We should learn
from every patient consultation

Every consultation creates stacks of information that
are mostly lost or forgotten.

Why Corti?

Everything has changed in healthcare, except for patient consultation which has remained the same since the time of Hippocrates. The problem is that medical professionals see tens of thousands of patients during their careers, but they do it alone, with little to no help, carrying the burden and the risk of mistakes in solitude.

Still, every difficult patient journey starts with a consultation that either was lacking, happened too late, or didn't happen at all, and the stress and blame can be hard for one individual to carry. To shield from pressure, our sophistication has turned to fragmentation through documentation, which has led to medical professionals spending half their day looking at charts, notes, and computer screens instead of in the eyes of patients.

Corti was founded in 2016 to give healthcare professionals a set of supportive tools that transcend today's time-consuming technologies.

Unlike every other vendor in healthcare, we do not want to bring yet more screens and technology but a way to leave them behind, as we are building bleeding-edge artificial intelligence to enhance humans and remove the clutter of administrative processes.

Corti's team encompasses some of the world's brightest data scientists, developers, designers, and creators, all choosing to be here to give healthcare professionals more time with patients and less time looking at a screen.

The team set out to create intelligent systems that work as an extension of the medical professional, making the burdens of triaging, note-taking, coding, and quality assurance into team sports instead of stressful isolated endeavors.

8×

Increase in calls that receive feedback

Seattle Fire Dept

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21%

Reduction in Mean Call Handling Time

SOS Alarm, Sweden

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46%

More critical emergencies identifed

Copenhagen EMS, DK

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Corti's Medical AI Platform

We trained Corti on thousands of hours of real patient calls and consultations. Using proprietary speech recognition and natural-language processing, Corti listens alongside professionals and turns this learning into actionable insights and suggestions.

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Laying the foundation for a better dialogue

Corti has an intuitive decision-support software that assists dispatchers through the triaging flow, improves their caring response, and reduces their number of mistakes.

AI-Powered Medical Coding

Increase revenue per visit and reduce claim denial rate with Corti's automated medical coder AI. Corti allows your staff to focus on critical, revenue-generating activities.

AI-powered decision support for when it matters most

Medical call takers make better, more consistent decisions when supported by Corti's AI. It listens in to the interview and makes suggestions, like a real-time second opinion.

Drive quality and efficiency across every interaction

Rally your organization towards excellence and empower every person to drive change. Corti analyzes every interaction to benchmark targets, identify training opportunities, and improve results.

Insights and Innovations

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Machine learning can support dispatchers to better and faster recognize out-of-hospital cardiac arrest during emergency calls: A retrospective study

Fast recognition of out-of-hospital cardiac arrest (OHCA) by dispatchers might increase survival. The aim of this observational study of emergency calls was to (1) examine whether a machine learning framework (ML) can increase the proportion of calls recognizing OHCA within the first minute compared with dispatchers, (2) present the performance of ML with different false positive rate (FPR) settings, (3) examine call characteristics influencing OHCA recognition.

On Scaling Contrastive Representations for Low-Resource Speech Recognition

Recent advances in self-supervised learning through contrastive training have shown that it is possible to learn a competitive speech recognition system with as little as 10 minutes of labelled data. However, these systems are computationally expensive since they require pre-training followed by fine-tuning in large parameter space. This paper explores the performance of such systems without fine-tuning by training a state-of-the-art speech recognizer on the fixed representations from the computationally demanding wav2vec 2.0 framework.

Do End-to-End Speech Recognition Models Care About Context?

The two most common paradigms for end-to-end speech recognition are connectionist temporal classification (CTC) and attention-based encoder-decoder (AED) models. It has been argued that the latter is better suited for learning an implicit language model. In this paper, this hypothesis is tested by measuring temporal context-sensitivity and it is evaluated how the models perform when the amount of contextual information is constrained in the audio input.