Evidence map›Paper›PMID 42805738›Full record

ArticleInternational journal of nursing sciences2026

Healthcare professionals' perceptions and needs for a Home-based Artificial Intelligence Pain Management System for cancer pain: A qualitative study.

Mengmeng Chen, Xin Chen, Jiajun Yuan, Meiling Yang, Jiachun Lu, Wing Chi Lam, Cheuk Yan Ng, Thomas K S Wong

Abstract read
In one paragraph

Article in International journal of nursing sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Mengmeng ChenSchool of Nursing, Guangzhou Medical University, Guangdong, China.
Xin ChenSchool of Nursing, Guangzhou Medical University, Guangdong, China.
Jiajun YuanSchool of Nursing, Guangzhou Medical University, Guangdong, China.
Meiling YangSchool of Nursing, Guangzhou Medical University, Guangdong, China.
Jiachun LuSchool of Nursing, Guangzhou Medical University, Guangdong, China.
Wing Chi LamSociety for the Promotion of Hospice Care, Jockey Club Home for Hospice, Hong Kong, China.
Cheuk Yan NgSociety for the Promotion of Hospice Care, Jockey Club Home for Hospice, Hong Kong, China.
Thomas K S WongSchool of Nursing, Guangzhou Medical University, Guangdong, China. Electronic address: Wongks.thomas@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study aimed to explore healthcare professionals' (HCPs') perceptions of and needs for a Home-based Artificial Intelligence Pain Management System (HAIPMS) for cancer pain in Hong Kong, China.

methodsA qualitative study was conducted using semi-structured interviews with 14 HCPs from Jockey Club Home for Hospice (JCHH) in Hong Kong, China between March and June 2025. Data were analyzed using thematic analysis.

resultsTheir professional experience ranged from 3 to 33 years. Thematic analysis generated three themes and ten subthemes. The themes were: 1) valued but under-supported home-based cancer pain management, including family-dependent and intermittent professional support; comfort, affordability, and system relief in home-based care; and subjective assessment, medication non-adherence, and delayed support; 2) cautious optimism toward AI integration, including expectations for visible, measurable, and continuous pain monitoring; concern of risks resulted from AI implementation; and heterogeneous attitudes shaped by professional and generational divides; and 3) practical requirements for HAIPMS implementation, including essential functional modules; user-centered design and adaptability; safety and ethical assurance requirements; and training and sustained support.

conclusionsHCPs viewed HAIPMS as a potentially useful supportive tool for home-based cancer pain management, provided that it preserves professional autonomy, fits local care contexts, and incorporates ethical safeguards, user-friendly design, and sustained support. These findings offer practical guidance for developing AI-assisted pain management systems in home-based cancer pain.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelCancer PainHealth PersonnelHome Care ServicesPain ManagementAdultFemaleHong KongHumansMaleMiddle AgedQualitative ResearchArtificial intelligenceCancer painHealthcare professionalsPalliative careQualitative methods

Identifiers

PMID42805738
PMCPMC13648177

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.