Communication Tools for End-of-Life Decision-Making in Ambulatory Care Settings: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Patients with serious illness, and their families, state that better communication and decision-making with healthcare providers is a high priority to improve the quality of end-of-life care. Numerous communication tools to assist patients, family members, and clinicians in end-of-life decision-making have been published, but their effectiveness remains unclear. OBJECTIVES: To determine, amongst adults in ambulatory care settings, the effect of structured communication tools for end-of-life decision-making on completion of advance care planning. METHODS: We searched for relevant randomized controlled trials (RCTs) or non-randomized intervention studies in MEDLINE, EMBASE, CINAHL, ERIC, and the Cochrane Database of Randomized Controlled Trials from database inception until July 2014. Two reviewers independently screened articles for eligibility, extracted data, and assessed risk of bias. Grading of Recommendations Assessment, Development, and Evaluation (GRADE) was used to evaluate the quality of evidence for each of the primary and secondary outcomes. RESULTS: Sixty-seven studies, including 46 RCTs, were found. The majority evaluated communication tools in older patients (age >50) with no specific medical condition, but many specifically evaluated populations with cancer, lung, heart, neurologic, or renal disease. Most studies compared the use of communication tools against usual care, but several compared the tools to less-intensive advance care planning tools. The use of structured communication tools increased: the frequency of advance care planning discussions/discussions about advance directives (RR 2.31, 95% CI 1.25-4.26, p = 0.007, low quality evidence) and the completion of advance directives (ADs) (RR 1.92, 95% CI 1.43-2.59, p<0.001, low quality evidence); concordance between AD preferences and subsequent medical orders for use or non-use of life supporting treatment (RR 1.19, 95% CI 1.01-1.39, p = 0.028, very low quality evidence, 1 observational study); and concordance between the care desired and care received by patients (RR 1.17, 95% CI 1.05-1.30, p = 0.004, low quality evidence, 2 RCTs). CONCLUSIONS: The use of structured communication tools may increase the frequency of discussions about and completion of advance directives, and concordance between the care desired and the care received by patients. The use of structured communication tools rather than an ad-hoc approach to end-of-life decision-making should be considered, and the selection and implementation of such tools should be tailored to address local needs and context. REGISTRATION: PROSPERO CRD42014012913.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.078 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".