Why the study?
Transferring long-term care residents to acute care hospitals carries significant health risks, and despite the emergence of decision-making tools to guide transfers, their application remains inconsistent.
Do decision-making tools reduce hospitalization rates and improve communication for long-term care residents facing transfer to acute care hospitals?
Do decision-making tools reduce hospitalization rates and improve communication for long-term care residents facing transfer to acute care hospitals?
Decision-making tools for long-term care to hospital transfers appear to reduce hospitalizations and improve communication, though current approaches are highly variable and lack standardization.
Supports broader use of decision tools in long-term care; extends evidence but leaves standardization unresolved.
The decision-making process for transferring long-term care (LTC) residents to acute care hospitals is complex. These transfers can carry significant health risks, such as loss of function, morbidity, and mortality. Decision-making tools have emerged to guide these transfers, offering structured frameworks for informed discussions. Despite their benefits, their application remains inconsistent across LTC settings. MEDLINE, Embase, and CENTRAL were searched, following PRISMA-Scr guidelines. Studies evaluating decision support tools for LTC-to-hospital transfers were included. Of 1,383 studies identified, 15 studies involving 50,175 patients were included. Tools were categorized into five intervention types: educational booklets, decision aid videos, multidisciplinary programs, advance directive programs, and checklists. Most studies reported reduced hospitalization rates and improved communication, but variability in tool types highlighted their fragmented application. This scoping review summarizes reported outcomes and highlights gaps in the application and evaluation of decision-making tools for LTC-to-hospital transfers, emphasizing the need for more standardized and culturally sensitive interventions.
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Ji et al. (2025) studied this question.
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