Evidence map›Paper›PMID 34035927›Full record

ArticleHealthcare technology letters2021

Ensuring data integrity of healthcare information in the era of digital health.

Mohammad Zarour, Mamdouh Alenezi, Md Tarique Jamal Ansari, Abhishek Kumar Pandey, Masood Ahmad, Alka Agrawal, Rajeev Kumar, Raees Ahmad Khan

Open access · goldAbstract read
In one paragraph

Article in Healthcare technology letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
21.9field-weighted citation impact, top 1% of its field
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

21 citing papers in PubMed, 1 synthesis or guideline pooled it, 118 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Is source data verification a valid tool to improve data quality of tumour documentation data? A critical assessment.Health information management : journal of the Health Information Management Association of Australia · 2026
    Article
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  5. Review
  6. Predictive estimations of health systems resilience using machine learning.BMC medical informatics and decision making · 2025
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  8. Article
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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 at 4 institutions in 2 countries.

Mohammad ZarourCollege of Computer & Information Sciences Prince Sultan University Riyadh Kingdom of Saudi Arabia.ORCID https://orcid.org/0000-0002-1169-9502
Mamdouh AleneziCollege of Computer & Information Sciences Prince Sultan University Riyadh Kingdom of Saudi Arabia.ORCID https://orcid.org/0000-0002-9813-5039
Md Tarique Jamal AnsariDepartment of Computer Application Integral University Lucknow Uttar Pradesh India.
Abhishek Kumar PandeyDepartment of Information Technology Babasaheb Bhimrao Ambedkar University Lucknow Uttar Pradesh India.
Masood AhmadDepartment of Information Technology Babasaheb Bhimrao Ambedkar University Lucknow Uttar Pradesh India.
Alka AgrawalDepartment of Information Technology Babasaheb Bhimrao Ambedkar University Lucknow Uttar Pradesh India.ORCID https://orcid.org/0000-0001-5503-7917
Rajeev KumarDepartment of Computer Applications Shri Ramswaroop Memorial University Barabanki Uttar Pradesh India.ORCID https://orcid.org/0000-0002-1813-1362
Raees Ahmad KhanDepartment of Information Technology Babasaheb Bhimrao Ambedkar University Lucknow Uttar Pradesh India.ORCID https://orcid.org/0000-0002-9454-1312
Babasaheb Bhimrao Ambedkar University · INPrince Sultan University · SAIntegral University · INShri Ramswaroop Memorial University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Data integrity continues to be a persistent problem in the current healthcare sector. It ensures that the data is correct and has not even in any manner been improperly changed. Incorrect data might become significant health threats for patients and a big responsibility for clinicians, resulting in problems such as scam, misconduct, inadequate treatment and data theft. This sort of endangering scenario causes tremendous difficulty in handling healthcare data. This research intends to describe the threat plot of data integrity in healthcare through numerous attack statistics from around the world and Saudi Arabia and identify the criticality in Saudi Arabia in particular. A literature review by descriptive analysis, unit analysis and rating analysis to achieve the planned systematic literature review goal is outlined. The outcome of ranking analysis using a fuzzy analytical hierarchy process methodology offers a route for Saudi Arabian researchers to promote medical records or data security in Arabic healthcare. It is suggested that blockchain is the most prioritized method for regular use and adaptation across Saudi Arabia in all data integrity management techniques. To address the challenges of data integrity and future path, the authors critically examine the challenges posed by data integrity in the healthcare sector.

Identifiers

PMID34035927
PMCPMC8136763
OpenAlexW3156332541

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.