A conversational agent for enhanced Self-Management after cardiothoracic surgery

Background: Enhanced self-management is crucial for long-term survival following cardiothoracic surgery. Objectives: This study aimed to develop a conversational agent to enhance patient self-management after cardiothoracic surgery. Methodology: The solution was designed and implemented following the Design Science Research Methodology. A pilot study was conducted at the hospital to assess the feasibility, usability, and perceived effectiveness of the solution. Feedback was gathered to inform further interactions. Additionally, a focus group with clinicians was conducted to evaluate the acceptability of the solution, integrating insights from the pilot study. Results: The conversational agent, implemented using a rule-based model, was successfully tested with patients in the cardiothoracic surgery unit (n = 4). Patients received one month of text messages reinforcing clinical team recommendations on a healthy diet and regular physical activity. The system received a high usability score, and two patients suggested adding a feature to answer user prompts for future improvements. The focus group feedback indicated that while the solution met the initial requirements, further testing with a larger patient cohort is necessary to establish personalized profiles. Moreover, clinicians recommended that future iterations prioritize enhanced personalization and interoperability with other hospital platforms. Additionally, while the use of artificial generative intelligence was seen as relevant for content personalization, clinicians expressed concerns regarding content safety, highlighting the necessity for rigorous testing. Conclusions: This study marks a significant step towards enhancing post-cardiothoracic surgery care through conversational agents. The integration of a diversity of stakeholder knowledge enriches the solution, grants ownership and ensures its sustainability. Future research should focus on automating message generation and delivery based on patient data and environmental factors. While the integration of artificial generative intelligence holds promise for enhancing patient interaction, ensuring the safety of its content is essential.