Demand analysis of transitional care for patients undergoing minimally invasive cardiac interventions with AI-driven solutions: a mixed-methods approach.

aimsMinimally invasive cardiac intervention (MICI) patients remain at high risk of readmission and mortality during their post-discharge phase, with 30-day readmission rates of up to 10%. Although technological innovations, especially AI-driven solutions, hold promise for improving outcomes, there is a pressing need to clarify the full spectrum of patient demands during the transition from hospital to home. This study aimed to systematically identify these demands to guide the development of AI-driven solutions that reduce readmission rates and improve clinical outcomes. METHODS AND

resultsA convergent parallel mixed-methods design was employed to systematically identify patient demands and inform the development of AI-driven interventions in transitional care. Quantitative and qualitative data were collected from 137 MICI patients recruited from four hospitals (June-August 2024). Quantitatively, a 23-item survey was analyzed using the Kano model, revealing no "must-be" demands-indicating that patients were accustomed to a lack of guidance post-discharge. However, health monitoring, medication guidance, symptom management, and personalized exercise plans were identified as "one-dimensional" demands that significantly impact patient satisfaction. Additionally, continuous exercise monitoring and dietary planning emerged as "attractive" features that could enhance care quality without negatively affecting satisfaction if absent. Qualitative interviews uncovered the importance of comorbidity management, psychological support and financial transparency, which were not fully captured in the survey data. The integration of these findings underscores the need for AI-driven personalized health monitoring systems and knowledge-based AI tools to revolutionize the transitional care process for MICI patients.

conclusionThis integrated analysis highlights the significant care demands of MICI patients during the transition from hospital to home. Key recommendations include: (1) deploying AI-driven health monitoring, medication guidance, and symptom management systems, (2) designing personalized exercise and dietary tools, and (3) creating accessible, knowledge-based platforms for reliable medical information. In addition, comorbidity management, psychological support and financial transparency are areas that call for our attention. By aligning with these patient-centered demands and leveraging AI's capabilities, future transitional care interventions-particularly in China have the potential to address healthcare staffing constraints and improve patient outcomes. However, due to the limitations of our study, these insights require further validation and exploration.