Natural Language Processing: We used Azure Machine Learning services to quickly train the model using a sample size of over 5,000 medical dictations. Through this exercise that was monitored closely for 100% accuracy in diagnosis, our model was able to scale quickly and expand effectively to review and identify critical cases on a much broader sample in a very short period of time. In order to reduce risks and situations where patient cases get identified wrongly as non-critical, the model was trained to be very conservative while drawing conclusions.
The model also allowed for quick deployment into a production environment.
Artificial Intelligence: The fully developed model now works with greater than 95% accuracy and uses our Artificial Intelligence solution to advise healthcare practitioners about the urgency associated with each case. Our solution has eliminated the need for additional time being spent by the already overburdened healthcare staff.