Towards automatic diabetes case detection and ABCS protocol compliance assessment.
OBJECTIVE
According to the American Diabetes Association, the implementation of the standards of care for diabetes has been suboptimal in most clinical settings. Diabetes is a disease that had a total estimated cost of $174 billion in 2007 for an estimated diabetes-affected population of 17.5 million in the United States. With the advent of electronic medical records (EMR), tools to analyze data residing in the EMR for healthcare surveillance can help reduce the burdens experienced today. This study was primarily designed to evaluate the efficacy of employing clinical natural language processing to analyze discharge summaries for evidence indicating a presence of diabetes, as well as to assess diabetes protocol compliance and high risk factors.


METHODS
Three sets of algorithms were developed to analyze discharge summaries for: (1) identification of diabetes, (2) protocol compliance, and (3) identification of high risk factors. The algorithms utilize a common natural language processing framework that extracts relevant discourse evidence from the medical text. Evidence utilized in one or more of the algorithms include assertion of the disease and associated findings in medical text, as well as numerical clinical measurements and prescribed medications.


RESULTS
The diabetes classifier was successful at classifying reports for the presence and absence of diabetes. Evaluated against 444 discharge summaries, the classifier's performance included macro and micro F-scores of 0.9698 and 0.9865, respectively. Furthermore, the protocol compliance and high risk factor classifiers showed promising results, with most F-measures exceeding 0.9.


CONCLUSIONS
The presented approach accurately identified diabetes in medical discharge summaries and showed promise with regards to assessment of protocol compliance and high risk factors. Utilizing free-text analytic techniques on medical text can complement clinical-public health decision support by identifying cases and high risk factors.
Centers for Disease Control and Prevention, 1600 Clifton Rd, Mail Stop E76, Atlanta, GA 30333, USA. nmishra@cdc.gov
Ninad K
NK
Mishra
Roderick Y
RY
Son
James J
JJ
Arnzen
D000465:Algorithms; D057225:Data Mining; D003920:Diabetes Mellitus / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted; D005260:Female; D019983:Guideline Adherence; D006801:Humans; D008297:Male; D016347:Medical Records Systems, Computerized; D012307:Risk Factors