Using Machine Learning to Improve Readmission Prediction in Alzheimer's Disease and Related Dementia

使用机器学习改善阿尔茨海默病和相关痴呆症的再入院预测

基本信息

批准号：
10672377
负责人：
Elham Mahmoudi
金额：
$ 13.01万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-15 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10672377
关键词：
Address Adherence Adult Algorithms Alzheimer&apos s disease patient Alzheimer&apos s disease related dementia American Appointment Artificial Intelligence Assessment tool Award Caregivers Caring Cessation of life Clinical Cognitive Communities Data Data Science Diet Discharge Plannings Discrimination Economics Elderly Electronic Health Record Emergency department visit Ensure Familiarity Foundations General Population Goals Health Health Expenditures Healthcare Hospitalization Hospitals Impaired cognition Influentials Institutionalization Intervention K-Series Research Career Programs Length of Stay Link Location Logistics Machine Learning Measurable Medical Medical History Medicine Mentors Mentorship Methodology Michigan Natural Language Processing Nurses Outcome Patient Care Patient Discharge Patients Persons Pharmaceutical Preparations Physicians Population Predictive Value Process Research Research Personnel Resource-limited setting Risk Risk Assessment Risk Factors Risk Reduction Sensitivity and Specificity Services Social Workers Socioeconomic Status Source Stress Training Training and Education United States Centers for Medicare and Medicaid Services Validation analytical method career comorbidity cost electronic health record system electronic structure follow-up frailty health data high risk hospital readmission improved indexing innovation machine learning prediction multidisciplinary patient population predictive tools premature readmission risk skills social sociodemographics statistics tool unstructured data

项目摘要

Project Summary/Abstract By 2060, approximately 14 million adults are expected to live with Alzheimer’s disease and related dementia (ADRD). Although ADRD patients represent 10% of the general geriatric population, they account for 37% of the direct healthcare expenditures. Compared to other older adults, ADRD patients are at a significantly higher risk of hospitalization and unplanned 30-day hospital readmission (hereafter “readmission”). Readmissions are costly and expose ADRD patients to expedited cognitive decline, premature institutionalization, and death. Availability of a caregiver after hospital discharge is critical for ADRD patients to ensure adherence to diet, medications, and follow-up appointments. There is a paucity of evidence examining readmission among the ADRD population. Most risk-assessment tools (e.g. LACE Index) have poor discrimination power and lack inclusion of influential medical and social features, and caregiver availability particular to ADRD patients. A potential solution is to develop a risk tool using hospitals’ electronic health records (EHRs) because they contain salient clinical and sociodemographic features as well as a wealth of information from physicians’, nurses’ and social workers’ notes (unstructured EHRs data). The specific research aims for this proposal are to (1) develop and validate a risk-assessment tool for predicting readmission among ADRD patients; (2) examine the feasibility/acceptability and clinical/economic utility of the readmission risk- assessment tool; and (3) develop a natural language processing (NLP) algorithm to extract information on caregiver availability from unstructured EHRs (exploratory). We hypothesize that the predictive power of our risk tool will be at least 20% higher than that of LACE Index (the current risk tool used in the Michigan Medicine hospitals). To accomplish this project, my mentors and I have defined a set of targeted career goals and educational training. My training aims include (1) gain familiarity with the clinical aspects of ADRD (linked with Research Aim 1); (2) acquire methodological skills in machine learning and predictive modeling (linked with Research Aim 1); (3) develop an understanding of the logistics of the ADRD patient discharge and care transition processes (linked with Research Aim 2); and (4) gain proficiency in NLP and algorithm validation (linked with Research Aim 3). By completion of this award, I will have used EHRs and data science to develop a validated risk-assessment tool for readmission for hospitalized ADRD patients. The results will enable efficient and targeted discharge planning to reduce readmission and wasteful spending. It will also provide pilot data needed to apply for an R01 examining the optimization of discharge process/location for hospitalized ADRD patients. This career development award will lay the foundation for me to become a unique health economist specialized in efficient care transitions for ADRD patients.

项目摘要/摘要到2060年，预计大约有1400万成年人将患有阿尔茨海默氏病和相关痴呆症（ADRD）。尽管ADRD患者占一般老年人群的10％，但他们占37％直接医疗支出。与其他老年人相比，ADRD患者处于明显更高住院的风险和计划外30天的医院再入院（以下“再入院”）。再入院是代价高昂，使ADRD患者加快认知能力下降，早产制度化和死亡。住院后护理人员的可用性对于ADRD患者至关重要，以确保遵守饮食，药物和后续约会。有大量证据检查了 ADRD人口。大多数风险评估工具（例如蕾丝指数）具有差异能力，缺乏歧视能力包括有影响力的医疗和社会特征，以及ADRD患者特别的护理人员的可用性。一个潜在的解决方案是使用医院的电子健康记录（EHR）开发风险工具，因为它们包含显着的临床和社会人口统计学特征以及医生的大量信息护士和社会工作者的注释（非结构化的EHRS数据）。具体研究目的是该提案是（1）开发和验证一种用于预测ADRD再入院的风险评估工具患者; （2）检查再入院风险的可行性/可接受性和临床/经济效用 - 评估工具；（3）开发自然语言处理（NLP）算法以提取信息关于非结构化EHR的护理人员的可用性（探索性）。我们假设预测能力我们的风险工具将比蕾丝指数高20％（密歇根州当前使用的风险工具医学医院）。为了完成这个项目，我和我的导师定义了一套针对性的职业目标和教育培训。我的培训目的包括（1）熟悉ADRD的临床方面（与研究目标1相关）；（2）获得机器学习和预测性的方法论技巧建模（与研究目标1相关）；（3）对ADRD的物流有所了解患者出院和护理过渡过程（与研究目标2相关）；（4）提高能力在NLP和算法验证中（与研究目标3相关）。通过完成此奖项，我将使用 EHR和数据科学开发了经过验证的风险评估工具，以重新入院患者。结果将使有效且有针对性的排放计划能够减少再入院和浪费开支。它还将提供申请R01的试点数据，以检查排放的优化住院ADRD患者的过程/位置。这个职业发展奖将为我奠定基础成为一种独特的健康经济，专门针对ADRD患者进行有效的护理过渡。