OBJECTIVE
Electronic health records (EHRs) have the potential to improve completeness and timeliness of tuberculosis (TB) surveillance relative to traditional reporting, particularly for culture-negative disease. We report on the development and validation of a TB detection algorithm for EHR data followed by implementation in a live surveillance and reporting system.
METHODS
We used structured electronic data from an ambulatory practice in eastern Massachusetts to develop a screening algorithm aimed at achieving 100% sensitivity for confirmed active TB with the highest possible positive predictive value (PPV) for physician-suspected disease. We validated the algorithm in 16 years of retrospective electronic data and then implemented it in a real-time EHR-based surveillance system. We assessed PPV and the completeness of case capture relative to conventional reporting in 18 months of prospective surveillance.
RESULTS
The final algorithm required a prescription for pyrazinamide, an International Classification of Diseases, Ninth Revision (ICD-9) code for TB and prescriptions for two antituberculous medications, or an ICD-9 code for TB and an order for a TB diagnostic test. During validation, this algorithm had a PPV of 84% (95% confidence interval 78, 88) for physician-suspected disease. One-third of confirmed cases were culture-negative. All false-positives were instances of latent TB. In 18 months of prospective EHR-based surveillance with this algorithm, seven additional cases of physician-suspected active TB were detected, including two patients with culture-negative disease. A review of state health department records revealed no cases missed by the algorithm.
CONCLUSIONS
Live, prospective TB surveillance using EHR data is feasible and promising.