Digital Decision Aids to Support Decision-Making in Palliative and End-of-Life Dementia Care: Systematic Review and Meta-Analysis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,030 | 0,096 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,020 | 0,034 |
| Bibliométrie | 0,011 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».