Skip to content
#diffusion models Open access

ASSESSMENT OF DIGITAL HEALTH TECHNOLOGIES IN IMPROVING PATIENT CARE IN THE NIGERIAN HEALTHCARE SYSTEM

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)
Mobile Health and mHealth Applications

Abstract

Digital Health Technologies (DHTs) including telemedicine, electronic health records (EHRs), mobile health (mHealth) applications, and Artificial Intelligence (AI)-enabled tools have emerged as central instruments for strengthening health systems and improving patient care, particularly in resource-constrained settings such as Nigeria. This paper undertakes a systematic narrative assessment of the extent to which digital health technologies have improved patient care within the Nigerian healthcare system. Adopting a desk-based, qualitative document-analysis design, the paper synthesises conceptual, theoretical, and empirical literature to examine the adoption, application, and outcomes of DHTs in Nigerian healthcare delivery. The review was anchored on Diffusion of Innovation Theory, complemented by the Technology Acceptance Model, the Unified Theory of Acceptance and Use of Technology, and the World Health Organization's Health System Building Blocks framework. Findings from the reviewed literature indicated that DHTs have improved treatment adherence, expanded access to care in underserved and rural communities, strengthened maternal and child health outcomes, and enhanced clinical decision-making and data quality. However, the pace and depth of adoption remain constrained by infrastructural deficits, epileptic power supply, low broadband penetration, high implementation costs, inadequate digital literacy among healthcare workers and patients, fragmented policy implementation, and weak interoperability among health information systems. The paper concluded that digital health technologies possess considerable potential to improve patient care in Nigeria, but that this potential is only partially realised due to systemic and infrastructural bottlenecks. It recommended coordinated investment in digital infrastructure, harmonisation of a national electronic health record standard, sustained capacity building for healthcare personnel, community-sensitive implementation strategies, and stronger public–private collaboration to accelerate the digital transformation of the Nigerian healthcare system.

View source

Similar papers

#computer vision Open access Jun 2016

Software Development in Startup Companies: The Greenfield Startup Model

The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.

Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al. · 178 citations · ⚡14
#computer vision Open access Oct 2016

Software Startups - A Research Agenda

Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.

M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al. · 157 citations · ⚡17
#machine learning Review Open access Oct 2016

“Failures” to be celebrated: an analysis of major pivots of software startups

This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.

Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al. · 127 citations · ⚡15
#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Conference Sep 2010

Exploring the Sources of Waste in Kanban Software Development Projects

The application of agile software methods and more recently the integration of Lean practices contribute to the trend of continuous improvement in the software industry. One such area warranting proper empirical evidence is a project’s operational efficiency when using the Kanban method. This short paper takes a new angle and explores waste in the Kanban-driven software development project context. A preliminary research model is presented for helping the consequent replication of the study. The results from the empirical analysis suggest Kanban can be an effective method in visualizing and organizing the current work, but does not prevent waste from creeping in, although the overall project outcome may be successful.

Marko Ikonen, Petri Kettunen, Nilay V. Oza et al. · 67 citations · ⚡9

Related blog posts

MIT News · Artificial Intelligence Sep 14, 2026

New method enables AI for safety-critical situations

The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.

GPT-Lab Sep 10, 2026

Responsible AI Must Consider Its Afterlife

AI may appear weightless, but every model depends on physical infrastructure. To understand responsible AI, we need to look beyond algorithms and consider the entire lifecycle of the hardware behind them. The post Responsible AI Must Consider Its Afterlife appeared first on GPT-Lab.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.