Doing Right By You Matters.
We take safety seriously. We’re a group of doctors and engineers, but we’re also patients, caregivers, mothers, fathers, sons and daughters. We know that getting it right matters. And we know that right now, it’s a pain to get good healthcare information. AND that information needs to help you better navigate a system that can be tough, on a good day.
Who We Are
Since 2017, ClosedLoop has been at the forefront of healthcare AI, leveraging our deep expertise to create cutting-edge technology that makes healthcare smarter and more effective. Our platform has generated over 700 million AI-driven predictions in 2023 alone, helping doctors and healthcare providers make better decisions for over 9 million people. With a strong focus on healthcare, we use the latest in AI and machine learning to provide accurate, reliable predictions that improve patient care. Recognized as “Best in KLAS” for three years in a row, doctors and hospitals who use our tools have consistently rated us as one of the top companies in healthcare technology. For you, this means that the team behind Healthy is using some of the most trusted and highly rated tools available to ensure you get the best possible experience.
Getting it Right
At Healthy, our approach is deeply rooted in research and a commitment to safety and accuracy. We develop AI tools that not only deliver reliable wellness guidance but also prioritize transparency and trust. By rigorously testing our AI models, including through expert clinical reviews and advanced safety mechanisms, we ensure that every recommendation aligns with the highest standards of care. Our focus on creating interpretable and precise AI solutions reflects our dedication to empowering healthcare providers with tools that enhance patient outcomes while maintaining the highest levels of safety.
References
1. Reardon, M., MD, MBA, & O’Dell, D., PhD. (2024). A Safe Path to Accessible, Personalized Health: Approaches to LLM Infrastructure that Improve Medical Accuracy and Safety.
2. McCall CJ, DeCaprio D, Gartner J. The measurement and mitigation of algorithmic bias and unfairness in healthcare AI models developed for the CMS AI health outcomes challenge. medRxiv. 2022 Oct. doi: 10.1101/2022.09.29.22280537. [CrossRef].
3. Bhatt, S., Cohon, A., Rose, J., Majerczyk, N., Cozzi, B., Crenshaw, D., & Myers, G. (2021). Interpretable machine learning models for clinical decision-making in a high-need, value-based primary care setting. NEJM Catalyst Innovations in Care Delivery, 2(4). https://doi.org/10.1056/CAT.21.0008
4. DeCaprio, D., Gartner, J., McCall, C. J., Burgess, T., Garcia, K., Kothari, S., & Sayed, S. (2020). Building a COVID-19 vulnerability index. Journal of Medical Artificial Intelligence, 3(6), Article 47. https://doi.org/10.21037/jmai-20-47
5. Gartner, J. (2021, October 12). A new metric for quantifying machine learning fairness in healthcare. ClosedLoop.ai. https://www.closedloop.ai/blog/a-new-metric-for-quantifying-machine-learning-fairness-in-healthcare/