Harnessing Natural Language Processing to Assess Quality of End-of-Life Care for Children With Cancer.

IF 3.3 Q2 ONCOLOGY JCO Clinical Cancer Informatics Pub Date : 2024-09-01 DOI:10.1200/CCI.24.00134
Meghan E Lindsay, Sophia de Oliveira, Kate Sciacca, Charlotta Lindvall, Prasanna J Ananth
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Abstract

Purpose: Data on end-of-life care (EOLC) quality, assessed through evidence-based quality measures (QMs), are difficult to obtain. Natural language processing (NLP) enables efficient quality measurement and is not yet used for children with serious illness. We sought to validate a pediatric-specific EOLC-QM keyword library and evaluate EOLC-QM attainment among childhood cancer decedents.

Methods: In a single-center cohort of children with cancer who died between 2014 and 2022, we piloted a rule-based NLP approach to examine the content of clinical notes in the last 6 months of life. We identified documented discussions of five EOLC-QMs: goals of care, limitations to life-sustaining treatments (LLST), hospice, palliative care consultation, and preferred location of death. We assessed performance of NLP methods, compared with gold standard manual chart review. We then used NLP to characterize proportions of decedents with documented EOLC-QM discussions and timing of first documentation relative to death.

Results: Among 101 decedents, nearly half were minorities (Hispanic/Latinx [24%], non-Hispanic Black/African American [20%]), female (48%), or diagnosed with solid tumors (43%). Through iterative refinement, our keyword library achieved robust performance statistics (for all EOLC-QMs, F1 score = 1.0). Most decedents had documented discussions regarding goals of care (83%), LLST (83%), and hospice (74%). Fewer decedents had documented discussions regarding palliative care consultation (49%) or preferred location of death (36%). For all five EOLC-QMs, first documentation occurred, on average, >30 days before death.

Conclusion: A high proportion of decedents attained specified EOLC-QMs more than 30 days before death. Our findings indicate that NLP is a feasible approach to measuring quality of care for children with cancer at the end of life and is ripe for multi-center research and quality improvement.

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利用自然语言处理技术评估癌症儿童临终关怀的质量。
目的:通过循证质量测量(QMs)评估生命末期护理(EOLC)质量的数据很难获得。自然语言处理(NLP)可实现高效的质量测量,但尚未用于重症儿童。我们试图验证儿科专用的EOLC-QM关键词库,并评估儿童癌症死者的EOLC-QM达标情况:在 2014 年至 2022 年期间死亡的癌症儿童单中心队列中,我们试用了一种基于规则的 NLP 方法来检查生命最后 6 个月的临床笔记内容。我们确定了五项生命最后阶段质量管理(EOLC-QMs)的讨论记录:护理目标、维持生命治疗的限制(LLST)、临终关怀、姑息治疗咨询和首选死亡地点。与黄金标准人工病历审查相比,我们评估了 NLP 方法的性能。然后,我们使用 NLP 分析了有记录的 EOLC-QM 讨论的死者比例以及相对于死亡的首次记录时间:在 101 位死者中,近一半为少数族裔(西班牙裔/拉丁裔[24%]、非西班牙裔黑人/非洲裔美国人[20%])、女性(48%)或确诊为实体瘤患者(43%)。通过迭代改进,我们的关键词库实现了强大的性能统计(对于所有 EOLC-QM,F1 分数 = 1.0)。大多数死者都有关于护理目标(83%)、LLST(83%)和临终关怀(74%)的讨论记录。较少死者记录了有关姑息治疗咨询(49%)或首选死亡地点(36%)的讨论。对于所有五项临终关怀-质量指标,首次记录平均发生在死亡前 30 天以上:结论:很高比例的死者在死前30多天就达到了指定的临终关怀质量标准。我们的研究结果表明,NLP是衡量癌症儿童临终护理质量的一种可行方法,多中心研究和质量改进的时机已经成熟。
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6.20
自引率
4.80%
发文量
190
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