Multidisciplinary Development and Initial Validation of a Clinical Knowledge Base on Chronic Respiratory Diseases for mHealth Decision Support Systems

Data de publicação: 13/12/2023 Data Ahead of Print: 13/12/2023

Autores da FMUP

• Ana Margarida Barbosa Ribeiro Pereira

  Autor

• Cristina Isabel Oliveira Jácome

  Autor

• Tiago António Queiros Jacinto

  Autor

• Rita Da Silva Amaral

  Autor

• Ana Isabel Alves De Sá E Sousa

  Autor

• Pedro Manuel Vieira Marques

  Autor

• Alexandre Rui Rodrigues Almeida

  Autor

• José Alberto Da Silva Freitas

  Autor

• Rute Alexandra Borges De Almeida

  Autor

• João De Almeida Lopes Da Fonseca

  Autor

Participantes de fora da FMUP

• Pereira, M
• Couto, M
• Martinho, D
• Vieira, A
• Martins, C
• Marreiros, G

Unidades de investigação

• Cirurgia e Fisiologia

• Laboratório Associado RISE- Rede de Investigação em Saúde

  

  Investigador

  Fernando Carlos De Landér Schmitt

• Medicina da Comunidade, Informação e Decisão em Saúde

• Recursos Comuns

  

  Investigador

  Guilhermina Maria Da Silva Rêgo

• RISE-Health

  

  Investigador

  Fernando Carlos De Landér Schmitt

Abstract

Most mobile health (mHealth) decision support systems currently available for chronic obstructive respiratory diseases (CORDs) are not supported by clinical evidence or lack clinical validation. The development of the knowledge base that will feed the clinical decision support system is a crucial step that involves the collection and systematization of clinical knowledge from relevant scientific sources and its representation in a human-understandable and computer-interpretable way. This work describes the development and initial validation of a clinical knowledge base that can be integrated into mHealth decision support systems developed for patients with CORDs. A multidisciplinary team of health care professionals with clinical experience in respiratory diseases, together with data science and IT professionals, defined a new framework that can be used in other evidence-based systems. The knowledge base development began with a thorough review of the relevant scientific sources (eg, disease guidelines) to identify the recommendations to be implemented in the decision support system based on a consensus process. Recommendations were selected according to predefined inclusion criteria: (1) applicable to individuals with CORDs or to prevent CORDs, (2) directed toward patient self-management, (3) targeting adults, and (4) within the scope of the knowledge domains and subdomains defined. Then, the selected recommendations were prioritized according to (1) a harmonized level of evidence (reconciled from different sources); (2) the scope of the source document (international was preferred); (3) the entity that issued the source document; (4) the operability of the recommendation; and (5) health care professionals' perceptions of the relevance, potential impact, and reach of the recommendation. A total of 358 recommendations were selected. Next, the variables required to trigger those recommendations were defined (n=116) and operationalized into logical rules using Boolean logical operators (n=405). Finally, the knowledge base was implemented in an intelligent individualized coaching component , pretested with an asthma use case. Initial validation of the knowledge base was conducted internally using data from a population-based observational study of individuals with or without asthma or rhinitis. External validation of the appropriateness of the recommendations with the highest priority level was conducted independently by 4 physicians. In addition, a strategy for knowledge base updates, including an easy-to-use rules editor, was defined. Using this process, based on consensus and iterative improvement, we developed and conducted preliminary validation of a clinical knowledge base for CORDs that translates disease guidelines into personalized patient recommendations. The knowledge base can be used as part of mHealth decision support systems. This process could be replicated in other clinical areas.

©Ana Margarida Pereira, Cristina Jácome, Tiago Jacinto, Rita Amaral, Mariana Pereira, Ana Sá-Sousa, Mariana Couto, Pedro Vieira-Marques, Diogo Martinho, Ana Vieira, Ana Almeida, Constantino Martins, Goreti Marreiros, Alberto Freitas, Rute Almeida, João A Fonseca. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 13.12.2023.

Keywords

• knowledge base; recommendations; personalization; clinical decision support system; chronic obstructive respiratory diseases; mobile phone