There is no doubt that over the past several years, speech-to-text technology has not only greatly improved but become part of our everyday lives. Just look around and notice the number of people who routinely rely on virtual assistants like Siri and Alexa to go about their daily lives. In the healthcare IT space, ambient technology is the latest iteration of this trend. This new technology holds the promise to finally free clinicians from the shackles of being tied to keyboards while trying to maintain meaningful interactions with their patients.
While it is important to acknowledge the significant achievements of these ambient technology companies, we must also recognize their limitations. In my opinion, these technologies provide clinicians with an efficient method of capturing a clinical note. However, while the process may prove more efficient, the output remains essentially the same as the notes that doctors have been generating through dictation for decades.
The true paradigm shift will occur only when these applications are able to intelligently understand that which is being dictated. Only then will this data become actionable. This is not to minimize these achievements to date, but rather to set a more ambitious goal than merely improving dictation.
The true paradigm shift will occur only when these applications are able to intelligently understand that which is being dictated. Only then will this data become actionable.
Here at IMO, we have a suite of large language models and knowledge graphs that are uniquely positioned to advance ambient AI with rich clinical terminology, a deep understanding of documentation workflows, and award-winning natural language processing (NLP) models.
This emergence of accurate speech-to-text technology combined with IMO’s ability to extract and structure meaningful concepts has the potential to truly transform the way in which we capture clinical data. The result will equip clinicians with a new set of powerful tools, greatly enhancing the quality and efficiency of doctor-patient encounters.
