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Cognome Collaboratives
Join a community of Healthcare AI & ML Practitioners
Share and problem solve with the support of health system peers
Collaboratives provide an opportunity to join our Federated (Model) Training Data Network
Commercialization and Clinical Trial Opportunities available to Collaborative members
Collaborative Features
All models are deployed locally (on prem or in your own cloud). No data ever leaves your system.
All models within a collaborative are open - including visibility into how the models are built and how they make predictions.
Any model you use will be trained on your data to optimize performance.
Our Federated Training Data Network further elevates model performance by connecting complementary training data sets.
Models are Large Language Models (LLMs) or predictive Machine Learning (ML) Models - and sometimes both.
Peer & Partner Support: collaborate with your peers as well as Cognome's data scientists, informaticians and developers.
You will always have access to Cognome's professional services team to assist with installation, integration and optimization.
A full list of all the models can be found here. We are adding more models all the time.
For a limited time, join any Collaborative and gain access to all.
Example models include:
Sepsis Predictor, Surveillance + Reporting Bundle: Identify, keep track of and report Sepsis cases. This bundle has the lowest flag rate in the industry and will relieve your chart abstraction team from time consuming regulatory reporting. Save lives, reduces alert fatigue, and improves regulatory complaince. Peer reviewed here.
Predict Readiness for Surgery: This model assigns ASA-PS classifications uniformly across all patients so you can clearly evaluate whether or not your patient is ready for surgery. Peer reviewed in the Journal of Clinical Anesthesia.
Respiratory Failure Predictor: This early recognition model predicts ARDS allowing care teams to intervene sooner and prevent poor or worsening outcomes. Peer reviewed in the National Library of Medicine.
Models in this collaborative reduce cost from manual tasks, especially for nurses and doctors, while improving the health system's ability to retain or increase revenues. Examples include:
Case Cancellation: This model predicts patients that are "no show" risks for an appointment or procedure. A single clinic at one of our clients saw $500,000 increase in revenue during the first 6 months alone.
AutoChart: is both a machine learning model plus an LLM. Train AutoChart to do any chart abstraction currently being performed manually. A collaborative member reduced nurse chart abstraction hours for American College of Surgeons' NSQIP registry by 90%. AutoChart not only saves time and money but also reduces burnout among clinical staff.
Length-of-Stay (LoS) Predictor: Accurately plan for patient discharge while delivering a better patient and family experience. Proactively address medical, behavioral health, and SDOH issues.
Our research collaborative will show you how to take your data and make it available for research of all kinds. Both for your own team, and more broadly with others.
Clinical Trial Matching: Give this model access to your patient charts and it will show you which ones qualify for your clinical trials. The model provides a comprehensive (human readable) write up of why each patient meets or does not meet the trial criteria.
Cognome I/O: this is the foundation system on which you can build your own models. It takes data from across your ecosystem and makes it available for research and for model creation.
Cognome Search: sits on top of Cognome I/O or any OMOP database and allows cohorts to be created (or uploaded) and studies to be made based on an almost limitless number of inclusion and exclusion criteria.
All data is hosted in your environment either on-premises or in your cloud.