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| Etiometry Inc | |
|---|---|
| Type | Privately held company |
| Industry | Medical Device |
| Founded | 2010 |
| Founder | Jesse Lock, MD, Dimitar Baronov and Evan Butler |
| Headquarters | Boston, MA |
Area served | Worldwide |
| Key people | Dimitar Baronov, Evan Butler and Shane Cooke |
| Products | Etiometry platform |
Number of employees | 50+ |
| Website | https://www.etiometry.com/ |
Etiometry, Inc. is an American medical technology company based in Boston, Massachusetts, founded in 2010. It develops the Etiometry Platform (also known as the T3 Platform), a regulated medical device software system for intensive care units (ICUs). According to product documentation, the platform aggregates real-time physiologic data and employs risk analytics to help clinicians identify early signs of deterioration in pediatric and adult critical care settings. It has received FDA 510(k) clearances, CE marking under EU Medical Device Regulation (MDR), and Health Canada licensing. Peer-reviewed publications have assessed the platform's clinical utility as a clinical decision support system, and published case studies have documented its use in academic intensive care units.
Etiometry was founded by Dimitar Baronov, Evan Butler, and Jesse Lock, drawing from collaborations between Boston Children's Hospital and Boston University. The founders applied aerospace engineering principles to ICU data modeling, leading to the creation of the initial T3 platform. Early pilots at Boston Children's and The Hospital for Sick Children (Toronto) informed the platform's design before its commercialization in 2014.
The first FDA 510(k) clearance was secured in March 2015 for the core data visualization module. Later clearances covered advanced risk indices: the IDO2(Inadequate Oxygen Delivery Index) (2016) and IVCO2 (Inadequate Ventilation of CO2) (2019). On February 12, 2025, Etiometry obtained its tenth clearance (510(k) K241479), which combined four indices (IDO2, IVCO2, ACD, and HLA) with cybersecurity compliant with FDA Section 524B, as reported in independent industry media. A further clearance was obtained on April 3, 2026 (510(k) K254066) for the Etiometry Platform under the Special 510(k) pathway.
The Etiometry Platform combines Data Aggregation and Visualization to integrate continuous data from bedside devices and laboratory systems into a time-sequenced dashboard for clinicians.
Risk Analytics Engine (RAE) computes real-time risk indices using Bayesian models:
According to product documentation and published studies, the platform supports local and remote ICU monitoring.
The following published studies have explored the platform's potential role in supporting clinical decision-making in critical care settings.
| Authority / Standard | Region | Details |
|---|---|---|
| FDA 510(k) (K241479) | USA | Ten clearances 2015–2025, including 2025 submission that added four analytic indices and cybersecurity improvements under FDA guidance Section 524B. |
| CE Mark (Class IIa, MDR) | EU | Certified for pediatric and adult ICU monitoring under EU MDR. |
| Health Canada MDL | Canada | Licensed as a Class III medical device; maintained through MDSAP audit. |
| ANVISA Registration | Brazil | Registered Class II medical device via MDSAP pathways. |
| ISO 13485:2016 | Global | Certified medical device QMS via MDSAP registry. |
| MDSAP | Global | Verified audit covering FDA, Health Canada, ANVISA. |
| ISO/IEC 27001 | Global | Certified information security management system. |
| SOC 2 Type II | USA | Independent audit of security controls. |
| HIPAA | USA | The platform is designed to satisfy HIPAA requirements. |
| FDA Section 524B Cybersecurity | USA | 2025 clearance aligned with updated cybersecurity rules. |
According to published reports, the platform has been implemented in hospitals in North America, Europe and Asia. Published use cases include cardiac output monitoring post-surgery, extubation decision support, remote ICU monitoring, and automation of hospital-defined protocols for workflows such as breathing trials. The platform archives high-frequency patient data, which has been used in retrospective research and internal model development.