The recent release of the Health Data, Technology, and Interoperability (HTI-1) Final Rule by the Office of the National Coordinator for Health IT (ONC) has introduced new transparency requirements for artificial intelligence (AI) and predictive algorithms used in certified health IT. As EHR/EMR vendors explore the potential of Large Language Models (LLMs) for various applications, such as patient summaries and responding to patient emails, they must carefully consider the implications of the HTI-1 Final Rule.

LLMs have shown promise in analyzing unstructured clinical data to provide insights and recommendations. However, their complexity and opacity pose challenges in meeting the transparency requirements outlined in the HTI-1 Final Rule, particularly in terms of demonstrating the source of training data, identifying potential biases, and providing a clear understanding of how the algorithm arrived at its predictions.

Quippe –– Diagnostically Connecting Data…Transparently

In contrast, Medicomp Systems’ Quippe Clinical Data Engine offers a reliable and transparent alternative for clinical decision support. For those using LLMs for predictive decision support, which Medicomp does not offer, Medicomp’s technology provides evidence-based decision support, curated by expert clinicians, to filter and present LLM-generated content –– thereby helping vendors navigate the challenges LLMs pose considering the HTI-1 final rule.

One of the key advantages of Quippe is its extensively annotated data –– with its diagnostic prompts sourced from reputable sources such as the National Library of Medicine. This allows Medicomp to demonstrate the origin of the data used in its clinical decision support, providing the level of transparency required by the HTI-1 Final Rule.

Furthermore, Medicomp’s algorithm for producing on-screen user information is not required to be revealed under the new rule. However, we can readily provide a clear explanation of how this algorithm works, ensuring that healthcare providers thoroughly understand the technology behind the decision support they receive. In contrast, LLMs currently struggle to provide this level of transparency, making it difficult for EHR vendors relying on these models to comply with the HTI-1 Final Rule.

As the healthcare industry navigates the new landscape of algorithmic transparency, collaboration between EHR/EMR vendors, healthcare providers, and regulatory bodies is crucial. Medicomp Systems is committed to working with its partners to establish best practices, share knowledge, and address potential challenges, ensuring that the benefits of AI in healthcare are realized while prioritizing patient safety and trust.

The HTI-1 Final Rule represents a significant step forward in ensuring the responsible and ethical use of AI and predictive algorithms in healthcare. With its transparent and reliable clinical decision support platform, Medicomp Systems is well-prepared to help EHR vendors and healthcare providers navigate the challenges and opportunities that lie ahead in this new era of algorithmic transparency.