Health Services Research & Development

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2017 HSR&D/QUERI National Conference Abstract

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4009 — Electronic Algorithms Can Efficiently Detect Delays in Follow-Up of Abnormal Mammograms

Lead/Presenter: Daniel Murphy, COIN - Houston
All Authors: Murphy DR (Houston VA IQuESt & Baylor College of Medicine) Meyer AN (Houston VA IQuESt & Baylor College of Medicine) Vaghani V (Houston VA IQuESt & Baylor College of Medicine) Russo E (Houston VA IQuESt & Baylor College of Medicine) Sittig DF (U of Texas Health Science Center) Wei L (Houston VA IQuESt & Baylor College of Medicine) Wu L (Houston VA IQuESt) Singh H (Houston VA IQuESt & Baylor College of Medicine)

Objectives:
Delayed follow-up of abnormal mammogram results can lead to adverse impact, but detecting such delays is difficult and time-consuming. We developed and tested the ability of "triggers" to scan through extensive amounts of electronic health record data to efficiently identify such delays.

Methods:
Using expert input, we developed an algorithm to flag records with delays (defined as > 60 days) in follow-up of abnormal mammogram results (BIRADS 0 and 3-5) using clinical data in the VA Informatics and Computing Infrastructure (VINCI). We then manually reviewed a sample of flagged and unflagged records from VA Integrated Service Network (VISN) 12 to determine the performance characteristics of the trigger (positive and negative predictive value, sensitivity, and specificity). Reviewers also assessed reasons for lack of follow-up and whether patients were subsequently diagnosed with breast cancer within 18 months.

Results:
Of 365,686 patients seen between 1/1/2010-5/31/2015, the trigger identified 2129 patients with abnormal mammograms, of which it flagged 522 as having delays in follow-up. On review of a randomly-selected 400 of these records, 283 true delays were identified (PPV 71%; 95%CI:66-75%), including 280 records without any documented plan, and 3 patients with a plan that was not adhered to. We additionally reviewed 100 patients with abnormal mammograms, which were not flagged by the trigger and found 7 delays (NPV 93% [95%CI:86-97%]; sensitivity 77% [95%CI:73%-80%]; specificity 91% [95%CI:89%-92%]). Most delays resulted from follow-up ordered but not scheduled within 60 days. Of the 283 patients experiencing delays, 3 were diagnosed with breast cancer within 18 months of the abnormal mammography results.

Implications:
The trigger achieved promising performance characteristics that warrant further testing in actual clinical practice. During testing, the trigger was able to detect 283 patients experiencing delays, 3 of which were diagnosed with breast cancer.

Impacts:
Clinical application of a mammography-related trigger could help efficiently identify Veterans experiencing delays in the follow-up of their abnormal mammogram results.