“HS patients benefit when all available information on their journey can be surfaced and used to improve care and decision-making,” Joseph Zabinski tells DermWire. He is the Managing Director of AI and Personalized Medicine at OM1. “Much of this information is difficult to access at scale, but by using AI, we can automate the process and enrich datasets with both extracted insights, and those derived from other content.“
This allows for better visibility into outcomes, the ability to explore response to treatments, and even predict risk of future events, he says. “These capabilities vastly enhance value for HS patients and the providers who care for them.”
Leveraging OM1’s augmented, automated, and manual note abstraction capabilities and a clinical dataset, providers now have access to treatment utilization and machine learning-generated Hurley Stage estimations. This allows providers to more accurately diagnose and stage disease progression to then develop targeted treatment plans.
The dataset, which is built off patient records collected over the last decade, includes natural history of disease and patient journeys, treatment effectiveness, phenotypic subtyping, and more, allowing for stakeholders across the healthcare ecosystem to better understand the disease.
The HS dataset breaks down electronic medical record demographical data including geography, comorbidities, treatments, and provider specialty, and amplifies it with unstructured data from medical claims and physician notes of disease stage, anatomic location, and disease manifestation.
“It offers a narrative of the patient journey – most specifically the information not otherwise captured in claims data – the presentation and location of the disease, “ adds Stefan Weiss, MD, MBA, FAAD Managing Director of Dermatology at OM1. “Understanding these two key factors allows for a differentiation of the patient experience and need for advanced therapeutics.”