Observational studyRadiology2020
Trends in Diagnostic Imaging Utilization among Medicare and Commercially Insured Adults from 2003 through 2016.
Observational study in Radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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
20 citing papers in PubMed.
- Recent advances in artificial intelligence for radiology report generation: a brief review.BJR artificial intelligence · 2026Review
- Explainable self-supervised learning for medical image diagnosis based on DINO V2 model and semantic search.Scientific reports · 2025Article
- Inpatient Imaging Utilization and Radiology Workload: Trends of the Past Decade and Through the COVID-19 Pandemic.Journal of the American College of Radiology : JACR · 2025Article
- Simulated radiation levels and patterns of MRI without a Faraday shielded room.Magnetic resonance in medicine · 2025Article
- Article
- Trends in CT use in an emergency department in Western Australia: 2015-2022.Insights into imaging · 2025Article
- Article
- Assessment of the Diagnostic Efficacy of Low-Field Magnetic Resonance Imaging: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Initial Imaging of Pregnant Patients in the Trauma Bay-Discussion and Review of Presentations at a Level-1 Trauma Centre.Diagnostics (Basel, Switzerland) · 2024Review
- Evaluating artificial intelligence for comparative radiography.International journal of legal medicine · 2024Article
- Patient, Provider, and Practice Characteristics Predicting Use of Diagnostic Imaging in Primary Care: Cross-Sectional Data From the National Ambulatory Medical Care Survey.Journal of the American College of Radiology : JACR · 2023Article
- Self-supervised pre-training with contrastive and masked autoencoder methods for dealing with small datasets in deep learning for medical imaging.Scientific reports · 2023Article
- SynthA1c: Towards Clinically Interpretable Patient Representations for Diabetes Risk Stratification.Predictive Intelligence in Medicine. PRIME (Workshop) · 2023Article
- Self-supervised learning for medical image classification: a systematic review and implementation guidelines.NPJ digital medicine · 2023Review
- Application of symmetry evaluation to deep learning algorithm in detection of mastoiditis on mastoid radiographs.Scientific reports · 2023Article
- Low-field MRI: Clinical promise and challenges.Journal of magnetic resonance imaging : JMRI · 2023Review
- Updated Trends, Disparities, and Clinical Impact of Neuroimaging Utilization in Ischemic Stroke in the Medicare Population: 2012 to 2019.Journal of the American College of Radiology : JACR · 2022Article
- Assessing and coping with the financial burden of computed tomography utilization in Limbe, Cameroon: a sequential explanatory mixed-methods study.BMC health services research · 2020Article
- Performance of deep learning to detect mastoiditis using multiple conventional radiographs of mastoid.PloS one · 2020Article
- Google Trends Data of Radiologists Who Accept Medicare: A Potential Tool for Predicting State Demand.Current problems in diagnostic radiologyArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Background Trends in noninvasive diagnostic imaging (NDI) utilization rates have predominantly been reported in Medicare enrollees. To the authors' knowledge, there has been no prior direct comparison of utilization rates between Medicare and commercially insured patients. Purpose To analyze trends in NDI utilization rates by modality, comparing Medicare fee-for-service and commercially insured enrollees. Materials and Methods This study was a retrospective trend analysis of NDI performed between 2003 and 2016 as reported in claims databases for all adults enrolled in fee-for-service Medicare and for roughly 9 million commercially insured patients per year. The commercially insured patients were divided into two populations: those aged 18-44 years and those aged 45-64 years. The same procedure code definitions for NDI were applied to both Medicare and commercial claims, rates were calculated per 1000 enrollees, and trends were reported over time in aggregate followed by modality (CT, MRI, nuclear imaging, echocardiography, US, radiography). Join-point regression was used to model annual rates and to identify statistically significant (
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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.