Evidence map›Paper›PMID 38998897›Full record

ArticleHealthcare (Basel, Switzerland)2024

An Evaluation of Patient Privacy Protection with Fuzzy Conjoint Analysis-A Case Study from Nurses' Perspective.

Güney Gürsel, Nükhet Bayer, Ömer Turunç, Abdullah Çalışkan, İrfan Akkoç, Ayhan Demirci, Melike Çetin, Özlem Köroğlu

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 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

8 authors.

Güney GürselSoftware Engineering Department, Faculty of Engineering and Architecture, Konya Food and Agriculture University, Konya 42080, Turkey.
Nükhet BayerDepartment of Health Sciences, Lokman Hekim University, Ankara 06530, Turkey.
Ömer TurunçBusiness Administration Department, Faculty of Economic, Administrative and Social Sciences, Antalya Bilim University, Antalya 07110, Turkey.
Abdullah ÇalışkanHealth Management Department, School of Health Sciences, Toros University, Mersin 33140, Turkey.
İrfan AkkoçRectorate, Izmir Tinaztepe University, İzmir 35400, Turkey.
Ayhan DemirciDepartment of International Trade and Logistics, Faculty of Economic, Administrative and Social Sciences, Toros University, Mersin 33140, Turkey.
Melike ÇetinDepartment of Economics, Faculty of Economic, Administrative and Social Sciences, Antalya Bilim University, Antalya 07110, Turkey.
Özlem KöroğluHealth Management Department, School of Health Sciences, Toros University, Mersin 33140, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith the rapid improvement in healthcare technologies, the security and privacy of the most sensitive data are at risk. Patient privacy has many components, even when data are in electronic format. Although patient privacy has extensively been discussed in the literature, there is no study that has presented all components of patient privacy.

methodsThis study presents a complete assessment framework, develops an inventory as an assessment tool, and examines the reliability and validity of the inventory. The study was carried out in three phases: conceptual framework development, inventory development, and an evaluation case study. Fuzzy conjoint analysis was used in the evaluation to deal with subjectivity and ambiguity. As a result of the evaluation, the case study institution was given a patient privacy maturity level between 1 and 5, where 1 is the worst and 5 is the best.

resultsThe case study evaluated the largest hospital in Turkey, which employs 800 nurses. Half of the nurses, 400, participated in the study. According to the literature, healthcare institutions do not invest enough in protecting patients' privacy, and the results of the study support this finding. The institution's maturity level was 2, which is poor.

conclusionsThis study measured privacy maturity with many assessment components. The result of the assessment explains to patients and the public whether their data are secure or not. With the implementation of this maturity level, patients have an idea about which institution to choose, and the public can infer the reliability of institutions in terms of patient privacy.

Indexed as

evaluationfuzzy conjoint analysispatient privacysimilarity

Identifiers

PMID38998897
PMCPMC11241207

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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.