ArticleBMC palliative care2026
A knowledge graph-driven paradigm for holistic symptom management: development and evaluation of a nurse-led KG-QA system in community palliative care.
Article in BMC palliative care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundCommunity nurses play a pivotal role in palliative care but face barriers in managing complex symptoms, such as fragmented knowledge and a lack of community-tailored evidence-based guidance, impairing clinical efficiency. The aim of this study was to develop and evaluate a knowledge graph-based question-answering system for symptom management in community palliative care.
methodsA three-phase codesign study guided by the Knowledge-to-Action framework was conducted. Phase 1 (Knowledge Creation): A Symptom Management Knowledge Base (Knowledge Product I) was developed through a codesign process involving a multidisciplinary expert panel. This panel adapted a knowledge base created by researchers through systematic evidence synthesis, employing FAME criteria for contextual adaptation. Phase 2 (Action Cycle: Implementation): A semantically structured knowledge graph (Knowledge Product II) was constructed via automated extraction by software developers, followed by manual verification by researchers. Based on this graph, a question-answering system was created and implemented as a WeChat mini-program, resulting in a practical KG-QA system (Knowledge Product III). Phase 3 (Action Cycle: Evaluation): The system's acceptability, usability, and perceived usefulness and ease of use were assessed among experts and community nurses during a two-week evaluation period using the Clinical Nursing Information System Effectiveness Evaluation Scale and the Post-Study System Usability Questionnaire, which is grounded in the Technology Acceptance Model.
resultsThe knowledge base comprises 225 evidence items for nine symptoms; the knowledge graph integrates ten entity types, 11 relationship categories, 442 entities and 668 relationships, with the system supporting four query interfaces and three search methods. The evaluations demonstrated high perceived usefulness and ease of use, with strong scores for acceptability (102.25 ± 16.21; 110.56 ± 9.90) and usability (2.47 ± 1.98; 2.23 ± 1.93).
conclusionThe question-answering system bridges the evidence-practice gap via a nursing-process paradigm, offering a potentially scalable model that aligns with national policies pending further validation. However, these findings are based on a small‑scale, single‑region, short‑term evaluation relying largely on subjective measures. Future research should explore its long-term clinical outcomes and cross-setting scalability.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.