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: 10219952
• 负责人: Miriam Ryvicker
• 金额: $ 21.35万
• 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 2022-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10219952
• 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; Home Care Services; Home Health Care Agencies; 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 阿尔茨海默病和相关痴呆症（ADRD）代表了一个迫在眉睫的公共卫生危机， 500万人和11%的老年人在美国。关于患者在医疗保健中的转变的研究 环境表明，患有慢性病的老年人容易受到信息传递不足的影响， 使他们面临护理质量下降、用药错误和可能预防的并发症的风险。 处于ADRD轨迹沿着不同阶段的患者-尤其是患有多种慢性疾病的患者-可能 由于护理环境中的信息丢失，会遇到独特的风险。在最近的一项研究中，我们开发了一种 一个包含56，652例患者的不同队列的纵向数据集-大多数年龄为65岁以上，患有多种合并症- 接受大型非营利家庭护理提供者的家庭保健（HHC）服务。对于入院的患者 在2010-2012年的HHC中，我们确定了在HHC之前和之后诊断为ADRD的患者亚组 入院在这项先前研究的范围之外，仍然有一个巨大的，在很大程度上未开发的数据源-护士的 在电子健康记录中捕获的自由文本临床笔记。大约有100万名护士免费注册- 文本注释与研究人群相关，从中可以获得丰富的潜在信息 新的见解和创新的方法来分析这种非结构化数据源的需求。本研究 我建议使用自然语言处理（NLP）技术，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，本研究旨在： 1.扩展和改进现有的NLP系统，以自动识别 护理自由文本笔记：（i）患者先前已确诊ADRD诊断的知识;（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.对于在HHC入院前没有ADRD诊断的患者，确定护士是否免费- NLP中识别的认知症状的文本文件可预测随后的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，患者ADRD诊断状态的知识，认知功能的观察结果） 症状和干预措施）预测：（i）服务使用;和（ii）ADRD的不良健康结果 患者的风险较高（例如，由于尿路感染、脱水、福尔斯而住院）。 这项研究将使我们能够检查HHC护士的做法，这往往是难以系统地观察， 并确定策略，以解决其ADRD患者和未确诊患者的复杂需求 可能会被诊断为ADRD这项研究的长期目标是建立一个家庭- 基于干预，旨在改善患者和护理人员沿着ADRD轨迹的生活质量。

项目成果

HomeADScreen: Developing Alzheimer's disease and related dementia risk identification model in home healthcare.

HomeADScreen：在家庭医疗保健中开发阿尔茨海默病和相关痴呆症风险识别模型。

• DOI: 10.1016/j.ijmedinf.2023.105146
• 发表时间: 2023
• 期刊: International journal of medical informatics
• 影响因子: 4.9
• 作者: Zolnoori,Maryam;Barrón,Yolanda;Song,Jiyoun;Noble,James;Burgdorf,Julia;Ryvicker,Miriam;Topaz,Maxim
• 通讯作者: Topaz,Maxim

Prevalence and predictors of incident ADRD diagnosis following a Medicare home health episode.

Medicare 家庭健康事件后 ADRD 诊断事件的患病率和预测因素。

• DOI: 10.1002/alz.13061
• 发表时间: 2023
• 期刊: Alzheimer's & dementia : the journal of the Alzheimer's Association
• 作者: Burgdorf,JuliaG;Mroz,TracyM;Reckrey,JenniferM;Barrón,Yolanda;Ryvicker,Miriam
• 通讯作者: Ryvicker,Miriam

Free-Text Documentation of Dementia Symptoms in Home Healthcare: A Natural Language Processing Study.

• DOI: 10.1177/2333721420959861
• 发表时间: 2020-01
• 期刊: Gerontology & geriatric medicine
• 影响因子: 2.7
• 作者: Topaz M;Adams V;Wilson P;Woo K;Ryvicker M
• 通讯作者: Ryvicker M