Development and Validation of Trigger Algorithms to Identify Delays in Diagnostic Evaluation of Gastroenterological Cancer

Abstract

Background & aims: Colorectal cancer (CRC) and hepatocellular cancer (HCC) are common causes of death and morbidity, and patients benefit from early detection. However, delays in follow-up of suspicious findings are common, and methods to efficiently detect such delays are needed. We developed, refined, and tested trigger algorithms that identify patients with delayed follow-up evaluation of findings suspicious of CRC or HCC.

Methods: We developed and validated two trigger algorithms that detect delays in diagnostic evaluation of CRC and HCC using laboratory, diagnosis, procedure, and referral codes from the Department of Veteran Affairs National Corporate Data Warehouse. The algorithm initially identified patients with positive test results for iron deficiency anemia or fecal immunochemical test (for CRC) and elevated α-fetoprotein results (for HCC). Our algorithm then excluded patients for whom follow-up evaluation was unnecessary, such as patients with a terminal illness or those who had already completed a follow-up evaluation within 60 days. Clinicians reviewed samples of both delayed and nondelayed records, and review data were used to calculate trigger performance.

Results: We applied the algorithm for CRC to 245,158 patients seen from January 1, 2013, through December 31, 2013 and identified 1073 patients with delayed follow up. In a review of 400 randomly selected records, we found that our algorithm identified patients with delayed follow-up with a positive predictive value of 56.0% (95% CI, 51.0%-61.0%). We applied the algorithm for HCC to 333,828 patients seen from January 1, 2011 through December 31, 2014, and identified 130 patients with delayed follow-up. During manual review of all 130 records, we found that our algorithm identified patients with delayed follow-up with a positive predictive value of 82.3% (95% CI, 74.4%-88.2%). When we extrapolated the findings to all patients with abnormal results, the algorithm identified patients with delayed follow-up evaluation for CRC with 68.6% sensitivity (95% CI, 65.4%-71.6%) and 81.1% specificity (95% CI, 79.5%-82.6%); it identified patients with delayed follow-up evaluation for HCC with 89.1% sensitivity (95% CI, 81.8%-93.8%) and 96.5% specificity (95% CI, 94.8%-97.7%). Compared to nonselective methods, use of the algorithm reduced the number of records required for review to identify a delay by more than 99%.

Conclusions: Using data from the Veterans Affairs electronic health record database, we developed an algorithm that greatly reduces the number of record reviews necessary to identify delays in follow-up evaluations for patients with suspected CRC or HCC. This approach offers a more efficient method to identify delayed diagnostic evaluation of gastroenterological cancers.

Keywords: Diagnostic Delay; Electronic Health Records; Health Information Technology; Medical Informatics; Primary Care.