How Stanford Healthcare prescribes AI to streamline the clinician and patient experience

Before an organization launches any AI initiative, considering how disruptive it can be, it’s essential to prioritize governance, as well as make sure sufficient training and change management strategies are implemented. “I think it’s a combination of having all these things in place, otherwise it creates fear and anxiety,” says Aditya Bhasin, Stanford Healthcare’s VP of software design and development. “Otherwise, how do you overcome these things and get the organization to the place it needs to go?”

So with this structure firmly in place, the Bay Area’s Stanford Healthcare, which runs about 300 facilities including two full service hospitals, has rolled out an AI solution that was initially conceived to mitigate overwhelmed clinicians by, for instance, automating draft responses for patients inquiring about billing.

The project started with 10 billing reps and saved 17 hours by handling 1,000 messages using 25 smart templates. Now enterprise-wide with 60% utilization, the AI also drafts test results and accelerates software development.

“We started this as a pilot for our billing reps, so when patients asked questions about where they were in their payment plans, this targeted that particular use case, and we saw substantial success,” he says. “Then we followed through on our patient advisory forum to see the type of responses it got and the timeliness of those responses. Patients were excited they got faster responses, and billing reps were excited, too, about getting technology to help them curate responses and answer them correctly. Then we rolled it out across the organization so now, all our billing reps have access to this technology.”

Not only is Bhasin and his team transforming business from operational, educational, and researching aspects, they’re also transforming how they approach software development.

“When we roll out these tools, we want physicians to spend more time with patients rather than documenting stuff, so now they just talk to patients and the documentation happens automatically for them. Just writing a routine and having it automatically create a lot of test cases around it is a huge impact.”

What it comes down to, says Bhasin, is it brings a bit of joy back into the work by helping to get things done faster while expanding the art of the possible.

Bhasin also details the strong security, trust, and next gen perspective needed for any AI implementation. Watch the full video below for more insights, and be sure to subscribe to the monthly Center Stage newsletter by clicking here.

On an impetus for AI: Stanford Medicine is a unique place. Our three missions are education, research, and clinical care, and AI is transforming every aspect of those missions. Our journey with AI started a couple of quarters after ChatGPT was announced, and the organization decided to double down across the three missions, using that technology to transform the core business. One of the first initiatives we undertook was to help our clinicians with burnout. Post Covid, our portal engagements and patients using the digital channel increased exponentially, so one of the first things that happened was we had physicians getting overwhelmed with messages coming from patients.

When this technology came about to use large language models, one of the first things we rolled out was to help physicians. We started creating draft responses, and in a regulated industry, that required a lot of soul searching and thought process. But whenever a patient messages our clinicians, we create a draft response, which helped reduce a physician’s cognitive load. That was very successful. Then we noticed other opportunities, like once people got their care, another friction point when you talk about patient experience is billing.

We’ve all experienced the complexity of that and it’s uniquely challenging for patients, especially when you have proxies. Billing reps require a lot of understanding of your unique insurance, and all that is a lot of work. So we looked at how to apply this new capability to that niche area, and started with Gemba rounding. One thing we observed was, given all the nuances related to billing, we created 25 different templates for the reps. That adds a lot of load to the work involved so we automated that entire process.

On better access to information: At an organization like Stanford healthcare, there are millions of test results per year. When you have a large patient population and thousands of physicians ordering images as a standard part of the workflow, patients immediately get the lab results in their portal now as part of our legal requirements. But if you ever try to read a radiology result or a blood panel, they come with a level of complexity, so you’re waiting for your physician to really decipher that and give you insight. When you have millions of these coming in, and as these labs get more complex, it adds a lot of workload on your physician. So if you’ve got a family practice or a specialist, and you order a bunch of these labs to decide on next steps, you have to read all of them and respond to patients.

We use the technology to create draft results, and understand how we can help create responses to help patients. At this point, the moment a lab result comes in, we also create a draft result for physicians. A lot of these complex systems pump information into our EHR, and then we use this homegrown framework to create responses for physicians. That’s been successful in terms of physician adoption. There’s always a human in the loop, and we never force our physicians to use this. So it’s the comfort level, complexity, and trust we have to build. The relationship between the physician and the patient is sacrosanct. We’re always trying to help them help their patients.

On strengthening skills: We’ve been very forward looking to embrace AI, and one thing we did a couple of years ago was make a secure version of AI called SecureGPT, which was available to virtually everyone in the organization. It’s been hugely successful and people use it for everything from administrative work to clinical projects. It provided valuable learning, and the organization feels comfortable and supported in having this tool universally available. Out of it, we also worked on training physician scientists. A huge part of AI is going into the next generation of curriculum and how to educate and create interactive sessions for our medical students. So we took aspects and learning to create mandatory training for everybody in the technology group, which is close to 1,000 people. Then we rolled it out to everybody in the organization. Now you’re talking tens of thousands of people from facilities and operational staff, to nurses and physicians.

On keeping it all in check: For most technologists, tech is the exciting part. But trust is paramount in a highly regulated environment like healthcare, as well as how we ensure we don’t expose our organization to risks while maintaining the right level of efficacy between physicians and patients. So with the advent of AI, we’ve got governance bodies at the C level, so everything clinical goes through that. We also have a process by which we validate every solution we come up with. We call it the FURM process, which is fair, useful, reliable, AI models, because with AI, we look to see our interventions are fair, and you want to make sure solutions are equitable across all aspects. But because the rate of change of technology is so fast, the biggest challenge for most people now is how do you bring your organizations along.

How do you roll out transformation in standard workflows, and how do you get business leaders to simultaneously reimagine how work can be done when you have powerful tools. It can really disrupt workflows. So as much as we’re trying to keep this interwoven into existing workflows, we find out how to tell people these capabilities are available. And these are early rollouts, which are enterprise wide. But results have been positive, so it’s creating a feedback effect where we’re taking on more projects to transform.