eQuality for all: Extending automated quality measurement of free text clinical narratives.
BACKGROUND
Electronic quality monitoring(eQuality) from clinical narratives may advance current manual quality measurement techniques.We evaluated automated eQuality measurement tools on clinical narratives of veterans' disability examinations.


METHODS
We used a general purpose indexing engine to encode clinical concepts with SNOMED CT. We developed computer usable quality assessment rules from established quality indicators and evaluated the automated approach against a gold standard of double independent human expert review. Rules were iteratively improved using a training set of 1446 indexed exam reports and evaluated on a test set of 1454 indexed exam reports.


RESULTS
The eQuality system achieved 86%sensitivity (recall), 62% specificity, and 96%positive predictive value (precision) for automated quality assessment of veterans' disability exams. Summary data for each exam type and detailed data for joint exam quality assessments are presented.


CONCLUSIONS
The current results generalize our previous results to ten exam types covering over 200 diagnostic codes. eQuality measurement from narrative clinical documents has the potential to improve healthcare quality and safety.
Brown
Steven H
SH
Department of Veterans Affairs Nashville TN, USA.
Elkin
Peter L
PL
Rosenbloom
S Trent
ST
Fielstein
Elliot
E
Speroff
Ted
T
D004185:Disability Evaluation; D016347:Medical Records Systems, Computerized / Q000592:standards*; D033262:Narration; D009323:Natural Language Processing; D011785:Quality Assurance, Health Care / Q000379:methods*; D039061:Systematized Nomenclature of Medicine; D014481:United States