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谷歌趋势数据显示,接受医疗保险的放射科医生:预测州需求的潜在工具。

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

机构信息

School of Medicine, West Virginia University, Morgantown, WV.

School of Medicine, George Washington University, Washington, DC.

出版信息

Curr Probl Diagn Radiol. 2022 Jan-Feb;51(1):46-50. doi: 10.1067/j.cpradiol.2021.03.004. Epub 2021 Mar 8.

DOI:10.1067/j.cpradiol.2021.03.004
PMID:33814217
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8423862/
Abstract

PURPOSE

To 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.

METHODS

The 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.

RESULTS

West 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.

CONCLUSION

Imaging 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.

摘要

目的

从 2004 年到 2009 年,通过谷歌搜索量来确定和分析每个州接受联邦医疗保险(Medicare)的放射科医生的需求,并通过各州的其他可用数据来确定这种需求。

方法

根据各州的人口数量,将接受联邦医疗保险的放射科医生人数除以各州的人口数量,得出每 10000 名居民的放射科医生密度。从 2004 年到 2009 年,从谷歌趋势中收集了“放射科医生”一词的相对搜索量(RSV)。然后,通过将每个州的 RSV 除以该州的放射科医生密度,计算出每个州的放射科医生需求指数(RDI)。为了标准化数值,每个州的 RDI 除以最大的 RDI,生成相对放射科医生需求指数(RRDI)。利用每个州每 1000 名联邦医疗保险受益人的医疗成像使用率、每个州的人口整体健康状况以及每个州参加联邦医疗保险的人口比例,与 RRDI 进行比较,以了解趋势。

结果

西弗吉尼亚州对接受联邦医疗保险的放射科医生的好奇心最大(谷歌搜索量比例最高)(RSV=100),其次是密西西比州(RSV=95)和阿肯色州(RSV=87)。俄勒冈州对接受联邦医疗保险的放射科医生的好奇心最低,谷歌搜索量比例最低(RSV=43),其次是佛蒙特州(RSV=49)、加利福尼亚州(RSV=50)和科罗拉多州(RSV=50)。人口密度最高的放射科医生分别在蒙大拿州、哥伦比亚特区和怀俄明州(3.25、1.56、1.11)。人口密度最低的放射科医生分别在俄克拉荷马州、德克萨斯州和犹他州(0.4、0.4、0.41、0.41)。RRDI 最高的是路易斯安那州(100)、阿肯色州(94.8)和德克萨斯州(86.3),最低的是蒙大拿州(10.6)、哥伦比亚特区(17.7)和怀俄明州(28.4)。研究发现,医疗成像的使用量(一种间接需求衡量指标)与 RRDI 呈正相关。每个州的 RRDI 和每 1000 名联邦医疗保险受益人的成像数量与该州总人口的整体健康状况呈正相关。然而,没有发现每个州的 RRDI 与参加联邦医疗保险的人口比例之间存在任何趋势。

结论

成像研究的实施,即需求的间接衡量指标,与 RRDI 呈正相关。RRDI 较高和每 1000 名联邦医疗保险受益人的成像数量与该州总人口的健康评分较低有关。RRDI 可能是一个有用的工具,可以反映每个州对接受联邦医疗保险的放射科医生的需求。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8b2/8423862/95db9ddddb4b/nihms-1730294-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8b2/8423862/22b433366f97/nihms-1730294-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8b2/8423862/95db9ddddb4b/nihms-1730294-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8b2/8423862/22b433366f97/nihms-1730294-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8b2/8423862/95db9ddddb4b/nihms-1730294-f0002.jpg

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