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.

项目概要 阿尔茨海默氏病和相关痴呆症（ADRD）是一场迫在眉睫的公共卫生危机，大约影响 美国 500 万人和 11% 的老年人对患者在医疗保健中的转变进行了研究。 环境表明，患有慢性病的老年人很容易受到信息传递不足的影响， 让他们面临护理质量下降、用药错误和潜在可预防并发症的风险。 处于 ADRD 轨迹不同阶段的患者，尤其是患有多种慢性病的患者，可能会 在最近的一项研究中，我们开发了一种因护理环境中信息丢失而面临独特风险的方法。 包含 56,652 名患者的不同队列的纵向数据集（其中大部分年龄在 65 岁以上，患有多种合并症） 接受大型非营利家庭护理提供商提供的家庭医疗保健 (HHC) 服务 对于入院的患者。 2010-2012 年 HHC 期间，我们确定了 HHC 之前和之后诊断为 ADRD 的患者亚组 在之前的研究范围之外，还有一个巨大且很大程度上未经探索的数据源——护士的数据。 电子健康记录中捕获的自由文本临床记录大约有 100 万条护士的自由记录。 与研究人群相关的文本注释，有大量的潜在信息可供获取 在这项研究中，我们需要新的见解和创新方法来分析这种非结构化数据源。 提议使用自然语言处理（NLP）技术，这是一种系统分析自由文本的方法 利用先前研究中开发的数据集，本研究旨在： 1. 扩展和改进现有的NLP系统，自动识别文本中的以下信息 护理自由文本注释：(i) 了解患者先前已确诊 ADRD 诊断；(ii) 认知症状和相关患者/护理人员需求的观察；以及 (iii) 提及干预措施 来满足这些需求。 2. 对于 HHC 入院前没有 ADRD 诊断的患者，确定护士是否可以免费 NLP 中识别的认知症状的文本记录可以预测随后的 ADRD 诊断 在4年的随访期间。 3. 在 HHC 入院前诊断为 ADRD 的患者中，确定护士的自由文本是否 记录模式（例如，了解患者的 ADRD 诊断状态、对认知能力的观察） 症状和干预措施）预测：(i) 服务使用；以及 (ii) ADRD 造成的不良健康结果 患者有胃肠道风险（例如因尿路感染、脱水、跌倒而住院）。 这项研究将使我们能够检查 HHC 护士的做法，这些做法通常很难系统地观察， 并确定策略来满足 ADRD 患者和未确诊患者的复杂需求 这项研究的长期目标是开发一种家庭治疗方法。 旨在改善 ADRD 轨迹上患者和护理人员的生活质量的干预措施。