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

轻度认知障碍(MCI)和早期痴呆(ED)对老年患者的影响显著 生活质量、医疗保健利用率和成本。尽管全国都在努力及时诊断MCI和ED (MCI-ED)，50%以上的患者仍未得到充分诊断和治疗。这主要是因为 患者无法识别早期症状，生物标记物的可获得性有限，以及临床医生时间不足 评估患者的MCI-ED，特别是进入家庭医疗(HHC)环境的患者。这 K99/R00将通过开发创新的算法来解决早期识别MCI-ED的障碍 构建于多个数据流的组合之上，包括从电子健康记录(EHR)提取的数据 以及在常规会面期间记录的患者与临床医生的口头交流。我们的主要目标是 在HHC环境中使用常规生成的数据，包括OASIS(结果和评估 信息集-联邦要求的HHC患者评估)、HHC护士笔记和HHC 病人与护士之间的语言交流，以开发MCI-ED筛查算法。长期的培训目标是 佐尔努里博士成为一名独立调查员，进行一项致力于 通过发展低成本、有效的信息学减轻MCI-ED患者延迟护理的负担 解决办法。这些解决方案将利用在临床会诊中生成的易于访问的数据，并将 建立在新的数据科学方法上，特别是语音分析，这是她博士后工作的重点。vbl.使用 来自哥伦比亚大学和纽约访问护士服务的特殊资源，K99 这一阶段的项目将侧重于获得演讲理论和实践方面的基本能力和技能 MCI-ED患者言语交流特点的认知障碍量化分析 与HHC护士的互动。R00阶段将重点开发一种筛选算法 MCI-ED的早期识别。具体目标是1)模拟MCI-ED患者的言语交流 使用自动语音分析系统的HHC护士；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，以及iii)HHC临床记录和病人-护士言语交流的沟通障碍； 3)开发一种灵敏的筛查算法来识别HHC合并MCI-ED患者。要完成以下任务 研究目标和培训目标，拥有语音分析专业知识的跨学科科学家团队， 认知障碍、HHC服务、生物统计学和职业发展指导已汇集在一起。 该项目意义重大，因为该算法将建立在生成的易于访问的数据流上 在例行公事的病人和护士见面时。该算法具有很强的应用于临床的潜力 提高临床医生对患者认知功能的关注以进行进一步评估和 制定适当的干预措施，以减少负面结果的风险。

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.