AUTOMATED TUBERCULOSIS DETECTION

Objective: To measure the accuracy of automated tuberculosis case dete ction. Setting: An inner-city medical center. Intervention: An electro nic medical record and a clinical event monitor with a natural languag e processor were used to detect tuberculosis cases according to Center s for Disease Control criteria. Measurement: Cases identified by the a utomated system were compared to the local health department's tubercu losis registry, and positive predictive value and sensitivity were cal culated. Results: The best automated rule was based on tuberculosis cu ltures; it had a sensitivity of .89 (95% CI .75-.96) and a positive pr edictive value of .96 (.89-.99). All other rules had a positive predic tive value less than .20. A rule based on chest radiographs had a sens itivity of .41 (.26-.57) and a positive predictive value of .03 (.02-. 05), and a rule that represented the overall Centers for Disease Contr ol criteria had a sensitivity of .91 (.78-.97) and a positive predicti ve value of .15 (.12-.18). The culture-based rule was the most useful rule for automated case reporting to the health department, and the ch est radiograph-based rule was the most useful rule for improving tuber culosis respiratory isolation compliance. Conclusions: Automated tuber culosis case detection is feasible and useful, although the predictive value of most of the clinical rules was low. The usefulness of an ind ividual rule depends on the context in which it is used. The major cha llenge facing automated detection is the availability and accuracy of electronic clinical data.