Fashioned in 2012, the Parkland Heart for Scientific Innovation (PCCI) is a know-how 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 deal with 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 below growth. Might you begin by speaking in regards to the growth of that reliable AI framework?
Miff: It has advanced 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 keep up and assist 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 seen 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 main points behind what’s driving the chance, the knowledge is helpful, however not as helpful because it could possibly be, by way of giving customers the consolation that what they’re seeing is smart. We develop this know-how known 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 essentially the most at that cut-off date. After which it provides you all of the precise knowledge that is feeding into that.
HCI: That is attention-grabbing as a result of we regularly hear from well being system leaders that providing that type of transparency is essential to getting clinician buy-in.
Miff: One other pillar is round compliance. It’s so vital to guarantee 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 parts 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 completely different parts that we put each single mannequin via 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 surroundings, since you’re managing PHI and managing a number of knowledge sources that want to come back collectively. It is very important spotlight that and continuously take note of it, and have all of the the rigor, the accreditations and all these elements in place.
The final one is round efficiency transparency. The extra fashions we deployed, the extra time we’re spending truly monitoring them to guarantee that they carry out in accordance with nonetheless they had 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 quite a lot 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 have the ability 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 have recognized.
HCI: Can we stroll via a number 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 method. We’re not ourselves a industrial 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 a bit of 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 finished 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 basically granular SDOH knowledge, but it surely’s finished on the block group degree. We reverse geocode, and attribute to a affected person document their block traits, so now we now 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 once you have a look at particular indicators, similar to whether or not someone has meals insecurities or housing instability. It’s superb to have the ability to take a block group data attribute to a document 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 Means of their knowledge 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 Means has been utilizing it for years to trace the influence that their investments within the communities are having, and observe that 12 months over 12 months.
United Means additionally wished to deliver the neighborhood organizations in and enhance their knowledge 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 state of affairs. It’s been superb to see. That is thrilling, as a result of it’s educating individuals use knowledge.
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 screen sufferers for the potential of violent interactions?
Miff: That is what it does. As you understand, violence towards frontline workers 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 attempting to alleviate the issue. This pulls it from a number of sources. This additionally makes use of the Group Vulnerability Compass knowledge. It even makes use of issues similar to smoking standing, earlier involvement with felony justice, or earlier violent occasions. It pulls all this complicated data collectively and mainly predicts the probability 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 without doubt one of the most susceptible time in our lives, once 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 nervousness. For instance, smoking at all times reveals up within the high 10 predicting components. All hospital campuses are smoke-free. In case you are a heavy smoker and never capable of smoke, that provides to your stress, and begins to create the next danger.
HCI: You even have a pre-term beginning prevention program.
Miff: The pre-term beginning concerned constructing a predictive mannequin taking a look at underlying components to determine ladies who’re more likely to have a pre-term supply. Initially that program initiated each training to ladies 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 know the non-medical limitations and drivers of well being. We’re modeling with knowledge from a neighborhood supply that is known as the DFW Hospital Basis, the place we now have near 100% of all pregnancies that happen throughout the 2 counties and the related sequence of examine issues. So we’re capable of geocode and mannequin these to know the place the very best density of those severe septic issues happen, and what’s the make-up of these neighborhoods. One of many intervention is iron distribution to have the ability to give pregnant ladies 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 prognosis for follow-up care. We’ve written about well being techniques which might be attempting to do a greater job of following up on incidental imaging findings. Is that this much like these efforts?
Miff: There are hidden issues within the notes from the radiology report similar to incidental findings and Parkland’s been doing this manually for numerous years. We now use LLMs to scan via all these notes and determine these incidental findings. I feel it is superb how strong the accuracy is — the place it is truly extra correct than people doing this.
