Shaped in 2012, the Parkland Heart for Scientific Innovation (PCCI) is a expertise analysis and growth group affiliated with Parkland Well being & Hospital System in Dallas. Healthcare Innovation lately interviewed PCCI President and CEO Steve Miff, Ph.D., about a few of the highlights of PCCI’s 2026 annual report, which has a give attention to predictive analytics and AI.
Healthcare Innovation: Steve, your annual report notes that PCCI has pioneered a novel framework to make sure reliable and sustainable AI growth, and it now has 14 fashions in manufacturing, seven in testing, one in early exploration, and others underneath growth. May you begin by speaking in regards to the growth of that reliable AI framework?
Miff: It has advanced over time, however significantly as we began to deploy fashions, we observed that it’s one thing that is required not solely on the entrance finish as you construct fashions, but additionally after deployment as you proceed to keep up and help them.
We have recognized 4 key pillars that we imagine are essential to offering the required transparency to create belief. One is prediction transparency. The second is efficiency transparency. Third is safety transparency, and forth is compliance.
With prediction transparency, what we have observed is that it is nice to have the ability to predict rising danger and the extent of danger for people, however until you’ll be able to give the small print behind what’s driving the danger, the knowledge is helpful, however not as helpful because it could possibly be, when it comes to giving customers the consolation that what they’re seeing is sensible. We develop this expertise referred to as “Islet” that permits real-time visualization of the knowledge behind a mannequin. With a click on of a button from the digital medical document, you may pop up a window that provides you not solely the present predictive rating, however the historic values. After which dynamically it brings forth the highest 5 components which might be influencing the prediction probably the most at that cut-off date. After which it offers you all of the precise information that is feeding into that.
HCI: That is attention-grabbing as a result of we frequently hear from well being system leaders that providing that sort of transparency is vital to getting clinician buy-in.
Miff: One other pillar is round compliance. It’s so necessary to be sure that any fashions which might be being deployed meet the rigor of the most recent compliance necessities. We have been a part of the Well being AI Partnership, one of many founding members with Duke and Mayo and Berkeley. They’ve printed some actually good standards and rubrics about parts that ought to then go into each the compliance on the entrance finish after which a lifecycle administration of AI. We have now recognized a rubric of 20 to 30 totally different parts that we put each single mannequin by earlier than is being deployed and evaluated as an internally generated service.
The third pillar is round safety. No matter occurs with the information must be within the safe atmosphere, since you’re managing PHI and managing a number of information sources that want to come back collectively. You will need to spotlight that and continuously take note of it, and have all of the the rigor, the accreditations and all these parts in place.
The final one is round efficiency transparency. The extra fashions we deployed, the extra time we’re spending really monitoring them to be sure that they carry out in accordance with nonetheless they have been designed, how they’re skilled, and that they are not beginning to deviate. That turns into overwhelmingly time-consuming, and we’re spending extra time on monitoring issues than really being able to develop new issues. So we constructed and are within the course of deploying an AI monitoring dashboard that automates loads of these statistical capabilities of the fashions which might be being deployed. We’re additionally doing that now for LLMs and ambient listening fashions. You will need to be capable to create these guardrails of what is anticipated, from a statistical perspective after which be alerted when the mannequin begins to deviate from the parameters that you’ve got recognized.
HCI: Can we stroll by a few of the AI improvements described within the annual report? However first I wished to ask whether or not a few of these improvements could possibly be commercialized or exported past Parkland’s use?
Miff: Sure, we design them that manner. We’re not ourselves a industrial entity, however we’re all the time wanting to have the ability to replicate these in different environments. For instance, our trauma mortality mannequin, which is exclusive, is a little bit bit extra area of interest as a result of it applies to Degree 1 trauma facilities and predicts real-time mortality — we’re within the technique of deploying that at Grady Well being in Atlanta.
