The best example is probably Natural Language Processing (NLP). Basically, NLP is an algorithmic process that “reads” verbal information and uses probabilities to classify what it means.
It’s a technology we all use every day. Inbox filters, for example, flag emails that contain the word “money,” because NLP algorithms have found that such emails are highly likely to be spam.
Obviously in healthcare, the stakes are a little higher. A type of NLP process called sentiment analysis can tease out invaluable qualitative information from patient comments, including how they felt about their encounters. For instance, if a patient’s comments contain the word “forever,” chances are good that they have a complaint about their wait times.
The advantage of NLP, as opposed to employing humans to sort through patient comments, is that they can do it en masse, and instantly. This enables health systems to spot problematic trends much earlier than they would without NLP on their side.