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Electronic health record-based triggers to detect potential delays in cancer diagnosis
  1. Daniel R Murphy1,2,
  2. Archana Laxmisan1,2,
  3. Brian A Reis1,2,
  4. Eric J Thomas3,
  5. Adol Esquivel4,
  6. Samuel N Forjuoh5,
  7. Rohan Parikh6,
  8. Myrna M Khan1,2,
  9. Hardeep Singh1,2
  1. 1Houston VA Health Services Research & Development Center of Excellence, and The Center of Inquiry to Improve Outpatient Safety Through Effective Electronic Communication, both at the Michael E. DeBakey Veterans Affairs Medical Center and the Section of Health Services Research and Development, Houston, Texas, USA
  2. 2Baylor College of Medicine, Department of Medicine, Houston, Texas, USA
  3. 3University of Texas Houston Medical School, Department of Internal Medicine; and the UT Houston-Memorial Hermann Center for Healthcare Quality & Safety, Houston, Texas, USA
  4. 4Department of Clinical Effectiveness and Performance Measurement, St. Luke's Health System, Houston, Texas, USA
  5. 5Department of Family and Community Medicine, Scott & White Healthcare, Texas A&M Health Science Center, College of Medicine, Temple, Texas, USA
  6. 6University of Texas School of Public Health, Houston, Texas, USA.
  1. Correspondence to Dr Daniel R Murphy, Michael E. DeBakey Veterans Affairs Medical Center (MEDVAMC), HSR&D Center of Excellence (152), 2002 Holcombe Boulevard, Houston, TX 77030 USA; drmurphy{at}


Background Delayed diagnosis of cancer can lead to patient harm, and strategies are needed to proactively and efficiently detect such delays in care. We aimed to develop and evaluate ‘trigger’ algorithms to electronically flag medical records of patients with potential delays in prostate and colorectal cancer (CRC) diagnosis.

Methods We mined retrospective data from two large integrated health systems with comprehensive electronic health records (EHR) to iteratively develop triggers. Data mining algorithms identified all patient records with specific demographics and a lack of appropriate and timely follow-up actions on four diagnostic clues that were newly documented in the EHR: abnormal prostate-specific antigen (PSA), positive faecal occult blood test (FOBT), iron-deficiency anaemia (IDA), and haematochezia. Triggers subsequently excluded patients not needing follow-up (eg, terminal illness) or who had already received appropriate and timely care. Each of the four final triggers was applied to a test cohort, and chart reviews of randomly selected records identified by the triggers were used to calculate positive predictive values (PPV).

Results The PSA trigger was applied to records of 292 587 patients seen between 1 January 2009 and 31 December 2009, and the CRC triggers were applied to 291 773 patients seen between 1 March 2009 and 28 February 2010. Overall, 1564 trigger positive patients were identified (426 PSA, 355 FOBT, 610 IDA and 173 haematochezia). Record reviews revealed PPVs of 70.2%, 66.7%, 67.5%, and 58.3% for the PSA, FOBT, IDA and haematochezia triggers, respectively. Use of all four triggers at the study sites could detect an estimated 1048 instances of delayed or missed follow-up of abnormal findings annually and 47 high-grade cancers.

Conclusions EHR-based triggers can be used successfully to flag patient records lacking follow-up of abnormal clinical findings suspicious for cancer.

  • Chart review methodologies
  • Information technology
  • Patient safety
  • Primary care
  • Trigger tools

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