Lived Experiences Versus Artificial Intelligence to Reduce Hospital Readmission: A Qualitative Study
Elham Mahmoudi

TL;DR
This study explores why hospital readmissions for dementia patients remain high and finds that AI models miss key human and systemic factors, suggesting solutions like specialized care and better support for caregivers.
Contribution
The study uniquely combines qualitative insights from caregivers and hospital staff to identify gaps in AI-driven readmission reduction efforts for dementia patients.
Findings
Caregiver burden and lack of support are major contributors to hospital readmissions for dementia patients.
Specialized dementia care protocols and accessible home care services are urgently needed to reduce readmissions.
Emergency departments are often the only option for caregivers when home care becomes unmanageable.
Abstract
Frequent hospitalization including hospital readmissions are expensive and harmful to older adults, particularly those with dementia. Despite efforts to predict and reduce hospital readmissions through novel applications of machine learning (ML), readmission remains elusive. This study stemmed from our prior unsuccessful work and work of others attempting to use ML to predict and, by acting upon that, reduce hospital readmission among people with dementia. To understand what was missing in our ML models, we used a qualitative descriptive study and interviewed 21 participants (n = 11 caregivers of people with dementia; n = 10 hospital staff including nurses, discharge planners, and social workers). Participants took part in a semi-structured interview. To analyze the data, we used thematic text analysis to identify overarching themes. We identified five themes related to high risk of…
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Taxonomy
TopicsHeart Failure Treatment and Management · Frailty in Older Adults · Intensive Care Unit Cognitive Disorders
