Using electronic medical records to develop a predictive model of 30‐day hospital readmission for people with ADRD
نویسندگان
چکیده
Background Hospitals are insufficiently equipped for Alzheimer’s disease and related dementia (ADRD) patients. Thus, readmission incidence is much higher costlier among ADRD patients than the general population. Hospital discharges often occur without adequate preparation complex care management needs of their caregivers. This study’s objective was to develop a risk-assessment tool hospitalized with ADRD. By supporting timely better identification who at risk why, already-scarce resources can be allocated more efficiently reduce readmissions. Methods We used 2016-2019 EMR data from University Michigan health system (Michigan Medicine) applied machine learning techniques (Random Forest, XGBoost, Logistic LASSO) tool. identified 2,899 individuals had least one index hospital admission. All features available in – demographics, lab results, prior counts healthcare use, characteristics hospitalization were included our predictive models. Additionally, we geocoded street address place residence using National Neighborhood Data Archive (NaNDA) U.S. Census tract-level information include two composite measures socioeconomic status: disadvantage affluence. Results The rate 22% versus 17% best model Random Forest (area under receiver operating characteristic curve = 0.66; sensitivity 0.64; specificity 0.61). accuracy (0.61) 42% LACE score (0.43), which currently by all top 5 predictors 30-day people length stay, frailty index, living disadvantaged neighborhood, total prior-year charges. Conclusion highly vulnerable require many resources, substantially greater elevated rates other adverse events. Leveraging help inform appropriate efficient coordination transitions Our identify high why they risk. enable decision-making upon discharge.
منابع مشابه
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ژورنال
عنوان ژورنال: Alzheimers & Dementia
سال: 2023
ISSN: ['1552-5260', '1552-5279']
DOI: https://doi.org/10.1002/alz.064722