In the heart of Silicon Valley, Stanford clinicians and researchers are exploring whether artificial intelligence could help manage a potential surge of Covid-19 patients — and identify patients who will need intensive care before their condition rapidly deteriorates.
The challenge is not to build the algorithm — the Stanford team simply picked an off-the-shelf tool already on the market — but rather to determine how to carefully integrate it into already-frenzied clinical operations.
“The hardest part, the most important part of this work is not the model development. But it’s the workflow design, the change management, figuring out how do you develop that system the model enables,” said Ron Li, a Stanford physician and clinical informaticist leading the effort. Li will present the work on Wednesday at a virtual conference hosted by Stanford’s Institute for Human-Centered Artificial Intelligence.
The effort is primed to be an accelerated test of whether hospitals can smoothly incorporate AI tools into their workflows. That process, typically slow and halting, is being sped up at hospitals all over the world in the face of the coronavirus pandemic.
The machine learning model Li’s team is working with analyzes patients’ data and assigns them a score based on how sick they are and how likely they are to need escalated care. If the algorithm can be validated, Stanford plans to start using it to trigger clinical steps — such as prompting a nurse to check in more frequently or order tests — that would ultimately help physicians make decisions about a Covid-19 patient’s care.
The model — known as the Deterioration Index — was built and is marketed by Epic, the big electronic health records vendor. Li and his team picked that particular algorithm out of convenience, because it’s already integrated into their EHR, Li said. Epic trained the model on data from hospitalized patients who did not have Covid-19 — a limitation that raises questions about whether it will be generalizable for patients with a novel disease whose data it was never intended to analyze.
Nearly 50 health systems, covering hundreds of hospitals, have been using the model to identify hospitalized patients with all sorts of medical conditions at the highest risk of deterioration, according to a spokesperson for Epic. Recently, Epic built an update to help hospitals measure the model’s performance specifically for Covid-19 patients; that work showed that the model performed well and didn’t need to be altered, the spokesperson said. Now, some hospitals, like Stanford, are evaluating the model in Covid-19 patients, while others are using it with confidence, according to the spokesperson.
In the months before the coronavirus pandemic, Li and his team had been working to validate the model on data from Stanford’s general population of hospitalized patients. Now, they’ve switched their focus to evaluate it on data from dozens of Covid-19 patients that have been hospitalized at Stanford — a cohort that, at least for now, may be too small to fully validate the model.
“We’re essentially waiting as we get more and more Covid patients to see how well this works,” Li said. He added that the model does not have to be completely accurate in order to prove useful in the way it’s being deployed: to help inform high-stakes care decisions, not to automatically trigger them.
As of Tuesday afternoon, Stanford’s main hospital was treating 19 confirmed Covid-19 patients, nine of whom were in the intensive care unit; another 22 people were under investigation for possible Covid-19, according to Stanford spokesperson Julie Greicius. The branch of Stanford’s health system serving communities east of the San Francisco Bay had five confirmed Covid-19 patients, plus one person under investigation. And Stanford’s hospital for children had one confirmed Covid-19 patient, plus seven people under investigation, Greicius said.
Stanford’s hospitalization numbers are very fluid; many people under investigation may turn out to not be infected, and many confirmed Covid-19 patients who have relatively mild symptoms may be quickly cleared for discharge to go home.
Santa Clara County, where Stanford is located, had confirmed 890 cases of Covid-19 as of Monday afternoon. It’s not clear how many of them have needed hospitalization, though San Francisco Bay Area hospitals have not so far faced the crush of Covid-19 patients that New York City hospitals are experiencing.
The model is meant to be used in patients who are hospitalized, but not yet in the ICU. It analyzes patients’ data — including their vital signs, lab test results, medications, and medical history — and spits out a score on a scale from 0 to 100, with a higher number signaling elevated concern that the patient’s condition is deteriorating.
Already, Li and his team have started to realize that a patient’s score may be less important than how quickly and dramatically that score changes, he said.
“If a patient’s score is 70, which is pretty high, but it’s been 70 for the last 24 hours — that’s actually a less concerning situation than if a patient scores 20 and then jumps up to 80 within 10 hours,” he said.
Li and his colleagues are adamant that they will not set a specific score threshold that would automatically trigger a transfer to the ICU or prompt a patient to be intubated. Rather, they’re trying to decide which scores or changes in scores should set off alarm bells that a clinician might need to gather more data or take a closer look at how a patient is doing.
“At the end of the day, it will still be the human experts who will make the call regarding whether or not the patient needs to go to the ICU or get intubated — except that this will now be augmented by a system that is smarter, more automated, more efficient,” Li said.
Using an algorithm in this way has potential to minimize the time that clinicians spend manually reviewing charts, so they can focus on the work that most urgently demands their direct expertise, Li said. That could be especially important if Stanford’s hospital sees a flood of Covid-19 patients in the coming weeks.
“It will have to be something that would work if we have 100 Covid patients in the hospital, and only, let’s say, 10-20 physicians being able to take care of them,” Li said.
This is part of a yearlong series of articles exploring the use of artificial intelligence in health care that is partly funded by a grant from the Commonwealth Fund.