Evidence map›Paper›PMID 40381875›Full record

ArticleJournal of the American College of Radiology : JACR2025

Inpatient Imaging Utilization and Radiology Workload: Trends of the Past Decade and Through the COVID-19 Pandemic.

Tina Shiang, Edwin Thai, Richard Hanson, Ronilda Lacson, Sanjay Saini, Catherine S Giess, David W Bates, Ramin Khorasani

Abstract read
In one paragraph

Article in Journal of the American College of Radiology : JACR, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Review
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  9. Article
  10. The Effect of AI on the Radiologist Workforce: A Task-Based Analysis.medRxiv : the preprint server for health sciences · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Tina ShiangCenter for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts. Electronic address: tshiang@bwh.harvard.edu.
Edwin ThaiDana-Farber Cancer Institute, Boston, Massachusetts.
Richard HansonCenter for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Assistant Director for Integration and Knowledge Management, Brigham and Women's Hospital.
Ronilda LacsonCenter for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Faculty and Associate Director, Center for Evidence-Based Imaging, Brigham and Women's Hospital.
Sanjay SainiDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; Vice Chair, Radiology Technical Practice Integration, Mass General Brigham.
Catherine S GiessCenter for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Deputy Chair of Radiology, Brigham and Women's Hospital.
David W BatesChief Innovation Officer, Chief of Division of General Internal Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Department of Health Policy and Management, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
Ramin KhorasaniDirector, Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Vice Chair, Radiology Quality and Safety, Mass General Brigham.

Funding

AHRQ HHS R18 HS028616
6 · The paper itself

Abstract

purposeThe aim of this study was to assess inpatient radiology imaging utilization and workload trends at a large academic medical center from fiscal year (FY) 2013 to FY 2023.

methodsThis retrospective study included 896,005 imaging examinations from adult inpatients treated from October 1, 2012, to September 30, 2023 (FY 2013 to FY 2023) at an 866-bed tertiary care academic institution. Examinations were coded by modality-radiography (XR), CT, MRI, ultrasound, and nuclear medicine (NM)-and adjusted for disease severity using case-mix index. Relative value units (RVUs) were used to measure radiology workload. Linear regression analysis and the χ

resultsAlthough total number of annual imaging examinations increased from 102,025 to 118,991 during the study period (P < .001), total imaging examinations per CMAA decreased (P < .001) because of decreases in XR, ultrasound, and NM examinations (P < .001 for all). However, radiology workload (professional, technical, and global) increased significantly (P < .001), attributable mainly to increased CT and MRI utilization (P < .001). Although CT and MRI examinations per CMAA increased by 19.2% and 21.4% respectively, professional RVUs increased by 80.6% for CT (P < .001) and by 90.7% for MRI (P < .001). Average length of stay increased by 32.1% from 5.6 to 7.4 days (P < .001) during the study period.

conclusionsDespite an overall decrease in disease severity-adjusted inpatient imaging utilization, inpatient radiology workload increased significantly over the decade as advanced imaging replaced lower cost modalities. Greater reliance on CT and MRI, possibly influenced by coronavirus disease of 2019 practice changes and not fully explained by disease severity, was associated with longer inpatient stays.

Indexed as

COVID-19Diagnostic ImagingRadiology Department, HospitalWorkloadAcademic Medical CentersAdultFemaleHumansInpatientsMaleMiddle AgedPandemicsRetrospective StudiesSARS-CoV-2COVID-19 pandemicImaging utilizationinpatientradiology workloadrelative value unit

Identifiers

PMID40381875
PMCPMC12790261

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Registered trials

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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.