Evidence map›Paper›PMID 40103645›Full record

ArticleDigital health

Leveraging healthcare professionals' insights to enhance data quality in medical big data platforms: A qualitative study.

Huang Jingjing, Huang Sufang, Lang Xiaorong, Liu Yuchen, Zhang Kexin, Liu Shiya

Abstract read
In one paragraph

Article in Digital health. 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

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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

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

6 authors.

Huang JingjingDepartment of Emergency, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.ORCID https://orcid.org/0000-0002-7525-6073
Huang SufangDepartment of Emergency, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.ORCID https://orcid.org/0000-0003-4071-8597
Lang XiaorongDepartment of Emergency, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.ORCID https://orcid.org/0000-0002-9084-3022
Liu YuchenDepartment of Emergency, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.ORCID https://orcid.org/0009-0000-1435-115X
Zhang KexinDepartment of Emergency, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.ORCID https://orcid.org/0000-0002-4660-2010
Liu ShiyaDepartment of Emergency, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aims to explore the awareness, attitudes, and actual usage of medical big data platforms among healthcare professionals to provide practical guidance and theoretical support for improving data quality for the development of medical informatization. Method: Semistructured interviews were conducted with 19 doctors and nurses from a tertiary hospital in Wuhan City between April and June 2024. Results: The analysis yielded seven major themes and nine subthemes: cognitive status, Value of Medical Big Data Platforms, data trust (subjective data, objective data), purposes of data recording (patient condition observation, self-protection, task completion), practical challenges (conflict between work purposes and recording requirements, inconsistent departmental training standards, influence of leadership style and team culture), standardization of data recording, and concerns about data privacy. Conclusion: The insufficient understanding of medical big data platforms among healthcare professionals affects the quality of data recording and research value, emphasizing the necessity of strengthening training, standardizing data recording, providing technical support, and ensuring data security.

Indexed as

Big datadata qualitymedical informatizationmedical staffqualitative research

Identifiers

PMID40103645
PMCPMC11915297

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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.