Development of a Screening Algorithm for Timely Identification of Patients with Mild Cognitive Impairment and Early Dementia in Home Healthcare

开发一种筛查算法，以便在家庭医疗保健中及时识别轻度认知障碍和早期痴呆症患者

• 批准号: 10591016
• 负责人: Maryam Zolnoori
• 金额: $ 11.51万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-15 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10591016
• 关键词: Accident and Emergency department; Address; Affect; Age; Algorithms; Asthma; Attention; Biological Markers; Biometry; Care given by nurses; Caring; Cerebrospinal Fluid; Clinical; Clinical Data; Cognitive; Communication; Competence; Data; Data Science; Data Set; Dedications; Dementia; Development; Diagnosis; Early Diagnosis; Early identification; Elderly; Electronic Health Record; Emergency department visit; Evaluation; Family; Goals; Healthcare Systems; Home Care Services; Hospitalization; Impaired cognition; Informatics; Intervention; Language; Life Style; Medical Care Team; Memory impairment; Mentorship; Methods; Modeling; Motor; National Institute on Aging; Natural Language Processing; New York; Nurses; Nursing Services; Outcome; Patient Admission; Patient Care; Patients; Performance; Pharmaceutical Preparations; Phase; Phonetics; Pilot Projects; Postdoctoral Fellow; Property; Public Health; Quality of life; Recording of previous events; Research; Research Personnel; Research Priority; Resources; Risk; Risk Factors; Risk Reduction; Scientist; Screening procedure; Semantics; Services; Speech; Structure; Symptoms; System; Systems Analysis; Techniques; Testing; Text; Time; Training; Universities; Visiting Nurse; Work; age related; care costs; career development; clinical care; clinical encounter; cognitive function; cost; daily functioning; data streams; early detection biomarkers; electronic health record system; executive function; health care service; health care service utilization; health care settings; improved; innovation; language impairment; mild cognitive impairment; motor disorder; novel; older patient; pre-doctoral; prevent; programs; provider communication; screening; showing emotion; skills; theories; verbal

Mild cognitive impairment (MCI) and early-stage dementia (ED) have a significant impact on elderly patients’ quality of life, healthcare utilization, and cost. Despite nationwide efforts for timely diagnosis of MCI and ED (MCI-ED), more than 50% of patients remained underdiagnosed and undertreated. This is mostly due to patients’ inability to recognize early symptoms, limited availability of biomarkers, and clinicians’ insufficient time to assess patients for MCI-ED, particularly for patients admitted to the home healthcare (HHC) setting. This K99/R00 will address barriers to early identification of MCI-ED via the development of an innovative algorithm built on a combination of multiple data streams, including data extracted from electronic health records (EHRs) and audio-recorded patient-clinician verbal communication during routine encounters. Our primary goal is to utilize the routinely generated data in the HHC setting, including OASIS (Outcome and Assessment Information Set - a federally required assessment of patients admitted to HHC), HHC nurses’ notes, and HHC patient-nurse verbal communication to develop an MCI-ED screening algorithm. The long-term training goal is for Dr. Zolnoori to become an independent investigator conducting a program of research dedicated to mitigating the burden of delayed care for patients with MCI-ED by developing low-cost, effective informatics solutions. The solutions will take advantage of easily accessible data generated in clinical encounters and will be built on novel data science methods, particularly speech analysis, the focus of her postdoctoral work. Using exceptional resources available from Columbia University and the Visiting Nurse Service of New York, the K99 phase of this project will focus on gaining essential competencies and skills in theory and practice of speech analysis and cognitive impairment to quantify properties of MCI-ED patients’ verbal communications in interactions with HHC nurses. The R00 phase will focus on the development of a screening algorithm for the early identification of MCI-ED. The specific aims are to 1) model MCI-ED patients’ verbal communications with HHC nurses using an automated speech analysis system; 2) utilize existing natural language processing algorithms to automatically identify MCI-ED related information, including i) clinical symptoms, ii) lifestyle risk factors, and iii) communication deficits from both HHC clinical notes and patient-nurse verbal communication; and 3) develop a sensitive screening algorithm to identify HHC patients with MCI-ED. To accomplish the research aims and training goals, an interdisciplinary team of scientists with expertise in speech analysis, cognitive impairment, HHC services, biostatistics, and career development mentorship has been assembled. This project is significant because this algorithm will be built on easily accessible data streams generated during routine patient-nurse encounters. The algorithm has a strong potential to be integrated into HHC clinical workflow to raise clinician’s attention to the patient’s cognitive functioning for further evaluation and development of proper interventions to reduce the risk of negative outcomes.

轻度认知功能障碍（MCI）和早期痴呆（艾德）对老年患者的认知功能有显著影响， 生活质量、医疗保健利用率和成本。尽管全国努力及时诊断MCI和艾德 （MCI-ED），超过50%的患者仍然诊断不足和治疗不足。这主要是由于 患者无法识别早期症状，生物标志物的可用性有限，临床医生的时间不足 评估MCI-ED患者，特别是接受家庭医疗保健（HHC）的患者。这 K99/R 00将通过开发创新算法解决早期识别MCI-ED的障碍 基于多个数据流的组合，包括从电子健康记录（EHR）中提取的数据 以及在常规遭遇期间的音频记录的患者-临床医生口头交流。我们的首要目标是 利用HHC环境中常规生成的数据，包括OASIS（结果和评估 信息集-联邦政府要求的对HHC收治患者的评估）、HHC护士记录和HHC 患者-护士口头沟通，以开发MCI-ED筛查算法。长期培训目标是 让Zolnoori博士成为一名独立的研究人员，开展一项致力于 通过开发低成本、有效的信息学，减轻MCI-ED患者延迟护理的负担 解决方案这些解决方案将利用临床诊疗中生成的易于访问的数据， 建立在新颖的数据科学方法，特别是语音分析，她的博士后工作的重点。使用 哥伦比亚大学和纽约探访护士服务处提供的特殊资源， 本项目的第一阶段将侧重于获得演讲理论和实践方面的基本能力和技能 分析和认知障碍，以量化MCI-ED患者的语言交流特性， 与HHC护士的互动。R 00阶段将侧重于开发一种用于 MCI-ED的早期识别。具体目标是：1）模拟MCI-ED患者的语言交流， HHC护士使用自动语音分析系统; 2）利用现有的自然语言处理 自动识别MCI-ED相关信息的算法，包括i）临床症状，ii）生活方式风险 因素，和iii）从HHC临床笔记和病人-护士口头沟通的沟通缺陷; 以及3）开发敏感的筛查算法以识别患有MCI-ED的HHC患者。 研究目标和培训目标，一个跨学科的科学家团队，在语音分析方面的专业知识， 认知障碍，HHC服务，生物统计学，和职业发展指导已经组装。 这个项目意义重大，因为这个算法将建立在易于访问的数据流上生成 在日常的病人和护士会面中该算法具有很强的潜力，可集成到HHC临床 工作流程，以提高临床医生对患者认知功能的关注，以进行进一步评估， 制定适当的干预措施，以减少负面结果的风险。