Evidence map›Paper›PMID 33814217›Full record

ArticleCurrent problems in diagnostic radiology

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

Christine P Doepker, Haig Pakhchanian, Rahul Raiker, Dhairya A Lakhani, Jeffery P Hogg

Abstract read
In one paragraph

Article in Current problems in diagnostic radiology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Observational
  2. 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

5 authors.

Christine P DoepkerSchool of Medicine, West Virginia University, Morgantown, WV.
Haig PakhchanianSchool of Medicine, George Washington University, Washington, DC.
Rahul RaikerSchool of Medicine, West Virginia University, Morgantown, WV.
Dhairya A LakhaniDepartment of Radiology, West Virginia University School of Medicine, Morgantown, WV.
Jeffery P HoggSchool of Medicine, West Virginia University, Morgantown, WV.; Department of Radiology, West Virginia University School of Medicine, Morgantown, WV.; Department of Neuroradiology, Rockefeller Neuroscience Institute, West Virginia University School of Medicine, Morgantown, WV.. Electronic address: jhogg@hsc.wvu.edu.

Funding

West Virginia IDEA-CTRU54GM104942 · NIGMS · WEST VIRGINIA UNIVERSITY · PI Sally Lynn Hodder · 2012 to 2026
$81.0M
NIGMS NIH HHS U54 GM104942
6 · The paper itself

Abstract

purposeTo identify and analyze the demand for radiologists who accept Medicare per state from 2004 to 2009, as reflected by volume of Google searches, and to place such demand in context with other available data by state.

methodsThe number of radiologists who accept Medicare by state was divided by each state's population to achieve the radiologist density per 10,000 residents. Relative search volume (RSV) for the term "radiologist" was collected from Google Trends from 2004 to 2009. The Radiologist Demand Index (RDI) for each state was then calculated by dividing each state's RSV by the radiologist density for that state. To standardize values, each state's RDI was divided by the largest RDI to generate the Relative Radiologist Demand Index (RRDI). Utilization of medical imaging per 1000 Medicare beneficiaries in each state, overall health of a population in each state, and percentage of the population enrolled in Medicare in each state were used to compare trends with the RRDI.

resultsWest Virginia had the greatest curiosity about radiologists who accept Medicare (as represented by proportion of Google searches) (RSV=100), followed by Mississippi (RSV=95), and Arkansas (RSV=87). Oregon demonstrated the lowest level of curiosity about radiologists who accept Medicare, by having the lowest proportion of google searches (RSV=43), followed by Vermont (RSV=49), California (RSV=50), and Colorado (RSV=50). The highest radiologist densities per population were found in Montana, D.C., and Wyoming (3.25, 1.56, 1.11, respectively). The lowest radiologist densities were found in Oklahoma, Texas, and Utah (0.4, 0.4, 0.41, 0.41, respectively). The RRDI was greatest in Louisiana (100), Arkansas (94.8), and Texas (86.3), and smallest in Montana (10.6), D.C. (17.7) and Wyoming (28.4). Positive trends between utilization of medical imaging per 1000 Medicare beneficiaries and state overall health and the RRDI were recognized. No trend between each state's RRDI and percentage of population enrolled in Medicare was noted.

conclusionImaging studies performed, an indirect measure of demand, showed trends with RRDI. Higher RRDI and imaging per 1000 Medicare beneficiaries trended with lower health scores for a state's general population. RRDI may be a useful tool reflecting each state's demand for radiologist who accepts Medicare.

Indexed as

MedicareSearch EngineAgedHumansRadiologistsTexasUnited StatesUtah

Identifiers

PMID33814217
PMCPMC8423862

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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.