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A qualitative descriptive study interviewed 21 participants, including dementia caregivers and hospital staff, using thematic text analysis to identify five drivers of high readmission risk. Care at home often becomes unfeasible without accessible dementia-specific outpatient care and respite centers, prompting reliance on emergency departments and hospitals. Recommendations include dementia-specialized wards, inpatient/discharge protocols with caregiver education, at-home follow-up, and affordable home care services.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/lived-experiences-versus-artificial-intelligence-to-reduce-hospital-readmission-a-qualitative-study/426925/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/lived-experiences-versus-artificial-intelligence-to-reduce-hospital-readmission-a-qualitative-study/426925.png","ImageObject",300,407,{"name":92,"@type":93},"OmBimo","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-19","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why did the study focus on qualitative methods instead of relying solely on machine learning?","Question",{"text":112,"@type":113},"Machine learning models have not successfully predicted and reduced hospital readmission for people with dementia. The study used qualitative interviews to identify what was missing in ML approaches.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What participant groups were interviewed in the qualitative study?",{"text":117,"@type":113},"The study interviewed 21 participants, including 11 caregivers of people with dementia and 10 hospital staff such as nurses, discharge planners, and social workers.",{"name":119,"@type":110,"acceptedAnswer":120},"What recommendations did caregivers and hospital staff propose to reduce readmission?",{"text":121,"@type":113},"Recommendations included dementia-specialized hospital wards, dementia-specific inpatient/discharge protocols with educational materials for caregivers, at-home follow-up visits, and accessible and affordable home care services.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},426925,1790789785,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":81},962090893581,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","436 Innovation in Aging, 2025, Vol. 9, No. S2  \ninterviews, n = 20), and an expert panel (n = 4) . Validity was assessed using the Old Caregiver Motive Scale (for convergent validity) and the Caregiver Preparedness Scale (for divergent validity). Items with a factor loading above 0.45 were retained. Principal Axis Factoring identified two distinct factors:  \n(1) Intrinsic motivation (e.g., love, emotional connection, appreciation, sense of purpose) and (2) Obligatory motivation (e.g., external expectations, duty, responsibility, lack of choice) . Nine items loaded onto Factor 1, and four onto Factor 2. The CMS demonstrated good psychometric properties (S-CVI = 1, Cronbach’s alpha = 0.764, Kaiser’s MSA = 0.875, RMSR = 0.046) and serves as a valid and reliable measure of caregiver motivation, distinguishing between intrinsic and obligatory motivations. Future research should explore its use in longitudinal studies and diverse caregiving populations.  \nAbstract citation ID: igaf122.1485  \nENGAGING A COMMUNITY COUNCIL FOR LATINO DEMENTIA RESEARCH: FINDINGS FROM A  \nMIXED-METHODS STUDY  \nSara Masoud1, Janna Lesser1, Juana Escareno1,  \nBelinda Flores1, Byeong Yeob Choi1, Carole White1, and Karla Daniela López-Lorenzo2, 1. UT Health San Antonio, San Antonio, Texas, United States, 2. Texas A&M University, College Station, Texas, United States  \nLatino communities are disproportionately affected by Alzheimer’s disease, yet they remain underrepresented in dementia research. To address this issue in alignment with community engaged frameworks, we formed a Steering Council (SC) (N = 16) of caregivers, individuals living with dementia, community health workers, health and social service providers, and researchers. The SC worked with the study team to design and implement six “community pláticas”among different South Texas communities. These gatherings were carefully designed to integrate culturally-specific components (e.g., music, food, language translation), with a key component being reciprocal group discussions aimed at identifying research priorities and patient-centered outcomes. To assess whether strategies to engage the SC were effective, an explanatory sequential mixed-methods design study was employed. The Research Engagement Survey Tool (REST) was administered at three time points, revealing statistically significant increases in quality of engagement over time (p \u003C 0.05 across multiple engagement principles) and strengthened levels of engagement (collaboration and partnership). Additionally, 10 in-depth interviews with SC members provided insight into the dynamics of engagement, emphasizing the evolution of trust, co-learning, and collective leadership over time. Integrated analysis confirmed that iterative, community-driven engagement strategies contribute to stronger, more sustainable research partnerships. This study offers a replicable framework for inclusive research engagement. Findings reinforce the need for culturally-aligned, patient-and family-centered research models that reflect the needs, perspectives, and experiences of populations typically excluded from research.  \nAbstract citation ID: igaf122.1486  \nLIVED EXPERIENCES VERSUS ARTIFICIAL INTELLIGENCE TO REDUCE HOSPITAL READMISSION: A QUALITATIVE STUDY  \nElham Mahmoudi, University of Michigan, Ann Arbor, Michigan, United States  \nFrequent 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 ","cbCaisEEnEHSCYnZ","https://ap.wps.com/l/cbCaisEEnEHSCYnZ","pdf",425845,"English","# Engaging a Community Council for Latino Dementia Research\n## Mixed-methods study design and community pláticas\n# Lived Experiences Versus Artificial Intelligence to Reduce Hospital Readmission\n## Qualitative interviews and thematic analysis\n# Participant Perceptions of a Trauma-Informed Care Planning Intervention\n## Trauma-informed approach and low advance care planning participation","[{\"question\":\"Why did the study focus on qualitative methods instead of relying solely on machine learning?\",\"answer\":\"Machine learning models have not successfully predicted and reduced hospital readmission for people with dementia. The study used qualitative interviews to identify what was missing in ML approaches.\"},{\"question\":\"What participant groups were interviewed in the qualitative study?\",\"answer\":\"The study interviewed 21 participants, including 11 caregivers of people with dementia and 10 hospital staff such as nurses, discharge planners, and social workers.\"},{\"question\":\"What recommendations did caregivers and hospital staff propose to reduce readmission?\",\"answer\":\"Recommendations included dementia-specialized hospital wards, dementia-specific inpatient/discharge protocols with educational materials for caregivers, at-home follow-up visits, and accessible and affordable home care services.\"}]","Lived Experiences Versus Artificial Intelligence to Reduce Hospital Readmission - A Qualitative Study | PDF",1790642222]