Information technology and artificial intelligence support in management experiences of the pediatric designated hospital during the COVID-19 epidemic in 2022 in Shanghai

Digital Health Technologies in Pediatric Infectious Disease and the Perspective of Patients and Healthcare Professionals: A Review

BACKGROUND AND AIMS

Pediatric infectious diseases pose a global health challenge, causing 13.7 million deaths in 2019 and three million in children under four. Early recognition and management are vital, and challenges in effectively addressing this persist, particularly in resource-limited areas. Digital health tools, such as telemedicine and mHealth, offer promising solutions. In this review, we aim to evaluate digital health applications in managing pediatric infectious diseases from patient and healthcare perspectives.

METHODS

A literature search was conducted using PubMed, Google Scholar, and Scopus with keywords including "digital health," "telemedicine," and "pediatric infectious disease." Studies published up to January 2024 were included and critically reviewed.

RESULTS

Digital health technologies aid in real-time monitoring and early diagnosis of infectious diseases, improving access to specialized care for pediatric populations. Tools like telemedicine and mHealth enhance communication between patients, caregivers, and physicians, facilitating shared decision-making. Wearable devices and mobile applications enable proactive health management and timely interventions. Despite access challenges in resource-limited settings, caregivers report benefits such as improved healthcare coordination, reduced delays in care, and better health outcomes for children.

CONCLUSION

Digital health shows promise in addressing pediatric infectious disease management, particularly in resource-limited settings, enhancing outcomes through timely interventions and better communication.

Nomogram to predict the incidence of delirium in elderly patients with non-severe SARS-CoV-2 infection

Objective

To construct and validate nomogram models that predict the incidence of delirium in elderly patients with non-severe SARS-CoV-2 infection.

Methods

Elderly patients (≥65y) tested positive for SARS-CoV-2 infection at the hospital were included. We used the 3-min diagnostic Confusion Assessment Method for delirium diagnosis. Least absolute shrinkage and selection operator (LASSO) logistical regression analysis was performed to explore potential independent influencing factors of delirium. A predict model visualized by nomogram was constructed based on the confirmed variables. The predictive accuracy and clinical value of the model were evaluated using receiver operating characteristic (ROC) curves.

Results

The data of 311 elderly patients were analyzed, of whom 73 (23.47%) patients were diagnosed with delirium. Three independent influencing factors of delirium were confirmed: age (OR1.16,1.11-1.22), Glomerular filtration rate (OR 0.98,0.97-0.99), platelet-large cell ratio (1.06,1.02-1.10). These parameters were used to create a nomogram to predict the development of delirium, which showed good predictive accuracy confirmed by the ROC curves (AUC 0.82,0.76-0.88).

Conclusion

We construct a credible nomogram to predict the development of delirium in elderly patients with Non-severe SARS-CoV-2 infection. Our finding may be useful to physicians in early prevention and treatment of delirium.