Real-time surveillance for tuberculosis using electronic health record data from an ambulatory practice in eastern Massachusetts.

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.