• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

医疗服务提供者数量对医院报告卡准确性的影响:一项蒙特卡洛研究。

Effect of provider volume on the accuracy of hospital report cards: a Monte Carlo study.

作者信息

Austin Peter C, Reeves Mathew J

机构信息

Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada.

出版信息

Circ Cardiovasc Qual Outcomes. 2014 Mar;7(2):299-305. doi: 10.1161/CIRCOUTCOMES.113.000685. Epub 2014 Mar 11.

DOI:10.1161/CIRCOUTCOMES.113.000685
PMID:24619320
Abstract

BACKGROUND

Hospital report cards, in which outcomes after the provision of medical or surgical care are compared across healthcare providers, are being published with increasing frequency. However, the accuracy of such comparisons is controversial, especially when case volumes are small. The objective was to determine the relationship between hospital case volume and the accuracy of hospital report cards.

METHODS AND RESULTS

Monte Carlo simulations were used to examine the influence of hospital case volume on the accuracy of hospital report cards in a setting in which true hospital performance was known with certainty, and perfect risk-adjustment was feasible. The parameters used to generate the simulated data sets were obtained from empirical analyses of data on patients hospitalized with acute myocardial infarction in Ontario, Canada, in which the overall 30-day mortality rate was 11.1%. We found that provider volume had a strong effect on the accuracy of hospital report cards. However, provider volume had to be >300 before ≥70% of hospitals were correctly classified. Furthermore, hospital volume had to be >1000 before ≥80% of hospitals were correctly classified.

CONCLUSIONS

Producers and users of hospital report cards need to be aware that, even when perfect risk adjustment is possible, the accuracy of hospital report cards is, at best, modest for small to medium-sized case loads (i.e., 100-300). Hospital report cards displayed high degrees of accuracy only when provider volumes exceeded the typical annual hospital case load for many cardiovascular conditions and procedures.

摘要

背景

医院报告卡越来越频繁地发布,其中会比较不同医疗服务提供者提供医疗或外科护理后的结果。然而,这种比较的准确性存在争议,尤其是在病例数量较少时。目的是确定医院病例数量与医院报告卡准确性之间的关系。

方法与结果

在已知真实医院表现且可行完美风险调整的情况下,使用蒙特卡洛模拟来检验医院病例数量对医院报告卡准确性的影响。用于生成模拟数据集的参数取自对加拿大安大略省急性心肌梗死住院患者数据的实证分析,其中总体30天死亡率为11.1%。我们发现医疗服务提供者的病例数量对医院报告卡的准确性有很大影响。然而,医疗服务提供者的病例数量必须超过300,才有≥70%的医院能被正确分类。此外,医院病例数量必须超过1000,才有≥80%的医院能被正确分类。

结论

医院报告卡的编制者和使用者需要意识到,即使可以进行完美的风险调整,对于中小病例量(即100 - 300),医院报告卡的准确性充其量也只是一般。只有当医疗服务提供者的病例数量超过许多心血管疾病和手术的典型年度医院病例量时,医院报告卡才显示出高度的准确性。

相似文献

1
Effect of provider volume on the accuracy of hospital report cards: a Monte Carlo study.医疗服务提供者数量对医院报告卡准确性的影响:一项蒙特卡洛研究。
Circ Cardiovasc Qual Outcomes. 2014 Mar;7(2):299-305. doi: 10.1161/CIRCOUTCOMES.113.000685. Epub 2014 Mar 11.
2
The impact of unmeasured clinical variables on the accuracy of hospital report cards: a Monte Carlo study.未测量的临床变量对医院报告卡准确性的影响:一项蒙特卡洛研究。
Med Decis Making. 2006 Sep-Oct;26(5):447-66. doi: 10.1177/0272989X06290498.
3
The relationship between the C-statistic of a risk-adjustment model and the accuracy of hospital report cards: a Monte Carlo Study.风险调整模型的 C 统计量与医院报告卡准确性之间的关系:一项蒙特卡罗研究。
Med Care. 2013 Mar;51(3):275-84. doi: 10.1097/MLR.0b013e31827ff0dc.
4
Quality of Quality Measurement: Impact of Risk Adjustment, Hospital Volume, and Hospital Performance.质量测量的质量:风险调整、医院规模和医院绩效的影响
Anesthesiology. 2016 Dec;125(6):1092-1102. doi: 10.1097/ALN.0000000000001362.
5
Impact of the choice of benchmark on the conclusions of hospital report cards.基准选择对医院报告卡结论的影响。
Am Heart J. 2004 Dec;148(6):1041-6. doi: 10.1016/j.ahj.2004.04.047.
6
The use of fixed- and random-effects models for classifying hospitals as mortality outliers: a Monte Carlo assessment.使用固定效应模型和随机效应模型将医院分类为死亡率异常值:蒙特卡洛评估
Med Decis Making. 2003 Nov-Dec;23(6):526-39. doi: 10.1177/0272989X03258443.
7
Comparison of "risk-adjusted" hospital outcomes.“风险调整后”医院治疗结果的比较。
Circulation. 2008 Apr 15;117(15):1955-63. doi: 10.1161/CIRCULATIONAHA.107.747873. Epub 2008 Apr 7.
8
Accuracy of hospital standardized mortality rates: effects of model calibration.医院标准化死亡率的准确性:模型校准的影响。
Med Care. 2014 Apr;52(4):378-84. doi: 10.1097/MLR.0000000000000111.
9
Community factors, hospital characteristics and inter-regional outcome variations following acute myocardial infarction in Canada.加拿大急性心肌梗死后的社区因素、医院特征及地区间结局差异
Can J Cardiol. 2005 Mar;21(3):247-55.
10
What is the best way to estimate hospital quality outcomes? A simulation approach.如何最好地评估医院质量结果?一种模拟方法。
Health Serv Res. 2012 Aug;47(4):1699-718. doi: 10.1111/j.1475-6773.2012.01382.x. Epub 2012 Feb 21.

引用本文的文献

1
The relationship between the C-statistic and the accuracy of program-specific evaluations.C 统计量与特定方案评估准确性的关系。
Am J Transplant. 2019 Feb;19(2):407-413. doi: 10.1111/ajt.15132. Epub 2018 Oct 29.
2
Are Case Volume and Facility Complexity Level Associated With Postoperative Complications After Hip Fracture Surgery in the Veterans Affairs Healthcare System?退伍军人事务部医疗保健系统中,髋关节骨折手术后的术后并发症是否与病例量和医疗机构复杂程度有关?
Clin Orthop Relat Res. 2019 Jan;477(1):177-190. doi: 10.1097/CORR.0000000000000460.
3
Investigating Risk Adjustment Methods for Health Care Provider Profiling When Observations are Scarce or Events Rare.
在观察数据稀缺或事件罕见时,研究用于医疗服务提供者概况分析的风险调整方法。
Health Serv Insights. 2018 Jul 5;11:1178632918785133. doi: 10.1177/1178632918785133. eCollection 2018.
4
Outlier classification performance of risk adjustment methods when profiling multiple providers.多提供者特征分析时风险调整方法的离群分类性能。
BMC Med Res Methodol. 2018 Jun 15;18(1):54. doi: 10.1186/s12874-018-0510-1.