Supporting providers by accelerating point of care decision-making is our goal.
At HITEKS, we developed a unique approach to NLP pattern recognition, which applies an algorithm to our Lexicon and finds hits in clinical documentation in subseconds (20-200 milliseconds, depending on the size of the document), allowing us to compete more effectively by being 100 times faster than our competitors at producing physician CDI and quality suggestions along with the detailed patient evidence supporting the advice.
This discussion has been open for a while now: we want Artificial Intelligence (AI) to be driven automatically through Machine Learning (ML) so that tomorrow’s clinical software is smarter. So how do we build, implement and train the ML algorithms?
Clinical algorithms are built with logistic regression and neural network models that are exposed to both pre- and post-processed datasets, which are then trained, tested and enhanced over time as they learn what the ground truth is.