Development of COVID-19 Health-Risk Assessment and Self-Evaluation (CHaSe): a health screening system for university students and staff during the movement control order (MCO).

COVID-19 has triggered a global health crisis. Death from severe respiratory failure and symptoms, including fever, dry cough, sore throat, anosmia, and gastrointestinal disturbances, has been attributed to the disease. Development of screening and diagnosis methods prove to be challenging due to shared clinical features between COVID-19 and other pathologies, such as Middle Eastern respiratory syndrome, severe acute respiratory syndrome, and common colds. This study aims to develop a comprehensive one-stop online public health screening system based on clinical and epidemiological criteria. The immediate target populations are the university students and staff of University Sultan Zainal Abidin and the civil servants of the Malaysian Ministry of Science, Technology, and Innovation. Forty-nine (49) clinical and epidemiological factors associated with COVID-19 were identified and prioritized based on their prevalence via rigorous review of the literature and vetting sessions. A pilot study of 200 volunteers was conducted to assess the extent of risk mitigation of COVID-19 infection among the university students and civil servants using the prototyped model. Consequently, twelve (12) clinical parameters were identified and validated by the medical experts as essential variables for COVID-19 risk-screening. The updated model was then revalidated via real mass-screening of 5000 resulting in the final adopted CHaSe system. Principal component analysis (PCA) was used to confirm the weightage of risk level toward COVID-19 to procures the optimal accuracy, reliability, and efficiency of this system. Twelve (12) factor loadings accountable for 58.287% of the clinical symptoms and clinical history variables with forty-nine (49) parameters of COVID-19 were identified through PCA. The variables of the clinical and epidemiological aspects identified are the C6 (History of joining high-risk gathering (where confirmed cases had been recorded), CH11 [History of contact with confirmed cases (close contact)], CH13 [Duration of exposure with confirmed cases (minutes)] with substantial positive factors of 0.7053, 0.706 and 0.5086, respectively. The contribution toward high-risk infection of COVID-19 was firmly attributable to the variables CH14 [Last contact with confirmed cases (days)], CH13 [Duration of exposure with confirmed cases (minutes)], and S1 (Age). The revalidated PCA for 5000 respondents also yielded twelve significant PCs with a cumulative variance of 58.288%. Importantly, the medical experts have revalidated the CHaSe system for accuracy of all clinical aspects (clinical symptoms and clinical history) and epidemiological links to COVID-19 infection. After revalidating the model for 5000 respondents, the PC variance for PC1, PC2, PC3, and PC4 was 27.36%, 11.79%, 10.347%, and 8.785%, respectively, with the cumulative explanation of 58.288% in data variability. The level of risks detected using the CHaSe system toward COVID-19 provides optimal accuracy, reliability, and efficiency to conduct mass-screening of students and government servants for COVID-19 infection.

Digital health for quality healthcare: A systematic mapping of review studies.

Objective: To systematically catalogue review studies on digital health to establish extent of evidence on quality healthcare and illuminate gaps for new understanding, perspectives and insights for evidence-informed policies and practices. Methods: We systematically searched PubMed database using sensitive search strings. Two reviewers independently conducted two-phase selection via title and abstract, followed by full-text appraisal. Consensuses were derived for any discrepancies. A standardized data extraction tool was used for reliable data mining. Results: A total of 54 reviews from year 2014 to 2021 were included with notable increase in trend of publications. Systematic reviews constituted the majority (61.1%, (37.0% with meta-analyses)) followed by scoping reviews (38.9%). Domains of quality being reviewed include effectiveness (75.9%), accessibility (33.3%), patient safety (31.5%), efficiency (25.9%), patient-centred care (20.4%) and equity (16.7%). Mobile apps and computer-based were the commonest (79.6%) modalities. Strategies for effective intervention via digital health included engineering improved health behaviour (50.0%), better clinical assessment (35.1%), treatment compliance (33.3%) and enhanced coordination of care (24.1%). Psychiatry was the discipline with the most topics being reviewed for digital health (20.3%). Conclusion: Digital health reviews reported findings that were skewed towards improving the effectiveness of intervention via mHealth applications, and predominantly related to mental health and behavioural therapies. There were considerable gaps on review of evidence on digital health for cost efficiency, equitable healthcare and patient-centred care. Future empirical and review studies may investigate the association between fields of practice and tendency to adopt and research the use of digital health to improve care.

Medical Experts' Agreement on Risk Assessment Based on All Possible Combinations of the COVID-19 Predictors-A Novel Approach for Public Health Screening and Surveillance.

During the initial phase of the coronavirus disease 2019 (COVID-19) pandemic, there was a critical need to create a valid and reliable screening and surveillance for university staff and students. Consequently, 11 medical experts participated in this cross-sectional study to judge three risk categories of either low, medium, or high, for all 1536 possible combinations of 11 key COVID-19 predictors. The independent experts' judgement on each combination was recorded via a novel dashboard-based rating method which presented combinations of these predictors in a dynamic display within Microsoft Excel. The validated instrument also incorporated an innovative algorithm-derived deduction for efficient rating tasks. The results of the study revealed an ordinal-weighted agreement coefficient of 0.81 (0.79 to 0.82, p-value < 0.001) that reached a substantial class of inferential benchmarking. Meanwhile, on average, the novel algorithm eliminated 76.0% of rating tasks by deducing risk categories based on experts' ratings for prior combinations. As a result, this study reported a valid, complete, practical, and efficient method for COVID-19 health screening via a reliable combinatorial-based experts' judgement. The new method to risk assessment may also prove applicable for wider fields of practice whenever a high-stakes decision-making relies on experts' agreement on combinations of important criteria.