Meet IANN

Inference Analytics

Neural Network

  • IANN’s neural network self-directs learning, driving deeper insights from clinical notes well beyond the capability of traditional NLPs.

  • Studies have proven that physician productivity increases by 20% when supported by IANN.

  • IANN produces readmission predictions with greater than 80% accuracy.IANN dynamically assesses risk to enhance regulatory and contract compliance.

Clinical Notes Insights and Followup Tracking

  • IANN is capable of classifying every Covid-19 journal article being published based on complete semantic content not simply key words or titles

  • IANN’s deep-learning platform recommends the articles most relevant to the specific search inquiry (https://www.c19search.com/) results show much higher relevance than other search options. 

  • IANN’s prediction model is capable of predicting disease propagation patterns with high confidence

IANN Covid-19 Search Engine

  • IANN radiology impression capabilities enable radiologists to complete cases 20% faster, reducing radiologist workload and stress

 

  • IANN’s impression generation capabilities have reduced medical errors by 28%

 

  • IANN’s neural network self-directs learning, enhancing knowledge and capabilities with each case review

Radiology Reports

  • IANN’s neural network facilitates extraction of a patient’s clinical status from notes and translates into coded data.

 

  • IANN translates the additional coded data into improved quality reporting, public health data and disease tracking and trending

 

  • IANN’s neural network self-directs learning, enhancing clinical knowledge through concurrent and /or retrospective case reviews

CDI and Revenue Cycle Management

Key Impact

Productivity - physicians should be able to reduce time spent by at least 20% on each report

IANN at work in Radiology

Radiologists write as many as 1 Billion reports in the US . IANN has been trained to understand radiology reports

How it Works

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NAVIGATION

WHAT WE DO

Despite the focus on Electronic Medical Records, unstructured narratives form a bulk of the data stored in health systems, for the most part these narratives remain untapped by care providers. We use Deep Learning and to understand and respond to these narratives.

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