Literature DB >> 31891320

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

Arthur S Hong1, David Levin1, Laurence Parker1, Vijay M Rao1, Dennis Ross-Degnan1, J Frank Wharam1.   

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 (P < .05) changes in trends. Results In almost all instances, Medicare enrollees had the highest utilization rate for each modality, followed by commercially insured patients aged 45-64 years, then aged 18-44 years. All three populations showed utilization growth through the mid to late 2000s (images per 1000 enrollees per year for Medicare: 91 [95% confidence interval {CI}: 34, 148]; commercially insured patients aged 45-64 years: 158 [95% CI: 130, 186]; aged 18-44 years: 83 [95% CI: 69, 97]), followed by significant declining trends from the late 2000s through early 2010s (images per 1000 enrollees per year for Medicare: -301 [95% CI: -510, -92]; commercially insured patients aged 45-64 years: -54 [95% CI: -69, -39]; aged 18-44 years: -26 [95% CI: -31, -21]) coinciding with code-bundling events instituted by Medicare (CT, nuclear imaging, echocardiography). There were significant trend changes in modalities without code bundling (MRI, radiography, US), although flat trends mostly were exhibited. After the early 2010s, there were significant trend changes largely showing flat utilization growth. The notable exception was a significant trend change to renewed growth of CT imaging among commercially insured patients aged 45-64 years and Medicare enrollees after 2012, although at half the prior rate (images per 1000 enrollees per year for Medicare: 17 [95% CI: 6, 28]; commercially insured patients aged 45-64 years: 11 [95% CI: 2, 20]). Conclusion Noninvasive diagnostic imaging utilization trends among commercially insured individuals are similar to those in Medicare enrollees, although at lower rates. Earlier rapid growth has ceased and, except for CT, utilization has stabilized since the early 2010s. © RSNA, 2019 See also the editorial by Hentel and Wolk in this issue.

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Year:  2019        PMID: 31891320      PMCID: PMC6996668          DOI: 10.1148/radiol.2019191116

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   29.146


  20 in total

1.  Permutation tests for joinpoint regression with applications to cancer rates.

Authors:  H J Kim; M P Fay; E J Feuer; D N Midthune
Journal:  Stat Med       Date:  2000-02-15       Impact factor: 2.373

2.  Health insurers and medical-imaging policy--a work in progress.

Authors:  John K Iglehart
Journal:  N Engl J Med       Date:  2009-03-05       Impact factor: 91.245

3.  Trends in the utilization of medical imaging from 2003 to 2011: clinical encounters offer a complementary patient-centered focus.

Authors:  Martey S Dodoo; Richard Duszak; Danny R Hughes
Journal:  J Am Coll Radiol       Date:  2013-07       Impact factor: 5.532

4.  New codes from a new source: the rolling five-year review.

Authors:  Ezequiel Silva
Journal:  J Am Coll Radiol       Date:  2010-01       Impact factor: 5.532

5.  After Nearly A Decade Of Rapid Growth, Use And Complexity Of Imaging Declined, 2008-14.

Authors:  David C Levin; Laurence Parker; Charles D Palit; Vijay M Rao
Journal:  Health Aff (Millwood)       Date:  2017-04-01       Impact factor: 6.301

6.  How to Survive a High-Deductible Health Plan. Is your insurance giving you a bad case of sticker shock? Here are ways to ease the pain.

Authors:  Donna Rosato
Journal:  Consum Rep       Date:  2017-01

7.  The sharp reductions in medicare payments for noninvasive diagnostic imaging in recent years: will they satisfy the federal policymakers?

Authors:  David C Levin; Vijay M Rao; Laurence Parker; Andrea J Frangos
Journal:  J Am Coll Radiol       Date:  2012-09       Impact factor: 5.532

8.  Financial impact of Medicare code bundling of CT of the abdomen and pelvis.

Authors:  David C Levin; Vijay M Rao; Laurence Parker
Journal:  AJR Am J Roentgenol       Date:  2014-05       Impact factor: 3.959

9.  Use of diagnostic imaging studies and associated radiation exposure for patients enrolled in large integrated health care systems, 1996-2010.

Authors:  Rebecca Smith-Bindman; Diana L Miglioretti; Eric Johnson; Choonsik Lee; Heather Spencer Feigelson; Michael Flynn; Robert T Greenlee; Randell L Kruger; Mark C Hornbrook; Douglas Roblin; Leif I Solberg; Nicholas Vanneman; Sheila Weinmann; Andrew E Williams
Journal:  JAMA       Date:  2012-06-13       Impact factor: 56.272

10.  Advanced Imaging Utilization Trends in Privately Insured Patients From 2007 to 2013.

Authors:  Michal Horný; James F Burgess; Alan B Cohen
Journal:  J Am Coll Radiol       Date:  2015-12       Impact factor: 5.532

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Authors:  Jason J Wang; Casey E Pelzl; Artem Boltyenkov; Jeffrey M Katz; Jennifer Hemingway; Eric W Christensen; Elizabeth Rula; Pina C Sanelli
Journal:  J Am Coll Radiol       Date:  2022-04-25       Impact factor: 6.240

2.  Google Trends Data of Radiologists Who Accept Medicare: A Potential Tool for Predicting State Demand.

Authors:  Christine P Doepker; Haig Pakhchanian; Rahul Raiker; Dhairya A Lakhani; Jeffery P Hogg
Journal:  Curr Probl Diagn Radiol       Date:  2021-03-08

3.  Performance of deep learning to detect mastoiditis using multiple conventional radiographs of mastoid.

Authors:  Kyong Joon Lee; Inseon Ryoo; Dongjun Choi; Leonard Sunwoo; Sung-Hye You; Hye Na Jung
Journal:  PLoS One       Date:  2020-11-11       Impact factor: 3.240

  3 in total

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