University of Hertfordshire

By the same authors

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Original languageEnglish
Pages1
Number of pages40
Publication statusPublished - 25 Aug 2019
Event2019 International Conference on Digital Health and Medical Analytics - Digitalisation Adding Value to Healthcare (DHA 2019) - Zhengzhou, China
Duration: 23 Aug 201925 Aug 2019
https://www.dha2019.org/

Conference

Conference2019 International Conference on Digital Health and Medical Analytics - Digitalisation Adding Value to Healthcare (DHA 2019)
CountryChina
CityZhengzhou
Period23/08/1925/08/19
Internet address

Abstract

The rapidly ageing population throughout the world poses a significant challenge for most countries. For instance, the ageing of the UK's population, combined with restrictions on public spending, will create strong latent demand for eHealth over the next 20 years. For many older people, institutionalised inpatient care is not only expensive, but also less attractive than being cared for in their own homes. eHealth innovative services are often regarded as promising avenues that will allow health and social care systems to cope with these challenges and to improve the quality of lives for older citizens. However, the user uptake of eHealth is surprisingly low in the UK and the successful deployment of such products and services is not guaranteed unless the interests of all the key stakeholders in health and social care are adequately addressed. While many previous studies addressed the technological aspects of eHealth innovations, the business models underpinning such innovations are often overlooked. In this study, through case studies of relevant organisations and facilitated workshops, we examine the key characteristics of the eHealth market from the perspective of business model development in order to develop new business models for sustainable and scalable market development of eHealth innovations. The study suggests that successful business models are likely to be the ones that best conform to DeLone and McLean Information Systems (D&M IS) Success Model Theory.

ID: 17769653