togetherMADE is a clinical decision-support platform that helps healthcare providers access and apply the latest medical evidence at the point of care. The platform combines AI-guided recommendations with moderated peer-to-peer networks, adapting guidance to the medicines, tools, and training available in each clinician's specific resource setting. It aims to shrink the 17-year gap between medical discovery and clinical adoption to just weeks.
Funding
Funding not disclosed
Founders
Product
Problem
It takes an average of 17 years for new medical evidence to become standard practice, and only 1 in 5 best practices ever reach the bedside. Life-saving evidence is often paywalled, stuck in group chats, irrelevant to many hospitals, or overwhelming to clinicians, leaving providers—across the U.S. and globally—unable to access or apply reliable guidance in time or in context. This gap leads to poorer patient outcomes, with patients up to 60% less likely to die when care follows proper guidance.
Solution
togetherMADE provides a bedside-ready clinical decision-support app that combines clinician-in-the-loop AI, machine learning, and moderated peer-to-peer networks to deliver resource-adaptive guidance in real time. The platform accelerates evidence into practice by shrinking the 17-year lag down to weeksaine and provides recommendations tailored to the medicines, tools, and training actually available at the moment of care. When evidence is unavailable, users can connect with expert peers in a HIPAA-compliant, Reddit-style community of practice for knowledge exchange across geographies and contexts. The platform also empowers sustainable partnerships by flowing royalties back to the hospitals, societies, and researchers whose content and insights power the platform.
Target Audience
The platform is designed to serve all clinicians, especially the 10 million globally who lack access to credible, relevant medical information that accounts for the medicines, tools, and training available at the point of care. This includes providers in rural and low-resource settings across the U.S. and internationally, such as the nurse practitioners and trauma clinicians referenced in the platform's user research.
Features
- Clinician-in-the-loop AI that combines machine learning with human expertise to generate context-relevant, evidence-based recommendations
- Resource-adaptive guidance technology that tailors clinical advice to the specific medicines, tools, and training available in the clinician's setting
- HIPAA-compliant, moderated peer-to-peer community of practice for clinician knowledge exchange, modeled on Reddit-style interaction
- Bedside-ready mobile application designed for fast decision-making in any clinical environment, from rural clinics to major hospitals
- Patent-pending technology platform that prioritizes applicability of evidence to local resource constraints