Literature DB >> 29220208

Changing Utilization of Noninvasive Diagnostic Imaging Over 2 Decades: An Examination Family-Focused Analysis of Medicare Claims Using the Neiman Imaging Types of Service Categorization System.

David A Rosman1, Richard Duszak2, Wenyi Wang2, Danny R Hughes3,4, Andrew B Rosenkrantz5.   

Abstract

OBJECTIVE: The objective of our study was to use a new modality and body region categorization system to assess changing utilization of noninvasive diagnostic imaging in the Medicare fee-for-service population over a recent 20-year period (1994-2013).
MATERIALS AND METHODS: All Medicare Part B Physician Fee Schedule services billed between 1994 and 2013 were identified using Physician/Supplier Procedure Summary master files. Billed codes for diagnostic imaging were classified using the Neiman Imaging Types of Service (NITOS) coding system by both modality and body region. Utilization rates per 1000 beneficiaries were calculated for families of services.
RESULTS: Among all diagnostic imaging modalities, growth was greatest for MRI (+312%) and CT (+151%) and was lower for ultrasound, nuclear medicine, and radiography and fluoroscopy (range, +1% to +31%). Among body regions, service growth was greatest for brain (+126%) and spine (+74%) imaging; showed milder growth (range, +18% to +67%) for imaging of the head and neck, breast, abdomen and pelvis, and extremity; and showed slight declines (range, -2% to -7%) for cardiac and chest imaging overall. The following specific imaging service families showed massive (> +100%) growth: cardiac CT, cardiac MRI, and breast MRI.
CONCLUSION: NITOS categorization permits identification of temporal shifts in noninvasive diagnostic imaging by specific modality- and region-focused families, providing a granular understanding and reproducible analysis of global changes in imaging overall. Service family-level perspectives may help inform ongoing policy efforts to optimize imaging utilization and appropriateness.

Entities:  

Keywords:  CT; MRI; mammography; radiography; subspecialty; ultrasound; utilization

Mesh:

Year:  2017        PMID: 29220208     DOI: 10.2214/AJr17.18214

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  9 in total

1.  Factors predicting the time-length variability of identically protocoled MRI exams.

Authors:  Gregory D Avey; Daryn S Belden; Ryan D Zea; John-Paul J Yu
Journal:  J Magn Reson Imaging       Date:  2019-01-13       Impact factor: 4.813

2.  Machine Learning for Automatic Paraspinous Muscle Area and Attenuation Measures on Low-Dose Chest CT Scans.

Authors:  Ryan Barnard; Josh Tan; Brandon Roller; Caroline Chiles; Ashley A Weaver; Robert D Boutin; Stephen B Kritchevsky; Leon Lenchik
Journal:  Acad Radiol       Date:  2019-07-17       Impact factor: 3.173

3.  An Empiric Medicare Claims-Based Utilization Approach to Mitigating the Iodinated Contrast Shortage.

Authors:  Richard Duszak; Jennifer Hemingway; Eric W Christensen; Amit M Saindane; Danny R Hughes; Elizabeth Y Rula
Journal:  J Am Coll Radiol       Date:  2022-05-25       Impact factor: 6.240

4.  Fully Automated Segmentation of Head CT Neuroanatomy Using Deep Learning.

Authors:  Jason C Cai; Zeynettin Akkus; Kenneth A Philbrick; Arunnit Boonrod; Safa Hoodeshenas; Alexander D Weston; Pouria Rouzrokh; Gian Marco Conte; Atefeh Zeinoddini; David C Vogelsang; Qiao Huang; Bradley J Erickson
Journal:  Radiol Artif Intell       Date:  2020-09-30

5.  Trends in Diagnostic Imaging Utilization among Medicare and Commercially Insured Adults from 2003 through 2016.

Authors:  Arthur S Hong; David Levin; Laurence Parker; Vijay M Rao; Dennis Ross-Degnan; J Frank Wharam
Journal:  Radiology       Date:  2019-12-31       Impact factor: 29.146

6.  Characteristics of COVID-19 Community Practice Declines in Noninvasive Diagnostic Imaging Professional Work.

Authors:  Richard Duszak; Jeff Maze; Candice Sessa; Howard B Fleishon; Lauren P Golding; Gregory N Nicola; Danny R Hughes
Journal:  J Am Coll Radiol       Date:  2020-07-03       Impact factor: 5.532

7.  Deep Learning to Assess Long-term Mortality From Chest Radiographs.

Authors:  Michael T Lu; Alexander Ivanov; Thomas Mayrhofer; Ahmed Hosny; Hugo J W L Aerts; Udo Hoffmann
Journal:  JAMA Netw Open       Date:  2019-07-03

8.  An Important and Often Ignored Turnaround Time in Radiology - Clinician Turnaround Time: Implications for Musculoskeletal Radiology.

Authors:  Michael Mayer; Ronnie Sebro
Journal:  J Belg Soc Radiol       Date:  2019-08-14       Impact factor: 1.894

9.  Time trends in incidence and prevalence of chronic pancreatitis: A 25-year population-based nationwide study.

Authors:  Søren S Olesen; Laust H Mortensen; Elisabeth Zinck; Ulrik Becker; Asbjørn M Drewes; Camilla Nøjgaard; Srdan Novovic; Dhiraj Yadav; Janne S Tolstrup
Journal:  United European Gastroenterol J       Date:  2021-02-22       Impact factor: 4.623

  9 in total

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