While most healthcare analytics tools only look at broad categories of cost versus outcomes, we break clinical data down into unique cohorts that account for variables in patient type, surgical technique, and other mitigating factors to make a true "apples to apples" comparison for reviewing clinical variation. We have developed more than 300 individual cohorts.
Rooted in existing cohorts we developed in collaboration with Intermountain Healthcare, our AI-driven platform identifies variance and cohorts (remove) by scanning and capturing thousands of data points from a variety of sources, including operative notes. Using Natural Language Processing (NLP) with 98% accuracy, our machine learning technology can quickly identify and pull pertinent data that would take significant labor hours if done manually. This advanced technology allows us to identify meaningful variation in cost and clinical outcomes, determine reasons behind the variation and make recommendations to physicians and administrators based on the output.
Our technology platform makes complex reporting simple, using intuitive charts, graphs and pertinent details that nurses, surgeons and administrators can all easily and quickly digest — making conversations productive, and recommendations easily actionable.