Application of Electronic Algorithms to Improve Diagnostic Evaluation for Bladder Cancer.
Strategies to ensure timely diagnostic evaluation of hematuria are needed to reduce delays in bladder cancer diagnosis.



To evaluate the performance of electronic trigger algorithms to detect delays in hematuria follow-up.



We developed a computerized trigger to detect delayed follow-up action on a urinalysis result with high-grade hematuria (>50 red blood cells/high powered field). The trigger scanned clinical data within a Department of Veterans Affairs (VA) national data repository to identify all patient records with hematuria, then excluded those where follow-up was unnecessary (e.g., terminal illness) or where typical follow-up action was detected (e.g., cystoscopy). We manually reviewed a randomly-selected sample of flagged records to confirm delays. We performed a similar analysis of records with hematuria that were marked as not delayed (non-triggered). We used review findings to calculate trigger performance.



Of 310,331 patients seen between 1/1/2012-12/31/2014, the trigger identified 5,857 patients who experienced high-grade hematuria, of which 495 experienced a delay. On manual review of 400 randomly-selected triggered records and 100 non-triggered records, the trigger achieved positive and negative predictive values of 58% and 97%, respectively.



Triggers offer a promising method to detect delays in care of patients with high-grade hematuria and warrant further evaluation in clinical practice as a means to reduce delays in bladder cancer diagnosis.
Daniel R. Murphy, MD MBA, Michael E. DeBakey Veterans Affairs Medical Center (MEDVAMC), Houston Center for Innovations in Quality, Effectiveness & Safety (IQuESt) (152), 2002 Holcombe Boulevard, Houston, TX 77030 USA, 713-440-4600 (o), 713-748-7359 (f), Email: drmurphy@bcm.edu.
Daniel R
DR
Murphy
Ashley N D
AN
Meyer
Viralkumar
V
Vaghani
Elise
E
Russo
Dean F
DF
Sittig
Kyle A
KA
Richards
Li
L
Wei
Louis
L
Wu
Hardeep
H
Singh
D000368:Aged; D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D057286:Electronic Health Records; D005260:Female; D006417:Hematuria / Q000150:complications; D006801:Humans; D008297:Male; D016482:Urinalysis; D001749:Urinary Bladder Neoplasms / Q000150:complications / Q000175:diagnosis* / Q000652:urine
Electronic health records; data mining; delayed diagnosis; hematuria; medical informatics; monitoring and surveillance; triggers; urologic neoplasms