Artificial Intelligence (AI) is ripe for development and investment for health information management (HIM). The major initiatives towards Interoperability and AI is just beginning in the US. The National Institute of Health has invested $1.5B into the All of Us Research Program, which invites participants across the country to share their biology, lifestyle and environment. Aggregated data sources like this one promise to enable AI technologies to improve diagnostic accuracy, clinical and operational efficiency and the overall patient experience.1 While these innovations are promising, they are also in a preliminary phase for HIM applications from an accuracy and reliability or practical use perspective.

Coming into the new year, we've seen and heard plenty of buzz around AI. But the underlying data is often overlooked; it's a key foundational piece that impacts AI at scale. Therefore, the proverbial data statement, 'garbage in, garbage out,' and the implications of bad data quality, is arguably the most understated AI trend. "Successful models depend heavily on rich, deep client data. Many B2B marketers put themselves at a disadvantage by trying to make predictive work on a limited set of data or data that is just plain wrong."