Evidence map›Paper›PMID 42646504›Full record

ArticleJMIR nursing2026

Manifestations and Potential Consequences of Information Distortion in Electronic Nursing Records: Qualitative Study.

Jianan Wang, Yihong Xu, Wen Li, Zhichao Yang, Yushu Sun, Xiaoxiao Zhang, Hongying Pan

Abstract read
In one paragraph

Article in JMIR nursing, 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

7 authors.

Jianan Wang *Nursing Department, Sir Run Run Shaw Hospital, 3 Qingchun East Road, Hangzhou, Zhejiang, 310016, China, 86 13857188922.ORCID http://orcid.org/0009-0003-0827-3134
Yihong Xu *Nursing Department, Sir Run Run Shaw Hospital, 3 Qingchun East Road, Hangzhou, Zhejiang, 310016, China, 86 13857188922.ORCID http://orcid.org/0000-0001-6177-8316
Wen LiNursing Department, Sir Run Run Shaw Hospital, 3 Qingchun East Road, Hangzhou, Zhejiang, 310016, China, 86 13857188922.ORCID http://orcid.org/0009-0008-0701-5121
Zhichao YangNursing Department, Sir Run Run Shaw Hospital, 3 Qingchun East Road, Hangzhou, Zhejiang, 310016, China, 86 13857188922.ORCID http://orcid.org/0009-0004-9665-6115
Yushu SunNursing Department, Sir Run Run Shaw Hospital, 3 Qingchun East Road, Hangzhou, Zhejiang, 310016, China, 86 13857188922.ORCID http://orcid.org/0009-0007-2690-9086
Xiaoxiao ZhangNursing Department, Sir Run Run Shaw Hospital, 3 Qingchun East Road, Hangzhou, Zhejiang, 310016, China, 86 13857188922.ORCID http://orcid.org/0009-0009-8154-7203
Hongying PanNursing Department, Sir Run Run Shaw Hospital, 3 Qingchun East Road, Hangzhou, Zhejiang, 310016, China, 86 13857188922.ORCID http://orcid.org/0000-0001-5597-1793

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Nursing records are a critical reflection of nursing practices and play a key role in ensuring the quality of patient care. However, these records sometimes fail to accurately represent the real-world health truth, resulting in information distortion. The manifestations and potential consequences of such distortion remain understudied, which may hinder efforts to improve nursing practices. Objective: This study aims to explore the primary manifestations and potential consequences of information distortion in electronic nursing records (ENRs). Methods: We conducted semistructured interviews at a Class-A tertiary hospital in China. A total of 12 nurses from various care units were recruited through purposive and snowball sampling. Inductive content analysis was used to analyze the qualitative data. Results: Qualitative analysis revealed that manifestations of information distortion in ENRs fall into 4 main categories: untimeliness, inaccuracy, incompleteness, and inconsistency. These categories are further subdivided into 7 subcategories: premature recording, delayed recording, incorrect data entry, fabrication, account misuse, missing data, and inconsistent data entry. The potential consequences of information distortion encompass 3 main categories: patient-related, nurse-related, and data-related. These categories are further subdivided into 5 subcategories: threats to patient safety, increases in negative emotions, damage to professional image, erosion of nursing professionalism, and impairment of secondary data use. Conclusions: The identified manifestations encompass a wide range of nursing activities, with potential consequences affecting patients, research, and caregivers. Strengthening ethics education, implementing monitoring mechanisms, establishing standardized guidelines, and leveraging technological solutions are recommended to reduce information distortion in ENRs. Further research on contributing factors and effective countermeasures is needed to mitigate information distortion in ENRs.

Indexed as

Electronic Health RecordsAdultChinaFemaleHumansInterviews as TopicQualitative Researchdata qualityelectronic nursing recordsinformation distortioninformation ethicsnursing

Identifiers

PMID42646504
PMCPMC13509441

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.