Objectives: This study aimed to identify factors influencing delirium prevention (risk identification and screening), from the perspective of health service staff, in order to ascertain the characteristics and implementation strategies critical for the clinical adoption of data-driven optimisations for delirium prevention. This pre-implementation study used the Monash Learning Health System (LHS) paradigm to visualise iterative integrated assimilation of delirium prevention in routine care.
Methods: A qualitative study was conducted in a large metropolitan public health network in Australia. Following consultation with organisational leaders, a purposive sample of clinical/non-clinical participants with expertise in delirium care delivery was recruited. Interviews were inductively analysed using a framework approach. The Consolidated Framework for Implementation Research (CFIR) domains underpinned interview questions and guided thematic mapping and analysis of responses.
Results: Semi-structured interviews were conducted with 18 participants (clinical [n = 14] and non-clinical [n = 4]). Key themes included challenges in consistently integrating delirium risk identification and screening processes into clinical workflows, infrastructure-related obstacles hindering the digitisation of decision support, and the need to engage caregivers and staff in designing optimisations to enable appropriate and timely delirium prevention.
Conclusions: This study generated insights into key factors influencing delirium prevention, focusing on the development and implementation of optimisations such as automated delirium risk prediction. Improving hospital information technology infrastructure, supporting workforce digital literacy and ensuring accountability in all professional groups are crucial for implementing automated delirium risk prediction models in clinical practice. Future research should examine the feasibility and efficacy of optimised delirium prevention interventions in pragmatic clinical trials.
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| http://dx.doi.org/10.1111/ajag.70011 | DOI Listing |
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