Managing healthcare information: analyzing trust.

Int J Health Care Qual Assur

School of Informatics, University of Skövde, Skövde, Sweden.

Published: August 2016

Purpose - The purpose of this paper is to analyze two case studies with a trust matrix tool, to identify trust issues related to electronic health records. Design/methodology/approach - A qualitative research approach is applied using two case studies. The data analysis of these studies generated a problem list, which was mapped to a trust matrix. Findings - Results demonstrate flaws in current practices and point to achieving balance between organizational, person and technology trust perspectives. The analysis revealed three challenge areas, to: achieve higher trust in patient-focussed healthcare; improve communication between patients and healthcare professionals; and establish clear terminology. By taking trust into account, a more holistic perspective on healthcare can be achieved, where trust can be obtained and optimized. Research limitations/implications - A trust matrix is tested and shown to identify trust problems on different levels and relating to trusting beliefs. Future research should elaborate and more fully address issues within three identified challenge areas. Practical implications - The trust matrix's usefulness as a tool for organizations to analyze trust problems and issues is demonstrated. Originality/value - Healthcare trust issues are captured to a greater extent and from previously unchartered perspectives.

Download full-text PDF

Source
http://dx.doi.org/10.1108/IJHCQA-11-2015-0136DOI Listing

Publication Analysis

Top Keywords

trust
13
trust matrix
12
case studies
8
identify trust
8
trust issues
8
challenge areas
8
trust problems
8
managing healthcare
4
healthcare analyzing
4
analyzing trust
4

Similar Publications

Exploring the Credibility of Large Language Models for Mental Health Support: Protocol for a Scoping Review.

JMIR Res Protoc

January 2025

Data and Web Science Group, School of Business Informatics and Mathematics, University of Manneim, Mannheim, Germany.

Background: The rapid evolution of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers (BERT; Google) and GPT (OpenAI), has introduced significant advancements in natural language processing. These models are increasingly integrated into various applications, including mental health support. However, the credibility of LLMs in providing reliable and explainable mental health information and support remains underexplored.

View Article and Find Full Text PDF

Leishmaniasis, caused by the Leishmania parasite, remains a persistent public health challenge in Pakistan. Despite control efforts, the disease prevalence continues to rise, particularly among pediatric populations. Understanding prevalence patterns and transmission dynamics is critical for effective control strategies.

View Article and Find Full Text PDF

Hepatitis C virus (HCV) presents a significant global health concern, affecting 3.3% of the world's population. The primary mode of HCV transmission is through blood and blood products.

View Article and Find Full Text PDF

Amidst the ongoing COVID-19 pandemic, the imperative of our time resides in crafting stratagems of utmost precision to confront the relentless SARS-CoV-2 and quell its inexorable proliferation. A paradigm-shifting weapon in this battle lies in the realm of nanoparticles, where the amalgamation of cutting-edge nanochemistry begets a cornucopia of inventive techniques and methodologies designed to thwart the advances of this pernicious pathogen. Nanochemistry, an artful fusion of chemistry and nanoscience, provides a fertile landscape for researchers to craft innovative shields against infection.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!