Algorithm Developed To Identify Delayed Notifications on Abnormal Mammograms

An AHRQ-funded research team developed an algorithm that identified patients who received delayed notifications about abnormal mammograms. The research team used 2010–2015 electronic health record data from about 365,000 patients treated in a network of seven health care facilities. Of 2,129 patients with abnormal mammograms, 552 experienced delayed notifications. The researchers estimated their algorithm would identify 72 patients experiencing notification delays each year across the seven facilities. Access the abstract for the study, which was published in the Journal of The American College of Radiology.

2017 Nov 1. pii: S1546-1440(17)31272-3. doi: 10.1016/j.jacr.2017.10.001. [Epub ahead of print]

Electronic Triggers to Identify Delays in Follow-Up of Mammography: Harnessing the Power of Big Data in Health Care.

Murphy DR1, Meyer AND2, Vaghani V2, Russo E2, Sittig DF3, Wei L2, Wu L4, Singh H2.

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Abstract

PURPOSE:

We previously developed electronic triggers to automatically flag records for patients experiencing potential delays in diagnostic evaluation for certain cancers. Because of the unique clinical, logistic, and legal aspects of mammography, this study was conducted to evaluate the effectiveness of a trigger to flag delayed follow-up on mammography.

METHODS:

An algorithm was developed to detect delays in follow-up of abnormal mammographic results (>60 days for BI-RADS® 0, 4, and 5 and >7 months for BI-RADS 3) using clinical data in the electronic health record. Flagged records were then manually reviewed to determine the trigger's performance characteristics (positive and negative predictive value, sensitivity, and specificity). The frequency of delays and patient communication related to abnormal results, reasons for lack of follow-up, and whether patients were subsequently diagnosed with breast cancer were also assessed.

RESULTS:

Of 365,686 patients seen between January 1, 2010, and May 31, 2015, the trigger identified 2,129 patients with abnormal findings on mammography, of whom it flagged 552 as having delays in follow-up. From these, review of 400 randomly selected records revealed 283 true delays (positive predictive value, 71%; 95% confidence interval, 66%-75%), including 280 records without any documented plan and three patients with plans that were not adhered to. Transcription and reporting inconsistencies were identified in 27% of externally performed mammographic reports. Only 335 records (84%) contained specific documentation that the patient was informed of the abnormal result.

CONCLUSIONS:

Care delays appear to continue despite federal laws requiring patient notification of mammographic results within 30 days. Clinical application of mammography-related triggers could help detect these delays.

Published by Elsevier Inc.