The potential biases of evidence-based medicine can be overcome with data-driven medicine, now that we are amassing large data sets from which AI algorithms can learn and discover.
Many kinds of cognitive biases can cloud physicians medical judgement. With the emergence of Artificial Intelligence in healthcare, recommendations can emerge that help overcome these biases. In order for AI to be truly useful, however, access to large aggregated data sets needs to occur. We are seeing the beginning of this now.
Next generation EHRs will be fundamentally different. The data will be external, shared and universal. The system will be broken into functional pieces. Chart note creation will be automated, and will use multiple inputs combining graphical, text, and voice over multiple devices. AI will be integrated to deliver clinical decision support.
Increasingly, healthcare has come to recognize that aggregated, universal health data is needed. Efforts at population health management, as well as research into underlying patterns of disease and health, rely on aggregated data. Numerous ventures in this direction are now emerging. It is important to understand where we are in this evolution, and what kinds of future technologies can emerge once we get there.
Healthcare is a vast, intensely complex ecosystem. In order to make sense off all the variety found in the sector, we can categorize health IT efforts into two segments: organizing the world's medical information, and making the data universally useful and available.