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支持癌症患者的结构化数据采集:威斯康星大学 Carbone 癌症中心生存项目的一项举措,旨在改进恶性诊断和癌症分期数据的采集。

Supporting Structured Data Capture for Patients With Cancer: An Initiative of the University of Wisconsin Carbone Cancer Center Survivorship Program to Improve Capture of Malignant Diagnosis and Cancer Staging Data.

机构信息

University of Wisconsin, Madison, WI.

Carbone Cancer Center, Madison, WI.

出版信息

JCO Clin Cancer Inform. 2022 Jun;6:e2200020. doi: 10.1200/CCI.22.00020.

Abstract

PURPOSE

Structured data elements within electronic health records are health-related information that can be entered, stored, and extracted in an organized manner at later time points. Tracking outcomes for cancer survivors is also enabled by structured data. We sought to increase structured data capture within oncology practices at multiple sites sharing the same electronic health records.

METHODS

Applying engineering approaches and the Plan-Do-Study-Act cycle, we launched dual quality improvement initiatives to ensure that a malignant diagnosis and stage were captured as structured data. Intervention: Close Visit Validation (CVV) requires providers to satisfy certain criteria before closing ambulatory encounters. CVV may be used to track open clinical encounters and chart delinquencies to encourage optimal clinical workflows. We added two cancer-specific required criteria at the time of closing encounters in oncology clinics: (1) the presence of at least one malignant diagnosis on the Problem List and (2) staging all the malignant diagnoses on the Problem List when appropriate.

RESULTS

Six months before the CVV implementation, the percentage of encounters with a malignant diagnosis on the Problem List at the time of the encounter was 65%, whereas the percentage of encounters with a staged diagnosis was 32%. Three months after cancer-specific CVV implementation, the percentages were 85% and 75%, respectively. Rates had increased to 90% and 88% more than 2 years after implementation.

CONCLUSION

Oncologist performance improved after the implementation of cancer-specific CVV criteria, with persistently high percentages of relevant malignant diagnoses and cancer stage structured data capture 2 years after the intervention.

摘要

目的

电子健康记录中的结构化数据元素是与健康相关的信息,可以在以后的时间点以有组织的方式输入、存储和提取。结构化数据还可以跟踪癌症幸存者的结果。我们旨在增加多个共享相同电子健康记录的肿瘤学实践中的结构化数据捕获。

方法

应用工程方法和计划-执行-研究-行动循环,我们启动了两项双重质量改进举措,以确保恶性诊断和分期被捕获为结构化数据。干预措施:密切就诊验证(CVV)要求提供者在关闭门诊就诊之前满足某些标准。CVV 可用于跟踪开放的临床就诊和图表延误,以鼓励最佳的临床工作流程。我们在肿瘤学诊所关闭就诊时添加了两个癌症特定的必需标准:(1)在问题清单上至少存在一个恶性诊断,以及(2)在适当情况下对问题清单上的所有恶性诊断进行分期。

结果

在 CVV 实施前 6 个月,就诊时问题清单上存在恶性诊断的就诊百分比为 65%,而分期诊断的就诊百分比为 32%。在实施癌症特异性 CVV 三个月后,这两个百分比分别为 85%和 75%。实施 2 年多后,这两个百分比分别增加到 90%和 88%。

结论

在实施癌症特异性 CVV 标准后,肿瘤学家的表现有所提高,在干预 2 年后,仍有持续较高的相关恶性诊断和癌症分期结构化数据捕获率。

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