JMIR Publications

ABSTRACT

Most mHealth decision support systems currently available for chronic obstructive respiratory diseases (CORD) are not supported by clinical evidence and/or lack clinical validation. The development of the knowledge base that will feed the clinical decision support system (CDSS) 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 CORD. A multidisciplinary team of healthcare 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 (e.g., 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 CORD or to prevent CORD; 2) directed to 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) operability of the recommendation; and 5) healthcare 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 with Boolean logical operators (n=405). Finally, the knowledge base was implemented in an intelligent individualized coaching component and pre-tested with an asthma use case. Initial validation of the knowledge base was performed internally using data from a population-based observational study of individuals with or without asthma and/or rhinitis. External validation of the appropriateness of the recommendations with the highest priority level was conducted independently by four physicians. Additionally, 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 performed preliminary validation of a clinical knowledge base for CORD 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.