利用自然语言处理和高维临床笔记检测护理目标和代理指定对话。

IF 1.7 4区 医学 Q2 NURSING
Alaa Albashayreh, Keela Herr, Weiguo Fan, W Nick Street, Stephanie Gilbertson-White
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引用次数: 0

摘要

涉及护理目标和代理指定对话的预先护理规划对于以患者为中心的护理至关重要。然而,确定这些对话的最佳时机和参与者仍具有挑战性。本研究探讨了在晚期疾病患者的临床笔记中记录护理目标和代理指定谈话这两项预先护理计划要素的频率、时机和预测因素。在这项回顾性观察研究中,我们利用高维数据和自然语言处理(NLP)来分析临床笔记,并预测是否存在预先护理规划对话。我们收录了在美国中西部一家医院接受治疗的患者的病历,这些患者均患有晚期慢性疾病并最终去世。我们对一个黄金标准数据集(n = 913 份病历)进行了人工标注,以确定病历中是否存在预先护理计划对话,标注者之间的一致性非常好(90.5%)。通过训练和测试四种 NLP 模型来检测护理目标和代理指定对话,我们发现基于变压器的模型(来自变压器的双向编码器表征 [BERT])准确率最高,F1 得分为 93.6。然后,我们将 BERT 模型部署到由 4,341 名患者的 247,241 份笔记组成的高维语料库中,分别在 85% 和 60% 的患者记录中检测到了护理目标和代理指定对话。时间分析表明,护理目标和代理指定对话分别在患者死亡前 28 天和 8 天首次记录在案。患者特征和转诊至专科姑息治疗是记录这些对话的重要相关因素。我们的研究结果证明了 NLP(尤其是基于 Transformer 的模型,如 BERT)在准确检测临床叙述中的护理目标和代理指定对话方面的潜力。这项研究发现了与这些对话相关的重要时间模式(包括延迟记录)和患者特征。它强调了高维数据在提高我们对预先护理计划的理解方面的价值,并为改善临床环境中以患者为中心的护理提供了启示。未来的研究应探索将这些模型整合到临床工作流程中,以促进及时有效的预先护理计划讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Harnessing Natural Language Processing and High-Dimensional Clinical Notes to Detect Goals-of-Care and Surrogate-Designation Conversations.

Advance care planning, involving goals-of-care and surrogate-designation conversations, is crucial for patient-centered care. However, determining the optimal timing and participants for these conversations remains challenging. This study explored the frequency, timing, and predictors of documenting two advance care planning elements, goals-of-care and surrogate-designation conversations, in clinical notes for patients with advanced illness. In this retrospective observational study, we leveraged high-dimensional data and natural language processing (NLP) to analyze clinical notes and predict the presence or absence of advance care planning conversations. We included notes for patients treated at a Midwestern United States hospital who had advanced chronic conditions and eventually passed away. We manually labeled a gold-standard dataset (n = 913 notes) for the presence or absence of advance care planning conversations at the note level, achieving excellent inter-annotator agreement (90.5%). Training and testing four NLP models to detect goals-of-care and surrogate-designation conversations revealed that a transformer-based model (Bidirectional Encoder Representations from Transformers [BERT]) achieved the highest accuracy, with an F1 score of 93.6. We then deployed the BERT model to a high-dimensional corpus of 247,241 notes for 4,341 patients and detected goals-of-care and surrogate-designation conversations in the records of 85% and 60% of patients, respectively. Temporal analysis revealed that goals-of-care and surrogate-designation conversations were first documented at medians 28 and 8 days before death, respectively. Patient characteristics and referral to specialty palliative care emerged as significant factors associated with documenting these conversations. Our findings demonstrate the potential of NLP, particularly Transformer-based models like BERT, to accurately detect goals-of-care and surrogate-designation conversations in clinical narratives. This study identified significant temporal patterns, including late documentation, and patient characteristics associated with these conversations. It highlights the value of high-dimensional data in enhancing our understanding of advance care planning and offers insights for improving patient-centered care in clinical settings. Future research should explore the integration of these models into clinical workflows to facilitate timely and effective advance care planning discussions.

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来源期刊
CiteScore
3.40
自引率
5.90%
发文量
107
审稿时长
>12 weeks
期刊介绍: Clinical Nursing Research (CNR) is a peer-reviewed quarterly journal that addresses issues of clinical research that are meaningful to practicing nurses, providing an international forum to encourage discussion among clinical practitioners, enhance clinical practice by pinpointing potential clinical applications of the latest scholarly research, and disseminate research findings of particular interest to practicing nurses. This journal is a member of the Committee on Publication Ethics (COPE).
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