Electronic Triggers to Identify Delays in Follow-Up of Mammography: Harnessing the Power of Big Data in Health Care
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.
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A sample size of 30 participants, each reviewing 35 test results, was required to achieve 80% power to detect a statistically significant difference in cognitive workload and performance.
“On the basis of the anticipated rate of appropriately identified abnormal test results in the literature12,13,14,15,16,17,18,19,20,21 and the anticipated magnitude of the association of the enhanced EHR, we required a sample size of 30 participants, each reviewing 35 test results, to achieve 80% power to detect a statistically significant difference in cognitive workload and performance.”
From Association of the Usability of Electronic Health Records With Cognitive Workload and Performance Levels Among Physicians (2019) · cites this paper as [18]
Even in the EHR environment, with alerts and reminders in place, physicians continue to often inappropriately manage abnormal test results.
“Even in the EHR environment, with alerts and reminders in place, physicians continue to often inappropriately manage abnormal test results.12,13,14,15,16,17,18,19,20,21”
From Association of the Usability of Electronic Health Records With Cognitive Workload and Performance Levels Among Physicians (2019) · cites this paper as [18]
Cites
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Self-citationCommunication outcomes of critical imaging results in a computerized notification system
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Self-citationManagement of Test Results in Family Medicine Offices
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Mazur, L. M., Mosaly, P. R., Moore, C., & Marks, L. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.1709 · Cited by 127 · see citing works
Rahman Jabin, M. S., & Hammar, T. (2022) · DIGITAL HEALTH · doi:10.1177/20552076221131139 · Cited by 32 · see citing works
- Authors
- Murphy, D. R., Meyer, A. N. D., Vaghani, V., Russo, E., Sittig, D. F., Wei, L., Wu, L., & Singh, H.
- Affiliations
- Michael E. DeBakey VA Medical Center (US)
- Memorial Hermann (US)
- Corresponding author
- Murphy, Daniel R.
- ORCID
- Venue
- Journal of the American College of Radiology
- Publisher
- Elsevier BV
- Published
- 2018-02
- Volume
- 15
- Issue
- 2
- Pages
- 287-295
- ISSN
- 1546-1440 (ISSN-L)
- OpenAlex
- W2765766150
- Cited by
- 30
- References
- 3
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 2 · 2023: 3 · 2022: 5 · 2021: 3 · 2020: 4 · 2019: 5 · 2018: 3
- FWCI
- 4.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American College of Radiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Veterans Affairs
- Agency for Healthcare Research and Quality (K08HS022901)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Oncology › Global Cancer Incidence and Screening
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- late follow-up, mammography, BI-RADS 3, breast cancer screening, electronic health records, clinical decision support, patient notification, follow-up adherence
- MeSH terms
- Big Data, Algorithms, Breast Neoplasms, Continuity of Patient Care, Female, Humans, Mammography, Medical Informatics Applications, Predictive Value of Tests, Sensitivity and Specificity, Reminder Systems, Electronic Health Records
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