NCI-Delirium Model: Co-caring to decrease the incidence of delirium among hospitalized older adults.
Bibliographic record
Abstract
Delirium negatively impacts the health and wellbeing of older adults and their family carers and contributes to unnecessary high healthcare costs, estimated at $8.8 billion per year in Australia. Improvements to clinical practice are urgently needed, particularly now, as the COVID-19 pandemic is placing an enormous burden on our hospitals. Innovative strategies that support partnerships with family carers have been reported as key to improved patient outcomes and satisfaction with care, particularly for older adults unable to participate in their own care. This is because carers have intimate knowledge of a family member’s cognitive state to identify early subtle changes others might not. Carers also provide valuable reassurance and familiarity to the older adults during their treatment and can orient them to place and time. Founded on our clinical observations, informed by a scoping review of the literature (in press) and co-designed with carers, consumers, clinicians and policy analysts, we developed a new model of delirium care: NCI-Delirium. NCI-Delirium is a validated, scalable, low risk model that values the lived experience of older adults and carers. NCI-Delirium supports the integration of carers as partners in the care of the older adult from admission into the acute care setting. To support the integration of carers as partners, they are offered a web-based Delirium Toolkit that contains: an education package to build awareness and skill development (e.g., delirium risk factors, therapeutic and preventive strategies and non-pharmacological interventions to manage the reorientation of the hospitalised older adult); a 7-item psychometrically tested screening tool designed for carers to identify delirium symptoms and people at risk of delirium; access to support resources (e.g., counselling, social prescriptions, peer-support); and a co-designed discharge plan with the health team. Preliminary results suggest that NCI-Delirium improves health service delirium outcomes. Carer involvement in this research, and the introduction of their perspectives, has elevated carers’ key relationships with clinicians. Our co-designed solution to support the integration of carers as partners in delirium management has improved carers’ caregiving burden, psychological wellbeing and knowledge of delirium. This translatable co-designed model of care provides the much needed rich and robust clinical, implementation and economic evidence, to address the risk factors associated with the prevalence of delirium in hospitalised older adults. It is anticipated that the model will transform how we integrate carers as partners in care to improve clinical practice, patient and carer experiences; and reduce caregiving burden. Globally, the identification and management of risk for delirium is imperative. Delirium is a potentially life-threatening condition, and not well detected in the acute or community care setting. If carers are integrated and supported as partners in care, the capacity to identify symptoms and support the diagnosis of delirium should increase, potentially reducing functional decline, transition to residential aged care facilities and mortality rates. The next steps are to evaluate this co-designed web-based delirium model of care for use by carers in the community and residential aged care setting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".