Stanford Health Care and Qualtrics expand their partnership to deploy AI agents for automating administrative tasks in healthcare.
The AI system will manage logistical tasks like predicting missed appointments and arranging transport, enhancing patient care.
The initiative aims to preserve the provider-patient relationship by freeing up time for healthcare providers.
Qualtrics and Stanford Health Care are expanding their partnership to deploy AI agents that tackle the tedious administrative tasks that get in the way of patient care, aiming to automate friction points and free up providers.
Beyond the chatbot: The human-supervised system will handle complex logistical work, from predicting missed appointments and arranging transport to detecting prescription delays and flagging conflicting care instructions between departments. The agents can also identify language barriers to connect patients with interpreters or translated materials.
The human-centric goal: The initiative is framed around preserving the provider-patient relationship. “Trust is built when patients feel truly seen, heard, and cared for,” said Stanford Health Care CEO David Entwistle, adding that the AI is designed to “protect the time and attention that positively fuels” that connection.
By embedding these automated interventions directly into workflows, the collaboration aims to create a scalable model where patient experience is a measurable, proactive driver of health outcomes, not just a reactive metric. The AI partnership builds on a deep-rooted relationship, as Stanford already uses Qualtrics for internal faculty and staff performance reviews. This foundational security is part of a much larger IT challenge in healthcare: managing the massive growth of sensitive data while enabling innovation.
The big picture: By shifting the operational burden of managing infrastructure, health systems can free up resources to build the very kinds of advanced applications Stanford is deploying. The challenge becomes even more acute with clinical data. AI-driven diagnostics require infrastructure that can process petabytes of imaging data with millisecond latency. Both examples show that before an organization can deploy AI to improve patient outcomes, it must first build a modern, secure, and scalable data foundation.
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