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01:08Role of Communication in the Nursing Process III: Evaluation and Documentation
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A successful patient outcome depends mainly on the evaluation stage of the nursing process. Evaluation determines effectiveness by reviewing what was done previously after the completion of nursing interventions. Every time a healthcare professional steps in or administers treatment, they must reassess or evaluate the action to ensure the intended result. During the evaluation phase, there are three probable patient outcomes:
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01:18Methods of Documentation I: Source-Oriented Records
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Source-oriented records, or SOR, are medical record-keeping organized by the data source. The SOR system was first developed in the mid-1900s to organize the growing patient data in hospitals and other healthcare facilities.
In an SOR, each discipline involved in patient care maintains a separate medical record section. This record-keeping method enables easy tracking of patient progress and ensures healthcare staff have access to up-to-date information.
Key Attributes include the following:
In an SOR, each discipline involved in patient care maintains a separate medical record section. This record-keeping method enables easy tracking of patient progress and ensures healthcare staff have access to up-to-date information.
Key Attributes include the following:
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01:26Methods of Documentation II: POMR
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The Problem-Oriented Medical Record (POMR) revolutionized medical record-keeping by introducing a systematic approach focusing on the patient's problems rather than merely listing symptoms. Dr. Lawrence Weed's introduction of this method in the 1960s marked a significant advancement in medical documentation. The POMR framework consists of four key components: the database, problem list, plan of care, and progress notes.
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01:15Nursing Evaluation
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The evaluation stage signals the end of the nursing process. The nurse gathers evaluative data to assess whether or not the patient has attained the expected results. Whereas the nurse collects data in the nursing assessment to identify the patient's health concerns, the evaluation stage data determines if the indicated health issues are resolved. Evaluative data collection includes two sections: the data acquired to evaluate patient outcomes and the time criteria for data collection.
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01:30Guidelines for Nursing Documentation I
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Quality documentation and reporting share essential characteristics that ensure they are practical and valuable resources for those who use them. These characteristics are:
Factual:
The following points emphasize the significance of upholding accurate and unbiased documentation in healthcare.
Factual:
The following points emphasize the significance of upholding accurate and unbiased documentation in healthcare.
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01:23Methods of Documentation V: CBE
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Charting by Exception, or CBE, is a method of documentation used in healthcare, particularly in nursing, that focuses on documenting only significant or abnormal findings rather than recording every detail. This approach aims to streamline the documentation process, improve efficiency, and ensure that healthcare providers can quickly identify deviations from normalcy in patient assessments.
In CBE, healthcare professionals establish predefined standards of practice that define what constitutes...
In CBE, healthcare professionals establish predefined standards of practice that define what constitutes...
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系统评估常见的自然语言处理技术,以编码临床笔记.
Nazgol Tavabi1,2, Mallika Singh1, James Pruneski1,2
1Boston Children's Hospital, Boston, MA, United States of America.
PloS one
|March 7, 2024
概括
传统的自然语言处理 (NLP) 方法有效地从操作笔记中预测外科手术程序代码 (CPT),优于复杂模型. 这些NLP技术提供了可解释性,可以减少医疗编码中的人为错误.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
背景情况:
- 准确的医疗编码对于医疗管理,研究和报销至关重要.
- 手动编码是劳动密集型的,容易出现人为错误.
- 自然语言处理 (NLP) 为自动化医疗编码提供了潜在的解决方案.
研究的目的:
- 评估常见的NLP技术,用于从操作笔记中预测当前程序术语 (CPT) 代码.
- 将传统NLP方法的性能与资源密集型模型 (如BERT) 的性能进行比较.
- 在NLP中引入分类任务的复杂度指标及其对数据集大小的影响.
主要方法:
- 各种NLP技术的综合性绩效评估.
- 分析重点是从操作笔记中获得的100个常见的肌肉骨CPT代码.
- 传统NLP方法与BERT的统计比较,包括AUROC和准确度指标.
主要成果:
- 传统的NLP方法在CPT代码预测方面显著优于BERT (P值=4.4e-17).
- 通过传统的NLP实现了高性能:平均AUROC为0.96和准确度为0.97.
- 证明了传统NLP模型的可解释性,这对于临床应用至关重要.
结论:
- 简单的,传统的NLP技术对于CPT代码预测非常有效和高效.
- NLP可以显著减少医疗编码错误,包括来自人类错误的错误.
- 提出的复杂性测量可以指导基于数据集特征的NLP模型的应用.