eHealth in pediatric respiratory allergy

Autores da FMUP

• Cristina Isabel Oliveira Jácome

  Autor

Participantes de fora da FMUP

• Pais-Cunha I.
• Vieira R.
• Pinto B.S.
• Almeida Fonseca J.

Unidades de investigação

• Laboratório Associado RISE- Rede de Investigação em Saúde

  

  Investigador

  Fernando Carlos De Landér Schmitt

• Medicina da Comunidade, Informação e Decisão em Saúde

• RISE-Health

  

  Investigador

  Fernando Carlos De Landér Schmitt

Abstract

Purpose of review This review explores the relevance of eHealth technologies to address unmet needs in pediatric respiratory allergies, particularly allergic rhinitis (AR) and asthma. Given the increasing burden of these conditions, there is a pressing need for effective solutions to enhance disease surveillance, diagnosis, and management.Recent findingsRecent literature highlights the potential of eHealth tools to transform pediatric respiratory allergy care. The use of digital data for infodemiology, application of machine learning models to improve diagnostic sensitivity, smartphone apps with digital patient reported outcome measure (PROMs) and embedded sensors to monitor disease, healthcare professional dashboards with real-time data monitoring and clinical decision support systems (CDSS) are advances emerging to optimize pediatric respiratory allergy care.SummaryIntegrating eHealth technologies into the pediatric respiratory allergy care pathway is a potential solution for current healthcare challenges to better meet the needs of children with AR and asthma. However, while the potential of eHealth is evident, its widespread implementation in real-world practice requires continued research, collaboration, and efforts to overcome existing barriers. Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.

Keywords

• Asthma; Child; Decision Support Systems, Clinical; Humans; Mobile Applications; Respiratory Hypersensitivity; Rhinitis, Allergic; Telemedicine; allergic rhinitis; atopy; childhood disease; clinical decision support system; diagnostic accuracy; early onset asthma; electronic health record; human; infodemiology; lower respiratory tract infection; lung auscultation; lung function; machine learning; patient-reported outcome; personalized medicine; Review; telehealth; telemonitoring; wheezing; allergic rhinitis; asthma; child; diagnosis; immunology; mobile application; respiratory tract allergy; telemedicine; therapy