Development of knowledge-based clinical decision support system for the management of cardiovascular diseases. (3rd October 2022) - L Canoa, J; Pena Gil, C; Monserrat, L
Abstract: Background: Knowledge-based clinical decision support systems (CDSS) are technological tools that analyze patient information and present justified diagnostic and therapeutic suggestions to the user. They improve adherence to evidence-based medicine and reduce medical errors. Material and methods: We have developed an advanced CDSS with the following components: 1. Knowledge Management System, which allows the introduction of interoperable clinical variables organized in taxonomies, and the generation, validation and direct maintenance of decision rules by experts doctors. 2. Smart Assistant for Medical Reports Generation, which facilitates user interaction for the introduction of clinical data (natural language and/or data entry forms) and decission making supported by real-time suggestions that are automatically generated by 3. Inference Engine. Rules and variables derived from the most relevant cardiovascular Clinical Practice Guidelines (CPGs) are introduced by cardiologists into the system. The rules undergo a process of peer review and internal and external validation with anonymized clinical cases. Results: More than 2, 000 clinical variables and more than 5, 000 rules corresponding to more than 80 CPGs and consensuns papers have been created and validated. An internal and external pilot validation of the system has been successfully carried out for the main cardiovascular problems, such as heart failure, atrial fibrillation, valvulopathies, coronary syndromes, cardiomyopathies, hypertension, diabetes and dyslipidaemias, using more than 400 cases. Conclusion: Our CDSS is capable of providing real-time diagnostic and therapeutic recommendations for complex scenarios in cardiovascular disease, combining thousands of variables and decision rules in a single system. Prospective evaluation of the system in real clinical environments, certification and integration in Electronic Medical Records for clinical use is required. Funding Acknowledgement: Type of funding sources: Private company. Main funding source(s): DILEMMA solutions, SL