Digital Decision Aids to Support Decision-Making in Palliative and End-of-Life Dementia Care: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Making a care-related decision is a complex cognitive process. Patient decision aids could provide information on potential options about risks and benefits, incorporate individual values and preferences, and help people with dementia or their family carers make decisions about palliative and end-of-life care. OBJECTIVE: This systematic review aimed to critically evaluate and synthesize evidence on the effectiveness of digital decision aids to support decision-making in palliative and end-of-life care for patients with dementia, their family carers, or clinicians. METHODS: A systematic literature search in 4 health-related databases (PubMed, Embase, CINAHL, and Web of Science) was performed in September 2024. Articles were included if the study focused on the development and evaluation of a digital decision support tool on end-of-life dementia care, used an experimental design, and was available in full text in English. Studies using a nonexperimental design were excluded. The Cochrane Collaboration's Risk of Bias Tool Version 2.0 or the Risk of Bias in Non-randomized Studies of Interventions Version 2.0 was used to assess risk of bias. Narrative synthesis and meta-analyses were performed to comprehensively summarize the technologies and outcomes of the decision aids. RESULTS: The literature search across datasets identified a total of 1274 records. With an additional 5 records from citation searching and reference reviewing, a total of 20 articles were included, with 10 studies using data from randomized controlled trials (RCTs) and 10 pretest-posttest pilot studies. Technologies of visual aids, videos, web pages, and telehealth were reported in the included studies to support decision-making for end-of-life dementia care. Most decision aids focused on the decision about the primary goal of care (life-prolonging care, limited care, and comfort care), except for 1 visual aid focusing on the decisions about feeding tube placement and drug treatment for dementia. Most decision aids engaged both patients and their family carers. Pilot studies examining feasibility showed that most participants found these decision aids relevant to their needs and easy to use, and were able to complete the intervention sessions. Meta-analyses of 4 RCTs showed that video decision aids were effective in increasing the proportion of participants opting for comfort care (odds ratio 3.81, 95% CI 1.92-7.56) but inconclusive for the proportion of documented do-not-hospitalize orders (odds ratio 1.60, 95% CI 0.70-3.67), compared to the control group. CONCLUSIONS: Internet-based decision aids offer a feasible and acceptable approach to support the shared decision-making between patients, families, and clinicians. The included studies reported various outcome measures, including preferred goal of care, quality of palliative care, decision-making performance, and health care use. More large-scale RCTs are needed, and consistent outcome measures should be considered to evaluate the effects of end-of-life decision aids. TRIAL REGISTRATION: PROSPERO CRD42024621321; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024621321.
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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 | 0.030 | 0.096 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".