• 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

随访影像建议的自动追踪

Automated Tracking of Follow-Up Imaging Recommendations.

作者信息

Mabotuwana Thusitha, Hall Christopher S, Hombal Vadiraj, Pai Prashanth, Raghavan Usha Nandini, Regis Shawn, McKee Brady, Dalal Sandeep, Wald Christoph, Gunn Martin L

机构信息

Radiology Solutions, Philips Healthcare, 22100 Bothell Everett Hwy, Bothell, WA 98021.

Department of Radiology, University of Washington, Seattle, WA.

出版信息

AJR Am J Roentgenol. 2019 Jun;212(6):1287-1294. doi: 10.2214/AJR.18.20586. Epub 2019 Mar 12.

DOI:10.2214/AJR.18.20586
PMID:30860895
Abstract

Radiology reports often contain follow-up imaging recommendations. Failure to comply with these recommendations in a timely manner can lead to poor patient outcomes, complications, and legal liability. As such, the primary objective of this research was to determine adherence rates to follow-up recommendations. Radiology-related examination data, including report text, for examinations performed between June 1, 2015, and July 31, 2017, were extracted from the radiology departments at the University of Washington (UW) and Lahey Hospital and Medical Center (LHMC). The UW dataset contained 923,885 examinations, and the LHMC dataset contained 763,059 examinations. A 1-year period was used for detection of imaging recommendations and up to 14-months for the follow-up examination to be performed. On the basis of an algorithm with 97.9% detection accuracy, the follow-up imaging recommendation rate was 11.4% at UW and 20.9% at LHMC. Excluding mammography examinations, the overall follow-up imaging adherence rate was 51.9% at UW (range, 44.4% for nuclear medicine to 63.0% for MRI) and 52.0% at LHMC (range, 30.1% for fluoroscopy to 63.2% for ultrasound) using a matcher algorithm with 76.5% accuracy. This study suggests that follow-up imaging adherence rates vary by modality and between sites. Adherence rates can be influenced by various legitimate factors. Having the capability to identify patients who can benefit from patient engagement initiatives is important to improve overall adherence rates. Monitoring of follow-up adherence rates over time and critical evaluation of variation in recommendation patterns across the practice can inform measures to standardize and help mitigate risk.

摘要

放射学报告通常包含随访影像学建议。未能及时遵守这些建议可能导致患者预后不良、出现并发症以及承担法律责任。因此,本研究的主要目的是确定对随访建议的依从率。从华盛顿大学(UW)和拉希医院及医疗中心(LHMC)的放射科提取了2015年6月1日至2017年7月31日期间进行的放射学相关检查数据,包括报告文本。UW数据集包含923,885次检查,LHMC数据集包含763,059次检查。使用1年时间来检测影像学建议,并使用长达14个月的时间来进行随访检查。基于一种检测准确率为97.9%的算法,UW的随访影像学建议率为11.4%,LHMC为20.9%。排除乳腺摄影检查后,使用准确率为76.5%的匹配算法,UW的总体随访影像学依从率为51.9%(范围为核医学的44.4%至MRI的63.0%),LHMC为52.0%(范围为透视的30.1%至超声的63.2%)。本研究表明,随访影像学依从率因检查方式和不同机构而异。依从率可能受到各种合理因素的影响。有能力识别能从患者参与举措中受益的患者对于提高总体依从率很重要。随着时间推移监测随访依从率以及对整个医疗机构建议模式的差异进行严格评估可为标准化措施提供依据,并有助于降低风险。

