Fashioned in 2012, the Parkland Middle 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 number 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 beneath growth. May you begin by speaking concerning the growth of that reliable AI framework?
Miff: It has developed over time, however notably as we began to deploy fashions, we seen 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 take care of and help them.
We have recognized 4 key pillars that we consider are important 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 seen is that it is nice to have the ability to predict rising threat and the extent of threat for people, however until you’ll be able to give the small print behind what’s driving the chance, the data is helpful, however not as helpful because it might be, by way of giving customers the consolation that what they’re seeing is smart. We develop this expertise referred to as “Islet” that permits real-time visualization of the data behind a mannequin. With a click on of a button from the digital medical file, 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 provides you all of the precise information that is feeding into that.
HCI: That is fascinating as a result of we regularly 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 ensure that any fashions which might be being deployed meet the rigor of the newest 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 revealed some actually good standards and rubrics about components that ought to then go into each the compliance on the entrance finish after which a lifecycle administration of AI. We’ve recognized a rubric of 20 to 30 totally different components 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 info must be within the safe setting, since you’re managing PHI and managing a number of information sources that want to come back collectively. It is very important spotlight that and continually 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 truly monitoring them to ensure that they carry out based on nevertheless they have been designed, how they’re educated, and that they don’t seem to be beginning to deviate. That turns into overwhelmingly time-consuming, and we’re spending extra time on monitoring issues than truly being able to develop new issues. So we constructed and are within the course of deploying an AI monitoring dashboard that automates a variety of these statistical features of the fashions which might be being deployed. We’re additionally doing that now for LLMs and ambient listening fashions. It is very important be capable of 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 have recognized.
HCI: Can we stroll by a number of the AI improvements described within the annual report? However first I needed to ask whether or not a few of these improvements might be commercialized or exported past Parkland’s use?
Miff: Sure, we design them that approach. We’re not ourselves a business entity, however we’re at all times wanting to have the ability to replicate these in different environments. For instance, our trauma mortality mannequin, which is exclusive, is slightly bit extra area of interest as a result of it applies to Stage 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 accomplished with a number of entities and well being techniques, and even with payers, is the work that we’re doing with our Group Vulnerability Compass, which is actually granular SDOH information, but it surely’s accomplished on the block group degree. We reverse geocode, and attribute to a affected person file their block traits, so now now we have it on 100% of the sufferers with out the necessity to interview them. We simply revealed a paper on this in JAMIA and it actually confirmed that it has unimaginable recall charges, not solely on the general index degree, however whenever you take a look at particular indicators, resembling whether or not anyone has meals insecurities or housing instability. It’s superb to have the ability to take a block group info attribute to a file after which for that to be so extremely indicative of what that individual says. We’ve 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 influence that their investments within the communities are having, and observe that yr over yr.
United Method additionally needed to deliver the neighborhood organizations in and improve their information capability. As an alternative of simply saying we’ll 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 superb to see. That is thrilling, as a result of it’s educating folks learn how 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 understand, 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 Group Vulnerability Compass information. It even makes use of issues resembling smoking standing, earlier involvement with prison justice, or earlier violent occasions. It pulls all this advanced info collectively and principally predicts the chance that that encounter will end in a violent occasion. You must be very cautious that you just’re not profiling people. You are actually figuring out triggers. This is among the most susceptible time in our lives, after we’re within the hospital for our personal well being or a cherished one, and also you add all these different issues that compile that anxiousness. For instance, smoking at all times reveals up within the prime 10 predicting components. All hospital campuses are smoke-free. If you’re a heavy smoker and never in a position to smoke, that provides to your stress, and begins to create a better threat.
HCI: You even have a pre-term delivery prevention program.
Miff: The pre-term delivery concerned constructing a predictive mannequin underlying components to determine girls who’re prone to have a pre-term supply. Initially that program initiated each schooling to girls by way of texting and alerts to their suppliers. Then a broader coalition got here collectively to do extra work on this area, 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 now we have near 100% of all pregnancies that happen throughout the 2 counties and the related collection of research problems. So we’re in a position to geocode and mannequin these to grasp the place the 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 can require ED visits and hospitalization 12 months out. It is the neighborhood degree, and it provides you each the medical points which might be driving that prediction and the non-medical drivers, and it ranks them, and it is very 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’ve written about well being techniques 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 resembling incidental findings and Parkland’s been doing this manually for a lot of years. We now use LLMs to scan by all these notes and determine these incidental findings. I believe it is superb how sturdy the accuracy is — the place it is truly extra correct than people doing this.
