Remote Patient Monitoring of Family Caregivers of Patients with Alzheimer's Disease

阿尔茨海默病患者家庭护理人员的远程患者监控

基本信息

批准号：
10253642
负责人：
Jonathan Nicolla
金额：
$ 29.98万
依托单位：
PREPPED HEALTH, LLC
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-30 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10253642
关键词：
Acute Address Algorithmic Analysis Algorithms Alzheimer&apos s Disease Alzheimer&apos s disease patient Alzheimer&apos s disease related dementia Assessment tool Behavioral Behavioral Symptoms COVID-19 pandemic Caregiver well-being Caregivers Caring Cellular Phone Clinic Visits Clinical Clinical assessments Communication Data Data Analyses Data Reporting Delirium Dementia Development Devices Distress Drug Prescriptions Elements Emotional Equipment Evaluation Family Caregiver Family member Financial cost Fright Health Health Care Costs Health system Home Home visitation Hospital Costs Hospitalization Hospitals Impaired cognition Individual Infection Infrastructure Intervention Interview Knowledge Lead Machine Learning Measures Medical emergency Methodology Methods Online Systems Participant Patient Monitoring Patient-Focused Outcomes Patients Persons Pharmacology Phase Pilot Projects Process Qualitative Methods Qualitative Research Reaction Reporting Research Methodology Risk Savings Site Small Business Technology Transfer Research Sorting - Cell Movement Structure Surveys System Testing Time Universities Update Viral Visit Walking adverse drug reaction adverse outcome base cost dashboard data visualization design digital efficacy testing emotional symptom experience experimental study family support follow up assessment health assessment high risk improved machine learning algorithm multi-site trial physical symptom prediction algorithm preference prevent primary caregiver primary outcome product development programs prospective prototype remote patient monitoring response secondary infection secondary outcome software development statistics tool usability user centered design

项目摘要

PROJECT SUMMARY Patients with Alzheimer’s disease & related dementia (ADRD) experience 400,000 avoidable hospitalizations annually, amounting to $5.4 billion in preventable healthcare costs. In addition to financial costs, avoidable hospitalizations increase risks for adverse outcomes, such as secondary infections, deliriums, or acute distress. To prevent hospitalizations, clinicians rely on caregivers of patients with ADRD to report any physical, behavioral, and emotional changes that caregivers observe. If communicated in a timely manner, many such changes can be addressed without hospitalization, but through pharmacological interventions, home visits, or clinic visits. Currently, communication between clinicians and family caregivers depends on the caregiver knowing when to call, and clinicians sorting through messages of various importance to find and address worrisome changes associated with an impending hospitalization. This strategy is insufficient, as it delays communication of potentially significant changes in patients with ADRD to the clinician. It also relies on caregivers’ ability to distinguish innocuous changes from those that can lead to hospitalizations. To minimize occurrence of avoidable hospitalizations in patients with ADRD, we will develop and test the first remote patient-monitoring platform, Digital Outpost, based on caregiver-reported information. The platform will contain two parts: the native Caregiver App and web-based Clinician Action Dashboard. Guided by user-centered design principles and rapid qualitative research methods, we will query 12 caregivers of patients with ADRD regarding content, look, feel, and experience of the Caregiver App. Using input of 20 clinicians through Delphi and discrete choice methodology, we will define the daily clinical surveys through which caregivers will report physical, behavioral, and emotional changes in patients with ADRD. We will also define the algorithms for analyzing and displaying caregiver-reported data in the Clinician Action Dashboard. Then, we will develop the supporting platform prototype using Agile/Kanban-based Software Development methodology and conduct a 14-day pilot study of Digital Outpost with ten primary caregivers of patients with moderate ADRD. Primary outcome of the pilot will be usability, measured by the System Usability Scale, with a follow-up assessment through rapid qualitative methods. Secondary outcome will be feasibility, measured through platform usage statistics. Upon completion, we will be poised to update the platform to include integration of other smartphone data (e.g. walking steps), use a machine-learning based predictive algorithm for hospitalization, and test efficacy of Digital Outpost in reducing all-cause hospitalization through multi-site trial in a Phase II STTR application.

项目概要阿尔茨海默氏病及相关痴呆症 (ADRD) 患者有 400,000 次本可避免的住院治疗每年，除了可避免的财务成本外，可预防的医疗费用高达 54 亿美元。住院治疗会增加不良后果的风险，例如继发感染、谵妄或急性应激。为了防止住院，请依赖 ADRD 患者的护理人员报告任何身体、行为、如果及时沟通，许多此类变化都可以发生。无需住院即可解决，而是通过药物干预、家访或诊所就诊。目前，教区居民和家庭照顾者之间的沟通取决于照顾者知道何时呼叫，并对各种重要的消息进行排序，以查找和解决令人担忧的变化这种策略是不够的，因为它延迟了沟通。 ADRD 患者对临床医生而言可能发生的重大变化还取决于护理人员的能力。区分无害的变化和可能导致住院的变化，以尽量减少可避免的情况的发生。 ADRD 患者住院，我们将开发和测试第一个远程患者监测平台，数字前哨站，基于护理人员报告的信息该平台将包含两部分：本地。护理人员应用程序和基于网络的临床医生操作仪表板以用户为中心的设计原则和快速指导。定性研究方法，我们将询问 ADRD 患者的 12 名护理人员的内容、外观、感觉、以及通过 Delphi 和离散选择输入 20 个护理者应用程序的经验。方法，我们将定义每日临床调查，护理人员将通过这些调查报告身体、行为、我们还将定义分析和显示 ADRD 患者的情绪变化。然后，我们将开发支持平台。使用基于敏捷/看板的软件开发方法进行原型设计，并进行为期 14 天的试点研究数字前哨站由 10 名中度 ADRD 患者的主要护理人员组成，该试点的主要结果将是。可用性，通过系统可用性量表进行衡量，并通过快速定性进行后续评估次要结果将是通过平台使用统计数据衡量的可行性。我们将准备更新该平台，以包含其他智能手机数据的集成（例如步行步骤），使用基于机器学习的住院预测算法，并测试 Digital Outpost 在减少住院方面的功效通过 II 期 STTR 应用中的多中心试验进行全因住院治疗。