. We performed quality assessment using an adapted CASP Checklist. Results: Of the 68 articles included, 42 articles found EWSs successfully functioned independently as surveillance systems for pandemic-wide infectious diseases outbreaks, and 16 studies reported EWSs to have contributing surveillance features through complementary roles. Chief complaints from emergency departments’ data is an effective EWS but it requires standardized formats across hospitals. Centralized Public Health records-based EWSs facilitate information sharing; however, they rely on clinicians’ reporting of cases. Facilitated reporting by remote health settings and rapid alarm transmission are key advantages of Web-based EWSs. Pharmaceutical sales and laboratory results did not prove solo effectiveness. The EWS design combining surveillance data from both health records and staff was very successful. Also, daily surveillance data notification was the most successful and accepted enhancement strategy especially during mass gathering events. Eventually, in Low Middle Income Countries, working to improve and enhance existing systems was more critical than implementing new Syndromic Surveillance approaches. Conclusions: Our study was able to evaluate the effectiveness of Early Warning Systems in different contexts and resource settings based on the EWSs’ method of data collection. There is consistent evidence that EWSs compiling pre-diagnosis data are more proactive to detect outbreaks. However, the fact that Syndromic Surveillance Systems (SSS) are more proactive than diagnostic disease surveillance should not be taken as an effective clue for outbreaks detection.

The landscape of digital health technologies is evolving rapidly, with clinical artificial intelligence increasingly integrated into primary care. Successfully adopting these technologies depends on the users' knowledge, attitude, and practice. This systematic review and meta-analysis aims to assess primary care physicians' knowledge, attitude, and practice toward clinical artificial intelligence and to uncover the key determinants influencing its implementation in primary care. PubMed, Web of Science, Scopus, and Institute of Electrical and Electronics Engineers (IEEE) were searched on 18.10.2023 and 03.05.2024 to systematically review quantitative and qualitative relevant primary studies. Three authors independently reviewed and appraised the studies using the Mixed Methods Appraisal Tool. Thematic analysis and proportion meta-analysis of the addressed domains were performed, with results aligned with a recent integration framework. 24 publications, including 4074 primary care physicians, suggested that knowledge levels were generally low, with passive opportunistic learning (pooled proportion 0·33, 95 % Confidence Interval (CI) 0·16-0·50, n = 6 studies, 2358 physicians). Attitudes varied, with concerns about losing jobs and rejecting new technologies (0·53, 95 %CI 0·42-0·64, n = 11, 2988). Practice experience was positive with AI simulation/prior training or negative with infrastructure and electronic medical records limitations (0·52, 95 %CI 0·36-0·68, n = 12, 3459). The risk of bias was low in 14 studies and moderate-high in ten, with significant heterogeneity between studies. This review underscores the importance of effectively integrating clinical artificial intelligence-assisted digital health technologies within primary care. Acknowledging the current knowledge, attitude, and practice state and identifying gaps and opportunities, a physician-driven artificial intelligence implementation process with sustainable adoption might be possible. More attention is needed to counterbalance the concerns hindering the effectiveness of advanced tools in primary care practice.

Vaccinations of healthcare workers (HCWs) aim to directly protect them from occupational diseases, and indirectly protect their patients and communities. However, studies increasingly highlight that HCWs can be vaccine hesitant. This review aims to analyze HCWs’ and public health professionals’ sentiments toward COVID-19 (Coronavirus Disease 2019) vaccination and determinants across different countries. A search strategy was conducted in PubMed using keywords such as “COVID-19”, “sentiment/acceptance”, “healthcare workers”, “vaccine hesitancy”, and “influenza”. A total of 56 articles were selected for in-depth analyses. The highest COVID-19 vaccination uptake was found in an Italian study (98.9%), and the lowest in Cyprus (30%). Older age, male gender, the medical profession, higher education level, presence of comorbidities, and previous influenza vaccination were associated with vaccine acceptance. Factors for low acceptance were perceived side effects of the vaccine, perceived lack of effectiveness and efficacy, and lack of information and knowledge. Factors for acceptance were knowledge, confidence in the vaccine, government, and health authorities, and increased perception of fear and susceptibility. All studies focused on healthcare providers; no studies focusing on public health professionals’ sentiments could be found, indicating a gap in research that needs to be addressed. Interventions must be implemented with vaccination campaigns to improve COVID-19 vaccine acceptance.

Introduction: The COVID-19 era highlighted vaccine hesitancy (VH) among health workers (HWs) as a major hurdle to optimum immunization practices. Through the identification of relevant determinants, barriers, and interventions to counteract VH, this literature review examines the impact of the...

To map and assess the quality of guidelines developed by or in partnership with the WHO on infectious diseases with a high risk of causing public health emergencies. Cross-sectional audit study. A search of the WHO website and MAGICapp was conducted to identify guidelines on treatment, management, diagnosis, prevention, and surveillance of the WHO's priority list of diseases. When these sources yielded no results, the AI search engine Perplexity was used to expand the search for locally developed WHO partner guidelines. Eligible guidelines were evaluated by three to four appraisers using the Appraisal of Guidelines for Research and Evaluation (AGREE II). Of the thirty-three guidelines assessed, 73 % were developed by the WHO and 27 % were in partnership with external organisations. Guidelines scored highest in clarity of presentation (73.71 %) but were weakest in editorial independence (26.63 %) and rigour of development (30.05 %). Thirteen guidelines (40 %) were suitable for practical use, with high scores in scope, rigour, and clarity. Fourteen (42 %) were recommended for clinical use with modifications due to insufficient rigour, stakeholder involvement, or editorial independence, whilst six (18 %) were not recommended because of significant methodological flaws. Furthermore, WHO's sole-produced guidelines had higher quality than those developed with external collaborators, except for one partnership. Moreover, no WHO guidelines were found for Crimean-Congo haemorrhagic fever, Lassa fever, Rift Valley fever, Nipah and henipaviral diseases, and SARS. The rigour of development and editorial independence of WHO-supported guidelines require improvement, particularly for diseases where comprehensive guidelines are lacking.