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

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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.