Unlocking potential: Critical success factors for AI integration in remote patient monitoring systems for post-cardiac surgery care

Abstract

Rising healthcare costs have led to a shift towards cost-effective medical technologies that enhance clinical outcomes. Remote Patient Monitoring (RPM) systems enable real-time data collection, reducing hospital stays, reducing healthcare providers’ workload, and improving patient satisfaction. Artificial Intelligence (AI) holds promise in transforming healthcare tasks by uncovering hidden patterns and trends. Despite its potential, AI adoption faces several multifaceted challenges such as data quality, privacy and security, interpretability of AI algorithms, and ethical and regulatory considerations. To successfully implement digital advancements these challenges must be addressed and critical success factors must be defined and prioritized. This study adopts a narrow perspective; it focuses on a context-specific domain and explores critical success factors (CSFs) for AI implementation in RPM systems for post-cardiac surgery patient care. A qualitative approach was used to collect data. Two sets of structured interviews were employed to elicit deep insights and experiences with end users and medical workers. Findings emphasize the importance of stakeholder input, revealing valuable insights into CSFs relevant to post-cardiac patient care. We found that technical aspects such as system reliability, ease of use, and data quality are particularly important and warrant attention. This research is crucial for the scientific and academic community as it addresses the multifaceted barriers to AI integration in healthcare. Illuminating and addressing these issues is critical for enhancing AI applications in healthcare.

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