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.

项目总结