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本文引用的文献

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Patient-Sharing Networks of Physicians and Health Care Utilization and Spending Among Medicare Beneficiaries.医生患者共享网络与医疗保险受益人的医疗利用和支出
JAMA Intern Med. 2018 Jan 1;178(1):66-73. doi: 10.1001/jamainternmed.2017.5034.
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[Patient-sharing networks : New approaches in the analysis and transformation of geographic variation in healthcare].[患者共享网络:医疗保健地理差异分析与转化的新方法]
Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2017 Dec;60(12):1356-1371. doi: 10.1007/s00103-017-2641-7.
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The Impact of Social Contagion on Physician Adoption of Advanced Imaging Tests in Breast Cancer.社会传播对乳腺癌医生采用先进影像检查的影响
J Natl Cancer Inst. 2017 Aug 1;109(8). doi: 10.1093/jnci/djw330.
4
Provider Patient-Sharing Networks and Multiple-Provider Prescribing of Benzodiazepines.医疗服务提供者患者共享网络与苯二氮䓬类药物的多医疗服务提供者处方开具
J Gen Intern Med. 2016 Feb;31(2):164-171. doi: 10.1007/s11606-015-3470-8. Epub 2015 Jul 18.
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Physician Networks and Ambulatory Care-sensitive Admissions.医师网络与门诊护理敏感型住院病例
Med Care. 2015 Jun;53(6):534-41. doi: 10.1097/MLR.0000000000000365.
6
Physician's peer exposure and the adoption of a new cancer treatment modality.医生的同行接触与一种新癌症治疗方式的采用
Cancer. 2015 Aug 15;121(16):2799-807. doi: 10.1002/cncr.29409. Epub 2015 Apr 22.
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Variation in patient-sharing networks of physicians across the United States.美国医生间的患者共享网络的差异。
JAMA. 2012 Jul 18;308(3):265-73. doi: 10.1001/jama.2012.7615.
8
Physician social networks and variation in prostate cancer treatment in three cities.医生社交网络与三个城市前列腺癌治疗的差异
Health Serv Res. 2012 Feb;47(1 Pt 2):380-403. doi: 10.1111/j.1475-6773.2011.01331.x. Epub 2011 Oct 18.
9
Mapping physician networks with self-reported and administrative data.用自报数据和行政数据绘制医生网络图谱。
Health Serv Res. 2011 Oct;46(5):1592-609. doi: 10.1111/j.1475-6773.2011.01262.x. Epub 2011 Apr 26.
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Social network analysis of patient sharing among hospitals in Orange County, California.加利福尼亚州橙县医院间患者共享的社会网络分析。
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从理赔数据中确定医生同行群体的经验方法:以乳腺癌护理为例。

An empiric approach to identifying physician peer groups from claims data: An example from breast cancer care.

机构信息

Section of Cardiovascular Medicine, Yale University School of Medicine, New Haven, Connecticut.

Cancer Outcomes, Public Policy and Effectiveness Research (COPPER) Center, Yale University School of Medicine, New Haven, Connecticut.

出版信息

Health Serv Res. 2019 Feb;54(1):44-51. doi: 10.1111/1475-6773.13095. Epub 2018 Nov 28.

DOI:10.1111/1475-6773.13095
PMID:30488484
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6338298/
Abstract

OBJECTIVE

To develop an empiric approach for evaluating the performance of physician peer groups based on patient-sharing in administrative claims data.

DATA SOURCES

Surveillance, Epidemiology and End Results-Medicare linked dataset.

STUDY DESIGN

Applying social network theory, we constructed physician peer groups for patients with breast cancer. Under different assumptions of key parameter values-minimum patient volume for physician inclusion and minimum number of patients shared between physicians for a connection-we compared agreement in group membership between split samples during 2004-2006 (T1) (reliability) and agreement in group membership between T1 and 2007-2009 (T2) (stability). We also compared the results with those derived from randomly generated groups and to hospital affiliation-based groups.

PRINCIPAL FINDINGS

The sample included 142 098 patients treated by 43 174 physicians in T1 and 136 680 patients treated by 51 515 physicians in T2. We identified parameter values that resulted in a median peer group reliability of 85.2 percent (Interquartile range (IQR) [0 percent, 96.2 percent]) and median stability of 73.7 percent (IQR [0 percent, 91.0 percent]). In contrast, stability of randomly assigned peer groups was 6.2 percent (IQR [0 percent, 21.0 percent]). Median overlap of empirical groups with hospital groups was 32.2 percent (IQR [12.1 percent, 59.2 percent]).

CONCLUSIONS

It is feasible to construct physician peer groups that are reliable, stable, and distinct from both randomly generated and hospital-based groups.

摘要

目的

利用行政索赔数据中患者共享信息,开发一种评估医师同行组表现的经验方法。

数据来源

监测、流行病学和最终结果-医疗保险链接数据集。

研究设计

应用社会网络理论,我们为乳腺癌患者构建了医师同行组。在关键参数值的不同假设下(纳入医师的最低患者量和医师之间共享的最低患者数量),我们比较了 2004-2006 年(T1)期间(可靠性)和 2007-2009 年(T2)期间(稳定性)的分组样本之间的分组成员一致性,并比较了结果与随机生成组和基于医院隶属关系的组的结果。

主要发现

该样本包括 142098 名在 T1 期间由 43174 名医师治疗的患者和 136680 名在 T2 期间由 51515 名医师治疗的患者。我们确定了导致同行组可靠性中位数为 85.2%(四分位距(IQR)[0%,96.2%])和稳定性中位数为 73.7%(IQR [0%,91.0%])的参数值。相比之下,随机分配的同行组的稳定性为 6.2%(IQR [0%,21.0%])。经验组与医院组的重叠中位数为 32.2%(IQR [12.1%,59.2%])。

结论

构建可靠、稳定且与随机生成和基于医院的组不同的医师同行组是可行的。