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

项目总结/摘要 到2060年，预计约有1400万成年人患有阿尔茨海默病和相关痴呆症 （ADRD）。虽然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患者的发病率明显较高， 住院和计划外30天再入院（以下简称“再入院”）的风险。再入院是 成本高昂，并使ADRD患者加速认知能力下降，过早住院和死亡。 出院后护理人员的可用性对于ADRD患者确保饮食依从性至关重要， 药物治疗和随访预约有一个缺乏证据审查重新接纳之间的 ADRD人群。大多数风险评估工具（如LACE指数）的区分能力较差， 包括有影响力的医学和社会特征，以及ADRD患者的护理人员可用性。一 一个潜在的解决方案是使用医院的电子健康记录（EHR）开发一个风险工具，因为它们 包含显著的临床和社会人口统计学特征以及来自医生的大量信息， 护士和社会工作者的笔记（非结构化EHR数据）。具体的研究目的就是为了提出这一建议 是（1）开发和验证用于预测ADRD患者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;以及（3）开发自然语言处理（NLP）算法来提取信息 非结构化EHR中的护理人员可用性（探索性）。我们假设预测能力 我们的风险工具将至少比LACE指数（密歇根州目前使用的风险工具）高出20 医院）。为了完成这个项目，我和我的导师们制定了一系列有针对性的职业目标 和教育培训。我的培训目标包括（1）熟悉ADRD的临床方面 （与研究目标1相关）;（2）掌握机器学习和预测的方法技能 建模（与研究目标1相关联）;（3）了解ADRD的物流 患者出院和护理过渡流程（与研究目标2相关）;以及（4）熟练掌握 在NLP和算法验证（与研究目标3链接）。完成这个奖项，我将使用 EHR和数据科学开发一种经验证的风险评估工具，用于因住院ADRD而再次入院 患者结果将使有效和有针对性的出院计划，以减少再入院和浪费 支出.它还将提供申请R 01所需的试验数据，以检查排放的优化 住院ADRD患者的流程/位置。这个职业发展奖将为我奠定基础 成为一名独特的健康经济学家，专门从事ADRD患者的有效护理过渡。

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.