Nurses' documentation of patient diagnoses, symptoms and interventions for home care patients with Alzheimer's Disease and related dementias: A natural language processing study

护士对患有阿尔茨海默病和相关痴呆症的家庭护理患者的患者诊断、症状和干预措施的记录：一项自然语言处理研究

• 批准号: 10056750
• 负责人: Miriam Ryvicker
• 金额: $ 26.07万
• 依托单位: VISITING NURSE SERVICE OF NEW YORK
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10056750
• 关键词: Address; Admission activity; Affect; Alzheimer' s disease patient; Alzheimer' s disease related dementia; Ambulatory Care; Assessment tool; Awareness; Care given by nurses; Caregivers; Caring; Chronic; Classification; Clinical; Clinical Nursing; Clinical assessments; Cognitive; Communication; Complex; Data; Data Set; Data Sources; Dehydration; Diagnosis; Diagnostic; Discipline of Nursing; Documentation; Early Diagnosis; Elderly; Electronic Health Record; Evaluation; Family; Family Caregiver; Foundations; Funding; Future; Goals; Health; Home Care Services; Home Health Care Agencies; Home environment; Hospitalization; Individual; Intervention; Knowledge; Link; Machine Learning; Medical Care Team; Medicare claim; Medication Errors; Methods; Natural Language Processing; Neurobehavioral Manifestations; New York; Nurses; Nursing Homes; Nursing Services; Outcome; Pathway interactions; Patients; Pattern; Performance; Population; Positioning Attribute; Provider; Public Health; Quality of Care; Quality of life; Research; Risk; Risk Management; Services; Symptoms; System; Techniques; Testing; Text; Time; United States; Urinary tract infection; Visiting Nurse; Work; aged; base; care coordination; care providers; cohort; comorbidity; ethnic diversity; experience; falls; follow-up; health care service; health care settings; hospice environment; improved; innovation; insight; longitudinal dataset; multiple chronic conditions; patient home care; patient subsets; racial and ethnic; study population; unstructured data

PROJECT SUMMARY Alzheimer's disease and related dementias (ADRD) represent a looming public health crisis, affecting roughly 5 million people and 11% of older adults in the United States. Studies on patient transitions across healthcare settings suggests that older adults with chronic conditions are vulnerable to inadequate transfer of information, putting them at risk for diminished quality of care, medication errors and potentially preventable complications. Patients at varied stages along the ADRD trajectory – especially those with multiple chronic conditions – may experience unique risks due to the loss of information across care settings. In a recent study, we developed a longitudinal dataset on a diverse cohort of 56,652 patients – mostly aged 65+ with multiple comorbidities – receiving home health care (HHC) services from a large non-profit home care provider. For patients admitted to HHC in 2010-2012, we identified subgroups of patients with ADRD diagnoses made prior to and after HHC admission. Outside the scope of this prior study remains a vast and largely unexplored data source – nurses’ free-text clinical notes captured in the electronic health record. With roughly 1 million entries of nurses’ free- text notes associated with the study population, there is a wealth of potential information from which to gain new insights and a need for innovative methods to analyze this unstructured data source. In this study, we propose to use natural language processing (NLP) techniques, a method for systematically analyzing free-text content that draws upon machine learning. Using the dataset developed in the prior study, this study aims to: 1. Expand and improve an existing NLP system to automatically identify the following information in the nursing free-text notes: (i) knowledge of the patient having a previously established ADRD diagnosis; (ii) observations of cognitive symptoms and related patient/caregiver needs; and (iii) mentions of interventions to address these needs. 2. For patients who do not have an ADRD diagnosis prior to HHC admission, determine whether nurses’ free- text documentation of cognitive symptoms identified in the NLP predict subsequent ADRD diagnoses during the 4-year follow-up period. 3. Among patients diagnosed with ADRD prior to HHC admission, determine whether nurses’ free-text documentation patterns (e.g. knowledge of the patient’s ADRD diagnostic status, observations of cognitive symptoms, and interventions) predict: (i) service use; and (ii) adverse health outcomes for which ADRD patients are at heightened risk (e.g. hospitalizations due to urinary tract infection, dehydration, falls). This study will allow us to examine HHC nurses’ practices, which are often difficult to observe systematically, and identify strategies to address the complex needs of their patients with ADRD and un-diagnosed patients who may be on a path toward ADRD diagnosis. The long-term goal of this research is to develop a home- based intervention that aims to improve quality of life for patients and caregivers along the ADRD trajectory.

项目摘要 阿尔茨海默氏病和相关痴呆症（ADRD）代表着迫在眉睫的公共卫生危机，影响了大致影响 美国有500万人和11％的老年人。研究跨医疗保健的患者过渡的研究 设置表明，患有慢性疾病的老年人很容易受到信息传递不足的影响， 使他们有降低护理质量，药物错误和可能预防的并发症的风险。 沿着Adrd轨迹的各个阶段的患者（尤其是患有多个慢性病的患者）可能 由于跨护理设置的信息丢失，经历独特的风险。在最近的一项研究中，我们开发了 在56,652名患者的潜水员队列上的纵向数据集 - 大多数65岁以上具有多种合并症 - 从大型非营利性家庭护理提供商那里获得家庭医疗保健（HHC）服务。适用于录取的患者 在2010年至2012年的HHC上，我们确定了在HHC之前和之后进行的ADRD诊断患者的亚组 入场。在这项先前研究的范围之外，这仍然是一个广阔的意外数据来源 - 护士的 电子健康记录中捕获的自由文本临床笔记。大约有100万护士的自由活动 与研究人群相关的文本说明，有大量的潜在信息可以从中获得 新的见解和需要创新方法来分析此非结构化数据源。在这项研究中，我们 使用自然语言处理（NLP）技术的建议，一种系统分析自由文本的方法 利用机器学习的内容。使用先前研究中开发的数据集，本研究的目的是： 1。扩展和改进现有的NLP系统，以自动识别以下信息 护士自由文本注释：（i）对先前建立的ADRD诊断的患者知识； （ii） 观察认知症状和相关患者/护理人员的需求； （iii）提及干预措施 解决这些需求。 2。对于在HHC入院之前没有ADRD诊断的患者，请确定护士是否自由 NLP中确定的认知症状的文本文档预测随后的ADRD诊断 在4年的随访期内。 3。在HHC入院之前被诊断为ADRD的患者中，确定护士的自由文本是否 文档模式（例如，了解患者的ADRD诊断状态，认知观察 符号和干预措施）预测：（i）服务使用； （ii）ADRD的不利健康结果 患者的风险增加（例如，由于尿路感染，脱水，下降而导致的住院）。 这项研究将使我们能够检查HHC护士的实践，这些实践通常很难系统地观察到 并确定策略以满足ADRD和未诊断患者患者的复杂需求 谁可能正走向ADRD诊断。这项研究的长期目标是开发房屋 - 旨在改善ADRD轨迹的患者和护理人员的生活质量的基于干预措施。