Zuckerberg San Francisco Normal Hospital (ZSFG) has achieved a big turnaround in readmission charges by combining a predictive danger mannequin in Epic, standardized care pathways, and proactive outreach to at-risk sufferers. Lucas Zier, M.D., M.S., director of cardiovascular high quality and outcomes at ZSFG, spoke with Healthcare Innovation about this initiative in addition to efforts to share AI improvements with different security web well being methods.
Healthcare Innovation: May you discuss somewhat bit about why ZSFG and different security web hospitals have struggled with readmission charges prior to now and and a few of the monetary repercussions that has?
Zier: Popping out of the Inexpensive Care Act, one pay-for-performance metric entails readmissions, and the hospital readmission discount program was instituted. I feel it was very properly intentioned, however the impact was that well being methods that are inclined to serve susceptible and underserved sufferers, by definition, look after sufferers with medical challenges, but in addition a variety of adversarial social wants. That tends to result in a affected person inhabitants which is extra liable to readmission. The downturn impact of that’s that well being methods that serve our most susceptible sufferers are inclined to get penalized most severely for elevated readmission charges.
San Francisco Normal Hospital, as of 2016, had a few of the worst readmission charges within the state of California when in comparison with different security web hospitals. The repercussions of that have been several-fold. One was that it was imperiling about $1.2 million in funding that we have been utilizing for medical care packages. Additionally, we realized that elevated readmission was reflective of some sub-optimal outcomes that we have been having, notably in sufferers with coronary heart failure, which was was our greatest driver of readmission. We had elevated mortality charges in comparison with different security web well being methods, and we additionally had fairness gaps in care. Specifically, the Black/African-American coronary heart failure inhabitants had worse outcomes in comparison with our basic inhabitants.
HCI: Did you develop or customise a predictive mannequin inside Epic?
Zier: Sure, we took a multi-pronged method. We localized Epic’s readmission mannequin to our well being system. However we acknowledged that simply offering an end-user with a danger prediction was not going to be ample to enhance well being outcomes. We took that predictive output and developed a choice help ecosystem. We mixed that with expertise that Epic offers, the place you possibly can basically hyperlink collectively these logic-based blocks inside Epic. We recreated the center failure tips and we have been in a position to floor determination help to suppliers on the level of care that was personalised to sufferers.
We might make suggestions about medical care and medicines, and we deal with each medical wants and a restricted slice of social wants — notably substance use, was a giant problem in our coronary heart failure inhabitants. One instance of determination help can be if a affected person screens constructive for for methamphetamine use, then we have been in a position to set off a referral to our dependancy care workforce. The predictive mannequin allowed us to risk-stratify who’re our highest danger sufferers, so we might floor that info to suppliers after which immediate them to position prioritized referrals for particular person sufferers.
We had a inhabitants well being administration workforce for coronary heart failure, however they did not all the time know who to concentrate on. The advantage of the predictive mannequin was that it ran throughout our total coronary heart failure inhabitants, so our workforce might take a look at this dashboard that we developed, and see who’s anticipated to be at excessive danger for readmission. That workforce might proactively look after these sufferers in the neighborhood to attempt to forestall a readmission, earlier than it occurred, versus reacting to a readmission that already occurred.
HCI: Do a sure proportion of those sufferers not have a major care relationship? And is that a problem, so far as following them in the neighborhood?
Zier: That may be a actually good query. Sure, some sufferers do not have a major care relationship, and that is likely one of the potential cease indicators earlier than you discharge sufferers is to verify they’ve a referral to major care, as a result of we all know sufferers who get referral to and are seen by major care are inclined to do higher once they go away the hospital. However one of many advantages of the predictive mannequin is that if a affected person is anticipated to have a excessive readmission danger, then when a supplier locations a follow-up referral for cardiology, that referral will get prioritized inside our referral queue.
HCI: Was there a timeframe over which you studied the impression of this intervention?
Zier: Principally, we had recognized the issue way back to 2015. At that time, we have been utilizing LEAN methodology to begin to develop some countermeasures. These preliminary pilots have been efficient, however they weren’t scalable. We went by an Epic implementation in 2019 and that is after I began to understand that possibly there’s a possibility to make use of the EHR for large-scale, system-wide efficiency enchancment. Our outcomes are actually from 2019 to 2024. It wasn’t a randomized analysis, however we did what we name an interrupted time-series evaluation. Basically we checked out varied charges of issues earlier than we applied the device, after which after we applied the device. The particular issues that we checked out have been our readmission charges and our mortality charges amongst our coronary heart failure sufferers. We additionally seemed particularly at outcomes inside our Black/African American inhabitants, as a result of we knew we had vital fairness gaps in these outcomes.
Total, there was a 6% discount in mortality. We additionally in contrast our outcomes to 5 different peer security web hospitals in California, and we discovered that we had, in comparison with these different well being methods, a big discount in mortality amongst our coronary heart failure sufferers. You’ll be able to by no means be 100% certain until you have got a randomized trial, however we really feel that we are able to confidently conclude that it was the impact of the device.
We additionally in contrast our readmission charges to different security web hospitals throughout the identical time interval. And for a lot of of these hospitals, their readmission charges went up, whereas ours went down. At our peak, our readmission price was about 34% and at its nadir it was about 19 p.c. So we had an over 13% discount in readmission charges, taking us from mainly the worst, relying on the month that you just take a look at, to the most effective amongst security web hospitals. And basically, we have inverted this pattern with our outcomes amongst Black/African-American sufferers and our basic coronary heart failure sufferers. We had vital inequities, notably in readmissions, and by 2022, we had utterly inverted that pattern. There was no distinction in readmission price.
It has been properly documented that typically when well being methods attempt to institute readmission discount packages, that comes on the expense of different outcomes. It isn’t unusual for well being methods, for instance, to see reductions in readmission with a slight enhance in mortality, and we have been actually centered on avoiding that. That was why mortality was an essential metric for us. We have been in a position to scale back readmissions whereas additionally decreasing mortality. In our minds, it is not an efficient consequence if you happen to obtain a well being system metric, however you set your sufferers in danger.
HCI: Let’s return to what you talked about earlier — this consortial work with the opposite security web hospitals. Is that to unfold this explicit intervention at different security web hospitals or is it broader than that?
Zier: The overarching concept is that we imagine that one of these expertise has vital alternative to enhance well being outcomes in well being methods that aren’t notably well-resourced.
This value about $1 million to develop, however as a result of we’re in a position to hit readmission metrics, we saved near $8 million.
One method to this readmission discount program is simply rent 10 extra folks and ship them out in the neighborhood, however security web well being methods cannot do this. We predict there’s an actual position for AI and machine studying to handle challenges inside these well being methods. However $1 million isn’t cheap. We’re fortunate at San Francisco Normal that now we have a basis that helps to help this work. And now we have a relationship with College of California, San Francisco, which creates some infrastructure to assist us to construct a few of these personalized instruments. However most security web well being methods don’t have that sort of help. So the collaboratory was based with the intent of taking a few of the well being methods, like Parkland which has an identical setup to San Francisco, and Grady does as properly, the place we are able to function an incubator for the event of those applied sciences.
If we will develop them, we should always attempt to disseminate them and share them with different well being methods. It is nonetheless very a lot in its inception. What we’re specializing in proper now could be a generative AI device that nearly features as a digital social employee to attempt to deal with affected person social and behavioral wants inside these populations. We hope that we are able to disseminate learnings. We will additionally disseminate expertise, and we are able to make it obtainable to one another at decrease value.
