Putting my Emergency Informatics hat back on for a day, I’d like to highlight another piece of work that brings us, yet again, another step closer to being replaced by computers.
Or, at the minimum, being highly augmented by computers.
There are multitudinous clinical decision instruments available to supplement physician decision-making. However, the general unifying element of most instruments is the necessary requirement of physician input. This interruption of clinical flow reduces acceptability of use, and impedes knowledge translation through the use of these tools.
However, since most clinicians are utilizing Electronic Health Records, we’re already entering the information required for most decision instruments into the patient record. Usually, this is a combination of structured (click click click) and unstructured (type type type) data. Structured data is easy for clinical calculators to work with, but has none of the richness communicated by freely typed narrative. Therefore, clinicians much prefer to utilize typed narrative, at the expense of EHR data quality.
This small experiment out of Cincinnati implemented a natural-language processing and machine-learning automated method to collect information from the EHR. Structured and unstructured data from 2,100 pediatric patients with abdominal pain were analyzed to extract the elements to calculate the Pediatric Appendicitis Score. Appropriateness of the Pediatric Appendicitis Score aside, their method performed reasonably well. It picked up about 87% of the elements of the Score from the record, and was correct when doing so about 86%, as well. However, this was performed retrospectively – and the authors state this processing would still be substantially delayed by hours following the initial encounter.
So, we’re not quite yet at the point where a parallel process monitors system input and provides real-time diagnostic guidance – but, clearly, this is a window into the future. The theory: if an automated process could extract the data required to calculate the score, physicians might be more likely to integrate the score into their practice – and thusly lead to higher quality care through more accurate risk-stratification.
I, for one, welcome our new computer overlords.
“Developing and evaluating an automated appendicitis risk stratification algorithm for pediatric patients in the emergency department”