Evidence map›Paper›PMID 40109496›Full record

ArticlePatient preference and adherence2025

Assessing the Needs of Patients with Cancer and Healthcare Professionals for a Digital Pain Management System: A Qualitative Study.

Xiaotong Xie, Xue Gao, Hui Wang, Binghua Li, Yan Wang, Xiaodong Jiao, Xiaowan Xv, Lingjun Zhou

Abstract read
In one paragraph

Article in Patient preference and adherence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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.

Xiaotong Xie *Faculty of Nursing, Naval Medical University, Shanghai, People's Republic of China.ORCID 0009-0000-9293-3526
Xue Gao *Department of Nursing, No. 943 hospital of the People's Liberation Army, Wuwei, Gansu Province, People's Republic of China.ORCID 0009-0008-1748-0975
Hui Wang *Faculty of Nursing, Naval Medical University, Shanghai, People's Republic of China.
Binghua LiFaculty of Nursing, Naval Medical University, Shanghai, People's Republic of China.
Yan WangOncology Departments, Changzheng Hospital, Shanghai, People's Republic of China.
Xiaodong JiaoOncology Departments, Changzheng Hospital, Shanghai, People's Republic of China.
Xiaowan XvOncology Departments, Changhai Hospital, Shanghai, People's Republic of China.
Lingjun ZhouFaculty of Nursing, Naval Medical University, Shanghai, People's Republic of China.ORCID 0000-0001-6725-8311

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To understand the willingness of patients and healthcare workers to use, as well as their needs for, an intelligent cancer pain management platform. The findings will serve as a reference for developing cancer pain management tools in China. Methods: A Purposive sampling was used to conduct semi-structured interviews from March to June 2024 with patients experiencing chronic cancer pain, their family members, and healthcare workers in the oncology departments of two tertiary grade A hospitals in Shanghai, China. Data were analyzed using the Colaizzi's seven-step analysis. Results: The needs of patients and healthcare workers for a cancer pain management app were categorized into five themes, namely, recording of pain status, medication reminders, health education, social support, and artificial intelligence assistance. Conclusion: Future development of an advanced cancer pain management platform must address the multiple challenges currently faced in out-of-hospital cancer pain management, while also considering the needs and preferences of patients and healthcare workers. The platform should integrate features such as visualization of patients' pain trends, online medical education, peer support, real-time counseling, artificial intelligence assistance, and guidance for at-home self-management and supervision.

Indexed as

artificial intelligencecancer painmHealthneeds assessmentpain management

Identifiers

PMID40109496
PMCPMC11921792

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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.