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Multidisciplinary development and initial validation of a clinical knowledge base on chronic respiratory diseases for mHealth decision support systems

2023-12-13Journal article Open access
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dc.contributor.authorPereira, Ana Margarida
dc.contributor.authorJácome, Cristina
dc.contributor.authorJacinto, Tiago
dc.contributor.authorAmaral, Rita
dc.contributor.authorPereira, Mariana
dc.contributor.authorSá-Sousa, Ana
dc.contributor.authorCouto, Mariana
dc.contributor.authorVieira-Marques, Pedro
dc.contributor.authorMartinho, Diogo
dc.contributor.authorVieira, Ana
dc.contributor.authorAlmeida, Ana
dc.contributor.authorMartins, Constantino
dc.contributor.authorMarreiros, Goreti
dc.contributor.authorFreitas, Alberto
dc.contributor.authorAlmeida, Rute
dc.contributor.authorFonseca, João A.
dc.date.accessioned2024-03-01T15:26:38Z
dc.date.available2024-03-01T15:26:38Z
dc.date.issued2023-12-13
dc.description.abstractMost 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 and 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationPereira, A. M., Jácome, C., Jacinto, T., Amaral, R., Pereira, M., Sá-Sousa, A., Couto, M., Vieira-Marques, P., Martinho, D., Vieira, A., Almeida, A., Martins, C., Marreiros, G., Freitas, A., Almeida, R., & Fonseca, J. A. (2023). Multidisciplinary Development and Initial Validation of a Clinical Knowledge Base on Chronic Respiratory Diseases for mHealth Decision Support Systems. Journal of Medical Internet Research, 25(1), e45364. https://doi.org/10.2196/45364pt_PT
dc.identifier.doi10.2196/45364pt_PT
dc.identifier.issn1438-8871
dc.identifier.urihttp://hdl.handle.net/10400.22/25114
dc.language.isoengpt_PT
dc.publisherJMIR Publicationspt_PT
dc.relation.publisherversionhttps://www.jmir.org/2023/1/e45364pt_PT
dc.subjectknowledge basept_PT
dc.subjectRecommendationspt_PT
dc.subjectPersonalizationpt_PT
dc.subjectClinical decision support systempt_PT
dc.subjectChronic obstructive respiratory diseasespt_PT
dc.subjectMobile phonept_PT
dc.titleMultidisciplinary development and initial validation of a clinical knowledge base on chronic respiratory diseases for mHealth decision support systemspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage23pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleJOURNAL OF MEDICAL INTERNET RESEARCHpt_PT
oaire.citation.volume25 (1)pt_PT
person.familyNameAmaral
person.givenNameRita
person.identifierR-00H-83K
person.identifier.ciencia-idED1E-5481-48E1
person.identifier.ciencia-id1A1E-751F-50F0
person.identifier.orcid0000-0002-7897-1101
person.identifier.orcid0000-0002-0233-830X
person.identifier.ridE-5535-2017
person.identifier.scopus-author-id56067841600
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationd8696cf3-a961-4d88-963a-cefd61572ae3
relation.isAuthorOfPublication790fdd33-acdb-4dfe-88dc-38538486c9b3
relation.isAuthorOfPublication.latestForDiscovery790fdd33-acdb-4dfe-88dc-38538486c9b3

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