We currently offer solutions for use in general Medicine and Surgery departments of acute care hospitals. The primary users of our solutions are doctors, nurses and clinical managers.

Our AI models use patient data that already exists within your EHR.

Our interoperability platform provides seamless integration with modern and legacy EHRs through HL7, FHIR, and databases. Our solution enables the exchange of accurate and timely patient information between EHRs and our AI solutions. 

No, our solutions automatically pull data from your EHR and use it in the format in which it exists. There is no additional data entry or transformation required on your part.

No, our AI applications run on the cloud and connect directly with your on-premise and cloud infrastructure.

We train our models on large samples of historical data from your hospital. We identify a target for the model – such as, being clinically stable or clinically deteriorating. From your historical data, the machine learning algorithm uncovers patterns in patients’ data that are associated with that target. Then, we test the accuracy of the model on another historical sample to see how well the model’s predictions compare to what actually happened.

We produce a detailed ‘model validation report’ for every hospital in which we deploy a solution. This report explains what data the model uses, how the model was constructed and trained, the clinical and operational features that the model learns from and a comprehensive evaluation of the accuracy, robustness and fairness of the model.

The outputs of our models are directly integrated into existing IT and communication systems such as your EMR, emails or text messaging and existing hardware you have in your unit.

We recognize the importance of patient privacy. Our platform meets industry standards for data privacy and security including being HIPAA compliant and SOC2-Type 2 certified. We have a strict privacy policy that describes the specific practices and guidelines that we follow to help ensure the confidentiality and security of your personal information when you use our services.

We can typically train our models and integrate our predictions into your workflows within 60 days depending on the setup and scope of deployment. We can define this scope in a few short discussions.

Our products are currently in use at a number of hospitals in Canada including St. Michael’s Hospital and Grand River Hospital.

We are commercializing AI applications that were originally developed by researchers at St. Michael’s Hospital in Toronto, Canada.  The team at St. Michael’s Hospital has published multiple papers on their experience deploying AI into clinical use, including:

Verma AA, Murray J, Greiner R, et al. Implementing machine learning in medicine. CMAJ. Aug 2021, 193(34): 1351-1357. https://www.cmaj.ca/content/193/34/E1351

Cohen J., Cao T, Viviano J, et al. Problems in the deployment of machine-learned models in health care. CMAJ. Sep 2021, 193(35): 1391-1394. https://www.cmaj.ca/content/193/35/E1391

Antoniou T, Mamdani M. Evaluation of machine learning solutions in medicine. CMAJ. Sep 2021, 193 (36): 1425-1429. https://www.cmaj.ca/content/193/36/E1425

Pou-Prom C, Murray J, Kuzulugil S, et al. From compute to care: Lessons learned from deploying an early warning system into clinical practice. Front. Digit. Health. 2022 4:932123. https://scholar.google.com/citations?view_op=view_citation&hl=en&user=lqbus3sAAAAJ&sortby=pubdate&citation_for_view=lqbus3sAAAAJ:KlAtU1dfN6UC

Verma AA, Pou-Prom C, McCoy LG, et al. Developing and validating a prediction model for death or critical illness in hospitalized adults, an opportunity for human-computer collaboration. Critical Care Explorations, forthcoming.