Healthcare AI: Spotlight on data collection challenges

His perspective on the quantity and quality of data that is being accumulated in the healthcare domain (specifically in EHRs) is very insightful and underlines the dynamics in our system that cause professionals to not always accurately and completely document in their systems.
As he seeks further advancements in voice recognition and voice guided systems, we at MUUTAA are developing tools to work with the limitations of the available data and seek for expert-augmented machine learning opportunities to drive efficiencies right now.
“The promise of EHRs was that they would create a wealth of actionable data that could be leveraged for better patient care. Unfortunately, this promise never fully materialized. Most of the interesting information that can be captured in the course of patient care either is not or is captured minimally or inconsistently. Often, just enough information is recorded in the EHR to support billing and is in plain text (not actionable) form. Worse, documentation requirements have had a serious impact on physicians, to whom it ultimately fell to input much of that data. Burnout and job dissatisfaction among physicians have become endemic.
EHRs didn’t create the documentation challenge. But using an EHR in the exam room can significantly detract from patient care. Speech recognition has come a long way since then, although it hasn’t changed that fundamental dynamic of the screen interaction that takes away from the patient. Indeed, using speech recognition, physicians stare at the screen even more intently as they must be mindful of mistakes that the speech recognition system may generate.”

Leave a Reply