One other factor we have carried out with a number of entities and well being programs, and even with payers, is the work that we’re doing with our Neighborhood Vulnerability Compass, which is admittedly granular SDOH information, nevertheless it’s carried out on the block group stage. We reverse geocode, and attribute to a affected person document their block traits, so now we’ve it on 100% of the sufferers with out the necessity to interview them. We simply printed a paper on this in JAMIA and it actually confirmed that it has unbelievable recall charges, not solely on the general index stage, however once you take a look at particular indicators, equivalent to whether or not someone has meals insecurities or housing instability. It’s wonderful to have the ability to take a block group info attribute to a document after which for that to be so extremely indicative of what that individual says. We have now 50-plus organizations that now use it.
HCI: I learn that’s being utilized by the United Method of their information capacity-building initiative within the Dallas space.
Miff: Sure, it’s been a six-year journey with them. What I simply completely love about that’s that it is foundational in a number of layers. United Method has been utilizing it for years to trace the affect that their investments within the communities are having, and monitor that yr over yr.
United Method additionally wished to deliver the group organizations in and improve their information capability. As an alternative of simply saying we will pay so that you can have licenses to entry this, they discovered 200 organizations and put them into cohorts that undergo a six-month curriculum to learn to apply it to their particular scenario. It’s been wonderful to see. That is thrilling, as a result of it’s instructing folks the best way to use information.
HCI: Let me ask about a few different predictive instruments which might be used within the hospital setting. One is a office security prediction device. Does it display sufferers for the potential of violent interactions?
Miff: That is what it does. As you already know, violence in opposition to frontline employees is actually a pandemic. It is gotten considerably worse after the COVID pandemic, and it continues to be an enormous problem. Many organizations are specializing in making an attempt to alleviate the issue. This pulls it from a number of sources. This additionally makes use of the Neighborhood Vulnerability Compass information. It even makes use of issues equivalent to smoking standing, earlier involvement with prison justice, or earlier violent occasions. It pulls all this complicated info collectively and principally predicts the probability that that encounter will end in a violent occasion. It’s a must to be very cautious that you simply’re not profiling people. You are actually figuring out triggers. This is likely one of the most weak time in our lives, once we’re within the hospital for our personal well being or a liked one, and also you add all these different issues that compile that anxiousness. For instance, smoking all the time exhibits up within the prime 10 predicting components. All hospital campuses are smoke-free. In case you are a heavy smoker and never in a position to smoke, that provides to your stress, and begins to create the next danger.
HCI: You even have a pre-term start prevention program.
Miff: The pre-term start concerned constructing a predictive mannequin taking a look at underlying components to determine girls who’re more likely to have a pre-term supply. Initially that program initiated each schooling to girls through texting and alerts to their suppliers. Then a broader coalition got here collectively to do extra work on this house, and we’re the analytical engine behind it. We’re utilizing CVC that we mannequin throughout these sufferers to grasp the non-medical limitations and drivers of well being. We’re modeling with information from a neighborhood supply that is referred to as the DFW Hospital Basis, the place we’ve near 100% of all pregnancies that happen throughout the 2 counties and the related collection of examine problems. So we’re in a position to geocode and mannequin these to grasp the place the very best density of those critical septic problems happen, and what’s the make-up of these neighborhoods. One of many intervention is iron distribution to have the ability to give pregnant girls iron very early within the being pregnant. We’re utilizing this to determine areas the place the iron distributions happen.
We additionally construct a maternal well being forecasting mannequin. Beforehand we had constructed a diabetes surveillance system, and we’re modeling it after that. The diabetes mannequin predicts deterioration that may require ED visits and hospitalization 12 months out. It is the neighborhood stage, and it offers you each the medical points which might be driving that prediction and the non-medical drivers, and it ranks them, and it’s totally dynamic.
HCI: One other one featured within the annual report is a digital imaging surveillance system that leverages generative AI to determine missed analysis for follow-up care. We have now written about well being programs which might be making an attempt to do a greater job of following up on incidental imaging findings. Is that this just like these efforts?
Miff: There are hidden issues within the notes from the radiology report equivalent to incidental findings and Parkland’s been doing this manually for various years. We now use LLMs to scan by all these notes and determine these incidental findings. I feel it is wonderful how strong the accuracy is — the place it is really extra correct than people doing this.
