Evidence map›Paper›PMID 39507125›Full record

ArticleFrontiers in health services2024

Application of zero trust model in preventing medical errors.

Nikhil Sood, Roop Parlapalli, Pranav Sharma, Rahul Kashyap

Abstract read
In one paragraph

Article in Frontiers in health services, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Nikhil SoodDepartment of Medicine, Banner Health, Gilbert, AZ, United States.
Roop ParlapalliDepartment of Medicine, Geisinger Community Medical Center, Scranton, PA, United States.
Pranav SharmaDepartment of Research, Global Remote Research Scholars Program, Princeton Junction, NJ, United States.
Rahul KashyapDepartment of Research, Well Span Health, York, PA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medical errors can occur in many areas of healthcare, including hospitals, clinics, and surgery centers. They can result in negative consequences for patients and their loved ones. Over the years, different methods have been used to reduce medical errors. Zero Trust is an information security model that denies access to applications and data by default. Other industries have successfully used Zero Trust Model (ZTM), and it has been shown to improve outcomes. This editorial analyzes how the ZTM can be introduced to prevent medical errors in healthcare settings. ZTM application in healthcare could potentially revolutionize patient safety by tightly controlling and monitoring access to sensitive patient data and critical systems. By enhancing security measures, the ZTM could address the paramount concerns of patient data privacy and safety in healthcare. The zero-trust approach offers a potential solution by identifying consistent causes of errors and providing viable solutions to prevent their recurrence. In the era of worsening ransomware attacks on healthcare systems, the ZTM could also have enormous implications in other cybersecurity aspects. With this manuscript, the authors advocate for the broader application of ZTM across other facets of healthcare cybersecurity.

Indexed as

medical errorsoutcomepatient safetyzero trust in healthcarezero trust model

Identifiers

PMID39507125
PMCPMC11538063

What Socratic 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 Socratic graph.