AdventHealth advances whole-person care with OpenAI

AdventHealth advances whole-person care with OpenAI | OpenAI

May 21, 2026

AdventHealth advances whole-person care with OpenAI

By treating AI adoption as the outcome, AdventHealth eases clinician workload, improves workflows, and unlocks more time for patient care.

Company size: Enterprise

Region: Global, North America

Industry: Healthcare

Products: ChatGPT

Results

80%

Reduction in time spent on administrative tasks

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AdventHealth is deploying ChatGPT for Healthcare to reduce administrative burden and streamline clinical workflows across its system. By automating time-intensive documentation and support tasks, care teams are reclaiming hours each week and allowing clinicians to focus more directly on patients. The result is not just operational efficiency, but expanded clinical capacity, faster access to care, and a measurable improvement in the patient experience.

Under pressure to do more with less

AdventHealth is a hospital system operating across nine states, serving millions of patients each year. Like many large health systems, it faces tight margins, growing demand and increasing administrative complexity.

Much of that pressure shows up in day-to-day workflows. Physician advisors reviewing cases for utilization management often spend about 10 minutes per case, not on a single task but on a sequence of steps: reading charts, identifying relevant details, checking criteria and drafting structured rationales. Across hundreds or thousands of cases, that time adds up quickly.

The burden extends beyond clinical roles. Teams in finance, HR, IT and other functions spend significant time drafting documents, summarizing information and preparing materials that are necessary but time-consuming. As a result, many operate in what leaders describe as “constant operations mode,” with limited capacity for higher-value work.

At the same time, interest in AI was already emerging inside the organization. Employees were experimenting with chatbots, even as formal policies restricted their use.

“We had folks who were eager to start, but there were a very large number of people who were on the sidelines,” says Rob Purinton, Chief AI Officer at AdventHealth. “They weren’t sure how to use AI effectively in their daily jobs.”

Adoption is the outcome

AdventHealth’s leadership concluded early that running isolated pilots would not lead to meaningful change. The central challenge was driving consistent, safe use across a large workforce.

“The hardest part of AI in healthcare is getting humans to use it safely, consistently, and at scale,” Purinton says. “We made a decision early on to treat adoption as the product.”

That decision shaped the rollout. Instead of positioning AI as automation, leaders framed it as a way to reduce administrative burden and return time to clinicians and staff.

“We don’t talk about AI as automation. We talk about time back,” Purinton says. “If we can take a 10-minute review and compress it meaningfully—while maintaining quality—that’s capacity we can give back to our clinicians.”

AdventHealth also treated adoption as a measurable operational metric. The organization tracks messages per user per business day, excluding weekends and holidays to create a consistent baseline. That metric is monitored and managed like any other KPI, with targets and trends reviewed regularly.

To scale usage, the system relied on domain-based peer groups rather than large, centralized training programs. Finance teams worked with finance teams and HR with HR, for example—sharing prompts, workflows and best practices relevant to their specific functions.

Enterprise-scale deployment with OpenAI

As the organization moved from experimentation to enterprise deployment, leadership prioritized tools that could meet healthcare requirements around privacy, governance and reliability.

“We chose OpenAI because we weren’t looking for a demo. We were looking for enterprise infrastructure,” Purinton says. “The reasoning capability, the structured outputs, and the governance controls gave us confidence that this wasn’t just productivity software. It was something we could responsibly scale across a health system.”

AdventHealth adopted ChatGPT Enterprise and later ChatGPT for Healthcare, which provided additional safeguards for regulated environments, including data protections and compliance support.

Speed of innovation and collaboration also influenced the decision.

“We really appreciate being closer to the edge of what’s possible,” Purinton says. “And we’ve found OpenAI to be highly collaborative as we think through pilots, deployments and what comes next.”

Workflow redesign for clinical and operational teams

One of the earliest and most measurable use cases was utilization management.

Using ChatGPT for Healthcare, physician advisors can generate structured summaries of patient charts, surface relevant clinical details and draft initial rationales. The clinician remains responsible for final judgment, but the time spent assembling information is reduced.

The organization measures impact using system-level data, including timestamps in electronic health records, rather than self-reported estimates.

“We prefer measures that are baked right into the process,” Purinton says. “We can see exactly how many minutes have improved and whether that change is statistically significant.”

Beyond clinical workflows, similar patterns have emerged across departments:

• Drafting documents and plans starts with a first-pass output rather than a blank page
• Policies and communications are converted into structured, usable formats
• Notes and unstructured information are quickly summarized into action steps

These changes reduce cycle times, limit back-and-forth revisions and improve consistency in outputs.

Measuring results through time and throughput

AdventHealth evaluates AI impact across two primary dimensions: adoption and workflow performance.

On the adoption side, tracking daily usage has created accountability and visibility into how quickly AI is becoming part of routine work.

On the workflow side, pilots are evaluated using throughput metrics such as time per task, turnaround time and volume handled. In utilization management, the goal is to reduce review time while maintaining quality and consistency.

Across departments, teams report:

• Reduced time spent on repetitive documentation and review tasks
• Faster turnaround on internal workflows

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