Introduction to AI in Telehealth
Dr. Kedar Mate is chief medical officer and cofounder of Qualified Health, a vendor of generative artificial intelligence infrastructure. He’s also former president and CEO of the Institute for Healthcare Improvement, which aims to advance equitable health outcomes worldwide through improvement science. Mate believes that in telehealth and remote patient monitoring today, much attention is being given to technologies like artificial intelligence – and not enough to the robust infrastructure needed to support important AI technology.
The Need for Foundational AI Stack
Mate says his company aims to help hospitals and health systems move beyond point systems toward platforms that embed safety, equity and real-world impact into virtual care delivery. He discussed why RPM and telehealth tools need a foundational AI stack to be safe, scalable and effective. According to Mate, AI tools can allow clinicians to set threshold parameters for remote monitoring and provide critical alerts similar to those for critical lab values, integrating seamlessly into existing clinical workflows rather than creating additional burden.
Moving from Fragmented Experiments to Operational AI
Mate also discussed what it takes to move from fragmented experiments to operational AI that supports real-time clinical decision making in virtual care. He emphasized the need to embed improvement science principles from Day One, including rapid cycle testing, measurement for learning, and systematic spread strategies that account for local variation in how care teams actually work. AI must integrate multimodal data from EHRs, wearables, medical imaging, genetics and social determinants of health to create holistic patient profiles.
Governance, Monitoring, and Evaluation
Governance, monitoring, and evaluation are crucial to support sustainable virtual care models. Continuous monitoring requires both clinical outcome measures and process measures that track how AI actually is being used by care teams and received by patients in their daily workflows. Governance structures must center equity and patient outcomes, not just efficiency metrics. Evaluation frameworks need to capture unintended consequences and system effects, such as how AI-enabled virtual care changes the nature of therapeutic relationships and care continuity.
Designing AI Systems for Equity
To design an AI system for telehealth that totally supports equity, one must start with the populations most marginalized by current healthcare systems. This means designing for those with limited digital literacy, unreliable internet or complex social needs, and building more robust systems for everyone. AI promotes healthcare equity by expanding access to quality care, enabling simultaneous translation into hundreds of languages, and improving both the care experience and clinical relationship.
Conclusion
In conclusion, AI has the potential to revolutionize telehealth and remote patient monitoring, but it requires a foundational AI stack to be safe, scalable and effective. To achieve this, healthcare organizations must embed improvement science principles, integrate multimodal data, and prioritize governance, monitoring, and evaluation. By designing AI systems with equity in mind, we can expand access to quality care and improve health outcomes for all.
FAQs
- Q: What is the main challenge in implementing AI in telehealth?
A: The main challenge is building a robust infrastructure that can support AI technology and ensure safe, scalable, and effective virtual care delivery. - Q: How can AI promote healthcare equity?
A: AI can promote healthcare equity by expanding access to quality care, enabling simultaneous translation into hundreds of languages, and improving both the care experience and clinical relationship. - Q: What is the importance of governance, monitoring, and evaluation in virtual care models?
A: Governance, monitoring, and evaluation are crucial to support sustainable virtual care models, ensure equity and patient outcomes, and capture unintended consequences and system effects.
