The problem is that doctors “continue to be trained to and socialized to think in stories” but electronic documentation in EHRs – or so it is assumed – require that they be “glorified (or not) data entry clerks” checking off boxes and providing codes for billing and other data elements for quality metrics. (Indeed both the ACP and AMIA have both highlighted this as an urgent issue. See our recent blog post “Human Digestible Documentation Tops AMIA HER 2020 Task Force Recommendations.”)
The opportunity is vast: use natural language processing (NLP) to mine the information in the electronic health record to uncover evidence and risk factors that protect the health of individuals and populations. He cites a recent Stanford study linking the use of proton pump inhibitors to heart attack, made possible only by use of NLP to scour 16 million records.
Think about a world in which a patient’s electronic notes, going back many years, can be mined for key risk factors or other historical elements, without the need to constrain the search to structured data fields like prescription lists or billing records. (Conflict alert: I’m an advisor to a company, QPID Health, which builds such a tool.)
We are happy to be part of the solution with our NLP-based clinical reasoning platform.