We have no shortage of relevant material to discuss when it comes to healthcare, health data, and Artificial Intelligence (AI) — topics that we can engage with on a near minute-by-minute basis. Our goal is to focus only on what truly matters to our world, particularly healthcare. While it’s tempting to dive into the simultaneous advancements in Quantum Computing and AI — and rest assured, books on those are being written right now — we’re more interested in the reality of the here and now. Large Language Models (LLMs) are already becoming commodities, and despite the construction of more and more data centers, with NVIDIA releasing two new chips (Blackwell Ultra and Rubit AI) later this year, the question remains: how do companies keep up and remain relevant in such a landscape? The answer, ultimately, lies in data — and the protection of that data.
The concept of a “moat” is well understood in industries with heavy regulatory oversight, and in the U.S., there is no greater moat than that in healthcare. Federal and state regulations are both deep and far-reaching. LLMs, for obvious reasons related to security and privacy, can’t access Electronic Health Records (EHR) of individuals. However, under specific conditions, they may gain access to de-identified (de-ID) medical data on a company-by-company basis.
It’s no secret that millions of de-identified medical records are bought and sold as valuable assets. There’s no more valuable set of mass information than medical records, in part because, by design, they contain far more detailed personal information about individuals than any other set of records. When compared to banking, tax, or credit records, the data within the 1.2 billion EHR records in the U.S. is exponentially richer.
Among the hundreds of articles on AI in my “to be discussed” file, one stood out and reinforced my belief that AI is going to change healthcare — though not in the way most people expect. In our view, AI isn’t quite ready for most healthcare applications, especially given the high costs, time constraints, safety concerns, and the energy consumption (both in terms of provider time and literal power usage). That’s why we’re focusing on using AI to empower the consumer. We are convinced that this approach will not only advance the “science” of healthcare but also make significant strides when we link clinical and genetic data into a single consumer data set.
Just a few days ago, the esteemed Journal of the American Medical Association (JAMA®) released a research letter titled “Manual vs. AI-Assisted Prescreening for Trial Eligibility Using Large Language Models—A Randomized Clinical Trial.” Below, I briefly discuss its findings.
Our Big Idea
If you’ve made it this far, I encourage you to read the summary and conclusion below. In our mission to consolidate the consumer’s EHR data, we believe we can make significant progress in identifying patients who may benefit from new clinical trials and connecting them — with their consent — to trial opportunities. But not just in one clinical area (such as cardiology) or within a single hospital network. We aim to extend this across most clinical applications, unencumbered by a single EHR data set.
The JAMA® study highlighted that AI significantly enhances clinical trial selection and enrollment, streamlining the traditionally labor-intensive and costly process of patient recruitment. Traditional manual screening methods require extensive human labor, including detailed reviews of unstructured patient data, and this can account for up to one-third of clinical trial expenses. AI tools, particularly the Retrieval Augmented Generation Enabled Clinical Trial Infrastructure for Inclusion Exclusion Review (RECTIFIER), use large language models (LLMs) to rapidly interpret and process unstructured EHR data, dramatically speeding up the patient screening process.
The study further emphasized that RECTIFIER outperformed older natural language processing (NLP) techniques in accurately interpreting and analyzing unstructured clinical information. By automating patient eligibility verification, RECTIFIER significantly reduces the manual burden, allowing research staff to focus more on patient care and enrollment. In practical terms, the tool reduced the backlog of patients awaiting manual screening from hundreds to just a few dozen, nearly doubling enrollment rates and substantially cutting down the time needed to identify suitable trial participants.
Critically, while the RECTIFIER tool showed impressive results in a cardiology-specific context within a large hospital network, its design allows for broader application across multiple clinical settings and diverse disease areas. The AI-assisted prescreening demonstrated comparable accuracy to manual methods, minimizing false positives and bolstering trust in its reliability. As this technology undergoes additional external validation, it holds great potential for widespread adoption, speeding up clinical trial completion and providing quicker access to new, potentially life-saving therapies for patients.
How AI Will Forever Change the Consumer
We have consistently highlighted the immense value of having all — or nearly all — of a consumer’s EHR data in one place. This Universal Health Record (UHR)™, when paired with carefully curated AI (not LLMs like those in the study above), has the potential to both inform consumers about their health and enhance communication between them and their healthcare providers. Moreover, it can help consumers find clinical trials they may be interested in and even challenge insurance companies regarding the denial of services, care, or medication.
Our mission has always been clear: to empower consumers and advance the science of healthcare in an increasingly complex — and at times, adversarial — environment for patients.
About HealthScoreAI ™
Healthcare is at a tipping point, and HealthScoreAI is positioning to revolutionize the industry by giving consumers control over their health data and unlocking its immense value. U.S. healthcare annual spending has exceeded $5 trillion with little improvement in outcomes. Despite advances, technology has failed to reduce costs or improve care. Meanwhile, 3,000 exabytes of consumer health data remain trapped in fragmented USA systems of 500 EHRs, leaving consumers and doctors without a complete picture of care.
HealthScoreAI seeks to provide a unique solution, acting as a data surrogate for consumers and offering an unbiased holistic view of their health. Giving Consumers tools to respond to denial of care by insurers, we aim to bridge gaps in healthcare access and outcomes. By monetizing de-identified data, HealthScoreAI seeks to share revenue with consumers, potentially creating a new $100 billion market value opportunity. With near-universal EHR adoption in the USA, and advances in technology, now is the perfect time to capitalize on the data available, practical use of AI and the empowering of consumers, in particular the 13,000 tech savvy baby boomers turning 65 every single day and entering the Medicare system for the first time. Our team, with deep healthcare and tech expertise, holds U.S. patents and a proven track record of scaling companies and leading them to IPO.
Noel J. Guillama-Alvarez
https://www.linkedin.com/in/nguillama/
+1-561-904-9477, Ext 355