Orrum and LivaNova Partner on Medical Device Analytics
Orrum Clinical Analytics announced a strategic partnership with medical device company LivaNova. Orrum's CŌRE Insights analytics platform will be integrated with LivaNova's Essenz Perfusion System. The collaboration aims to unite intelligent data with perfusion technology used during cardiac surgery.
- Orrum Clinical Analytics was founded by perfusionists to address challenges in collecting and analyzing surgical data; its CŌRE Insights platform is built on a modern data stack using Snowflake and a managed dbt core environment to handle security and scalability for sensitive patient information. - The LivaNova Essenz Perfusion System, which received FDA 510(k) clearance in March 2023, is designed for data-driven care, with a central cockpit and patient monitor that records and displays real-time perfusion data from integrated sensors. - LivaNova, formed by a 2015 merger of Italy's Sorin S.p.A. and the US's Cyberonics Inc., employs a "razor-and-blade" business model for its cardiopulmonary division, selling capital equipment like the Essenz system to drive recurring revenue from associated high-margin disposables. - A key component of the Essenz system is the In-Line Blood Monitor (ILBM), which utilizes B-Capta™ sensing technology and is the only system of its kind compliant with Clinical Laboratory Improvement Amendments (CLIA) guidelines, providing values consistent with hospital blood gas analyzers. - The integration addresses a core architectural challenge in healthcare technology: securely ingesting and analyzing high-velocity, real-time data from medical devices and then connecting it with other hospital information systems for a unified view. - Orrum's CŌRE Registry enables a key function of mature analytics platforms by allowing surgical teams to benchmark their performance on metrics like oxygen delivery and mean arterial blood pressure against anonymized data from other teams. - The partnership reflects a broader trend in cardiac surgery to apply machine learning and predictive analytics to vast datasets from electronic health records and intraoperative devices to create dynamic, real-time clinical decision support tools.