==== Front
Innov Aging
Innov Aging
innovateage
Innovation in Aging
2399-5300 Oxford University Press US 

10.1093/geroni/igaa057.112
igaa057.112
Abstracts
Session 2823 (Paper)
Services and Intervention
AcademicSubjects/SOC02600
Machine-Learning Modeling to Predict Hospital Readmission Following Discharge to Post-Acute Care Settings
Howard Elizabeth 1 Morris John N 2 Schachter Erez 3 1 
Boston College, Dover, Massachusetts, United States
2 
Hebrew SeniorLife, Boston, Massachusetts, United States
3 
Profility, Boston, Massachusetts, United States
2020 
16 12 2020 
16 12 2020 
4 Suppl 1 Program Abstracts from The GSA 2020 Annual Scientific Meeting “Turning 75: Why Age Matters”34 35
© The Author(s) 2020. Published by Oxford University Press on behalf of The Gerontological Society of America.2020This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.Abstract
Increased attention to post-acute care (PAC) settings and available services to meet patients’ needs following acute hospital discharge is needed as these settings are being utilized increasingly in models of care delivery. The primary purpose was to generate a model to identify the most predictive factors relevant to hospital readmission within 90 days following discharge to one of three types of PAC sites: home with home care services (HC), skilled nursing facility (SNF), in-patient rehabilitation facility (IRF). Specific aims were to (1) examine number and characteristics of older adults discharged to the 3 PAC sites; (2) compare 90 day hospital readmission rate across sites and acuity level; and (3) examine assessment items across population and subgroups to identify variables most predictive of hospital readmission. 2015 assessment data from 3,592,995 Medicare beneficiaries were analyzed representing 1,536,908 from SNFs, 306,878 from IRFs, and 1,749,209 receiving HC services. Total sample 90-day readmission was 25.8 % . Patients discharged to IRF had lowest readmission rate (23.34%), and those receiving HC services had highest readmission rate (29.34%). Creation of risk subgroups however, revealed alternative outcomes. Among all patients in the low, intermediate and high risk groups, the lowest readmission rates occurred among SNF patients. Factor analysis of assessment variables indicated bladder and bowel incontinence and functional limitations were the most distinguishing factors between the very low and very high risk subgroups.