ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2017
Improving Quality of Follow-Up Imaging Recommendations in Radiology.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of natural language processing applied to radiology reports.BMC medical informatics and decision making · 2021Pooled it
- Automated identification of incidentalomas requiring follow-up: A multi-anatomy evaluation of LLM-based and supervised approaches.Journal of biomedical informatics · 2026Article
- Use of ChatGPT Large Language Models to Extract Details of Recommendations for Additional Imaging From Free-Text Impressions of Radiology Reports.AJR. American journal of roentgenology · 2025Article
- Development and External Validation of an Artificial Intelligence Model for Identifying Radiology Reports Containing Recommendations for Additional Imaging.AJR. American journal of roentgenology · 2023Article
- The FIND Program: Improving Follow-up of Incidental Imaging Findings.Journal of digital imaging · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Failure of timely follow-up imaging recommendations can result in suboptimal patient care. Evidence suggests that the use of conditional language in follow-up recommendations is associated with changes to follow-up compliance. Assuming that referring physicians prefer explicit guidance for follow-up recommendations, we develop algorithms to extract recommended modality and interval from follow-up imaging recommendations related to lung, thyroid and adrenal findings. Using a production dataset of 417,451 radiology reports, we observed that on average, follow-up interval was not mentioned in 79.4% of reports, and modality was missing in 47.4% of reports (4,819 reports contained a follow-up imaging recommendation for one of the three findings). We also developed an interactive dashboard to be used to monitor compliance rates. Recognizing the importance of increasing precision of follow-up recommendations, a quality improvement pilot study is underway with the goal of achieving a target where follow-up modality and interval are both explicitly specified.
Indexed as
Identifiers
29854188PMC5977608What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.