Related papers: EHealth Technologies Integration with Healthcare Work Activities in Public Hospitals: A Critical Realist Perspective

EHealth Technologies Integration with Healthcare Work Activities in Public Hospitals: A Critical Realist Perspective

URL: http://arxiv.org/abs/2108.09734v1
Date: Sun, 22 Aug 2021 14:28:37 GMT
Title: EHealth Technologies Integration with Healthcare Work Activities in Public Hospitals: A Critical Realist Perspective
Authors: Mourine Achieng and Ephias Ruhode
Abstract summary: Integration of eHealth technologies with healthcare work activities has seen great advancement in many healthcare systems in developing countries. These efforts have been tainted by several challenges such as fragmentation, lack of standardization and co-ordination. The aim of this paper is to explain why the current integration efforts do not adequately facilitate healthcare work activities in public hospitals in under-served contexts of South Africa.
Score: 0.0
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: Integration of eHealth technologies with healthcare work activities has seen great advancement in many healthcare systems in developing countries. However, these efforts have been tainted by several challenges such as fragmentation, lack of standardization and co-ordination. Subsequently, the undertakings of eHealth articulated in health strategy/policy documents have not been fully realised. The implications of this has been that the majority of the population still access inadequate healthcare services. The aim of this paper is to explain why the current integration efforts do not adequately facilitate healthcare work activities in public hospitals in under-served contexts of South Africa. A critical realist perspective within a qualitative approach was adopted. A total of 21 participants were purposively sampled and interviewed because of their knowledge and experience in the healthcare service delivery process as well as their involvement in the integration of ehealth. The study applied the Activity Analysis and Development (ActAD) model as a theoretical analytical tool and draws on normalization process theory (NPT) as an explanatory framework. The findings highlight generative mechanisms such as the inadequate analysis of system's fit-for-purpose in healthcare workflows have inhibiting effects in the integration process of eHealth.

Related papers

AI as a Medical Ally: Evaluating ChatGPT's Usage and Impact in Indian Healthcare [2.259877069661293]
This study investigates the integration and impact of Large Language Models (LLMs), like ChatGPT, in India's healthcare sector. Our findings reveal that healthcare professionals value ChatGPT in medical education and preliminary clinical settings, but exercise caution due to concerns about reliability, privacy, and the need for cross-verification with medical references. General users show a preference for AI interactions in healthcare, but concerns regarding accuracy and trust persist.
arXiv Detail & Related papers (2024-01-28T08:20:36Z)
Analysis of Blockchain Integration in the e-Healthcare Ecosystem [0.0]
This article studies the most commonly adopted approaches in healthcare data management systems using blockchain technology. An evaluation is conducted based on a set of observed common characteristics, distinguishing one approach from the others. For effective implementation in the context of e-health, we emphasize the existence of crucial challenges.
arXiv Detail & Related papers (2024-01-08T12:19:53Z)
The Design and Implementation of a National AI Platform for Public Healthcare in Italy: Implications for Semantics and Interoperability [62.997667081978825]
The Italian National Health Service is adopting Artificial Intelligence through its technical agencies. Such a vast programme requires special care in formalising the knowledge domain. Questions have been raised about the impact that AI could have on patients, practitioners, and health systems.
arXiv Detail & Related papers (2023-04-24T08:00:02Z)
Privacy-preserving machine learning for healthcare: open challenges and future perspectives [72.43506759789861]
We conduct a review of recent literature concerning Privacy-Preserving Machine Learning (PPML) for healthcare. We primarily focus on privacy-preserving training and inference-as-a-service. The aim of this review is to guide the development of private and efficient ML models in healthcare.
arXiv Detail & Related papers (2023-03-27T19:20:51Z)
Reliable and Resilient AI and IoT-based Personalised Healthcare Services: A Survey [1.581123237785583]
This paper conducts a comprehensive survey on personalized healthcare services. We first present an overview of key requirements of comprehensive personalized healthcare services in modern healthcare Internet of Things (HIoT) Second, we explored a fundamental three-layer architecture for IoT-based healthcare systems using AI and non-AI-based approaches. Third, we highlighted different security threats against each layer of IoT architecture along with the possible AI and non-AI-based solutions.
arXiv Detail & Related papers (2022-08-29T23:14:02Z)
Fair Machine Learning in Healthcare: A Review [90.22219142430146]
We analyze the intersection of fairness in machine learning and healthcare disparities. We provide a critical review of the associated fairness metrics from a machine learning standpoint. We propose several new research directions that hold promise for developing ethical and equitable ML applications in healthcare.
arXiv Detail & Related papers (2022-06-29T04:32:10Z)
MIMO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning [49.57261599776167]
We propose an end-to-end robust Transformer-based solution, Mutual Integration of patient journey and Medical Ontology (MIMO) for healthcare representation learning and predictive analytics.
arXiv Detail & Related papers (2021-07-20T07:04:52Z)
Towards a framework for evaluating the safety, acceptability and efficacy of AI systems for health: an initial synthesis [0.2936007114555107]
We aim to set out a minimally viable framework for evaluating the safety, acceptability and efficacy of AI systems for healthcare. We do this by conducting a systematic search across Scopus, PubMed and Google Scholar. The result is a framework to guide AI system developers, policymakers, and regulators through a sufficient evaluation of an AI system designed for use in healthcare.
arXiv Detail & Related papers (2021-04-14T15:00:39Z)
Assessing the Severity of Health States based on Social Media Posts [62.52087340582502]
We propose a multiview learning framework that models both the textual content as well as contextual-information to assess the severity of the user's health state. The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user's health.
arXiv Detail & Related papers (2020-09-21T03:45:14Z)
CPAS: the UK's National Machine Learning-based Hospital Capacity Planning System for COVID-19 [111.69190108272133]
The coronavirus disease 2019 poses the threat of overwhelming healthcare systems with unprecedented demands for intensive care resources. We developed the COVID-19 Capacity Planning and Analysis System (CPAS) - a machine learning-based system for hospital resource planning. CPAS is one of the first machine learning-based systems to be deployed in hospitals on a national scale to address the COVID-19 pandemic.
arXiv Detail & Related papers (2020-07-27T19:39:13Z)
Towards an Electronic Health Record System in Vietnam: A Core Readiness Assessment [0.0]
The aim of this study is to initiate a carefully planned nationwide EHR system in Vietnam by assessing the core readiness. To collect data, participant observation, document analysis and an in-depth interview were used. The Ministry of Health is currently working on improving the conditions for future implementation of a Vietnamese EHR system.
arXiv Detail & Related papers (2020-05-28T19:11:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.