相似文献

1
Automated Tracking of Follow-Up Imaging Recommendations.随访影像建议的自动追踪
AJR Am J Roentgenol. 2019 Jun;212(6):1287-1294. doi: 10.2214/AJR.18.20586. Epub 2019 Mar 12.
2
Determining Adherence to Follow-up Imaging Recommendations.确定对随访影像学建议的依从性。
J Am Coll Radiol. 2018 Mar;15(3 Pt A):422-428. doi: 10.1016/j.jacr.2017.11.022.
3
Determining Follow-Up Imaging Study Using Radiology Reports.基于放射科报告判断是否需要进行随访影像学检查。
J Digit Imaging. 2020 Feb;33(1):121-130. doi: 10.1007/s10278-019-00260-w.
4
Impact of Follow-Up Imaging Recommendation Specificity on Adherence.随访影像学推荐特异性对依从性的影响。
Stud Health Technol Inform. 2022 Jun 29;295:87-90. doi: 10.3233/SHTI220667.
5
Extracting Follow-Up Recommendations and Associated Anatomy from Radiology Reports.从放射学报告中提取随访建议及相关解剖结构。
Stud Health Technol Inform. 2017;245:1090-1094.
6
Variation in Follow-up Imaging Recommendations in Radiology Reports: Patient, Modality, and Radiologist Predictors.影像学报告中随访影像学建议的变化:患者、检查方式和放射科医生的预测因素。
Radiology. 2019 Jun;291(3):700-707. doi: 10.1148/radiol.2019182826. Epub 2019 May 7.
7
Factors Affecting Adherence to Outpatient Radiology Report Recommendations.影响门诊放射科报告建议遵从性的因素。
J Am Coll Radiol. 2023 Jun;20(6):540-547. doi: 10.1016/j.jacr.2023.03.009. Epub 2023 Mar 27.
8
A Multidisciplinary Approach to Improving Appropriate Follow-Up Imaging of Ovarian Cysts: A Quality Improvement Initiative.一种改善卵巢囊肿适当随访成像的多学科方法:一项质量改进计划。
J Am Coll Radiol. 2016 May;13(5):535-41. doi: 10.1016/j.jacr.2016.01.015. Epub 2016 Mar 4.
9
Does radiologist recommendation for follow-up with the same imaging modality contribute substantially to high-cost imaging volume?放射科医生建议采用相同成像方式进行随访是否会大幅增加高成本成像的使用量?
Radiology. 2007 Mar;242(3):857-64. doi: 10.1148/radiol.2423051754.
10
Reducing Delay in Diagnosis: Multistage Recommendation Tracking.减少诊断延迟:多阶段推荐跟踪
AJR Am J Roentgenol. 2017 Nov;209(5):970-975. doi: 10.2214/AJR.17.18332. Epub 2017 Jul 25.

引用本文的文献

1
Privacy-Preserving Large Language Model for Matching Findings and Tracking Interval Changes in Longitudinal Radiology Reports.用于匹配纵向放射学报告中的检查结果并跟踪间隔变化的隐私保护大语言模型。
J Imaging Inform Med. 2025 Apr 11. doi: 10.1007/s10278-025-01478-7.
2
Communication and Follow-Up of Noncritical Actionable Imaging Findings.非危急可操作影像检查结果的沟通与随访
Mo Med. 2024 Nov-Dec;121(6):433-435.
3
Patient Photograph Association With Radiologist Recommendations for Additional Imaging.患者照片与放射科医生关于额外影像学检查建议的关联
J Am Coll Radiol. 2025 Apr;22(4):478-485. doi: 10.1016/j.jacr.2024.10.018. Epub 2024 Nov 12.
4
Actionability of Recommendations for Additional Imaging in Head and Neck Radiology.头颈部放射学中额外成像建议的可操作性。
J Am Coll Radiol. 2024 Jul;21(7):1040-1048. doi: 10.1016/j.jacr.2024.01.005. Epub 2024 Jan 12.
5
Patient Engagement in Neuroradiology: A Narrative Review and Case Studies.神经放射学中的患者参与:叙述性综述与案例研究
AJNR Am J Neuroradiol. 2024 Mar 7;45(3):250-255. doi: 10.3174/ajnr.A8077.
6
Development and Assessment of an Information Technology Intervention to Improve the Clarity of Radiologist Follow-up Recommendations.开发和评估信息技术干预措施,以提高放射科医生随访建议的清晰度。
JAMA Netw Open. 2023 Mar 1;6(3):e236178. doi: 10.1001/jamanetworkopen.2023.6178.
7
Event-Based Clinical Finding Extraction from Radiology Reports with Pre-trained Language Model.基于事件的放射学报告临床发现提取与预训练语言模型。
J Digit Imaging. 2023 Feb;36(1):91-104. doi: 10.1007/s10278-022-00717-5. Epub 2022 Oct 17.
8
Analysis of Radiology Report Recommendation Characteristics and Rate of Recommended Action Performance.分析放射学报告推荐特征和推荐行动执行率。
JAMA Netw Open. 2022 Jul 1;5(7):e2222549. doi: 10.1001/jamanetworkopen.2022.22549.
9
Automatic Fully-Contextualized Recommendation Extraction from Radiology Reports.从放射学报告中自动提取全上下文推荐信息。
J Digit Imaging. 2021 Apr;34(2):374-384. doi: 10.1007/s10278-021-00423-8. Epub 2021 Feb 10.