As another clinician who has done some work previously in bioinformatics and NLP I think another serious difficulty is the assumptions that are placed in parts of free text medical records. For example, my HPI may note "patient is afebrile with WBC count of 7.2, hemodynamically stable with H/H 9/32." At the surface this is just me reciting data about the patient, but my implication is that the patient is less likely to have a superimposed infection and is not actively bleeding. I generally write these terms in the assessment/plan portion of the note, but not everyone does, as it seems obvious to a physician. Every physician reading the HPI knows exactly why I asked those questions, whereas a computer can't read between the lines (at least as well/easily), so to speak. Some of these syntactic issues don't help, and most physicians won't improve their notes for NLP because it takes even more time to document when documentation requirements inflate annually as is.
BaksideAttak1 karma
As another clinician who has done some work previously in bioinformatics and NLP I think another serious difficulty is the assumptions that are placed in parts of free text medical records. For example, my HPI may note "patient is afebrile with WBC count of 7.2, hemodynamically stable with H/H 9/32." At the surface this is just me reciting data about the patient, but my implication is that the patient is less likely to have a superimposed infection and is not actively bleeding. I generally write these terms in the assessment/plan portion of the note, but not everyone does, as it seems obvious to a physician. Every physician reading the HPI knows exactly why I asked those questions, whereas a computer can't read between the lines (at least as well/easily), so to speak. Some of these syntactic issues don't help, and most physicians won't improve their notes for NLP because it takes even more time to document when documentation requirements inflate annually as is.
View HistoryShare Link