Using Longitudinal Social Network Methods to Evaluate the Impact of eHealth Innovation Ecosystem Development: A Case Study of the EPIC project

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Abstract

Background Rubens et al (2011) describes ‘innovation ecosystems’ as inter-organizational, political, economic, environmental, and technological systems of innovation that are conducive to business growth. eHealth or digital health is the use of apps, websites, internet of things, robotics etc. in health and care. Figure 1. eHealth Innovation Ecosystems (Source: ECH Alliance) Numerous cities and regions around the world are trying to establish eHealth ecosystems where stakeholders on the demand side (clinicians, patients, carers etc.) can work with providers (SMEs, digital companies, services etc.) and education and research to provide innovation ecosystems in eHealth (Figure 1.). In order to foster a vibrant ecosystem, it is important to evaluate the extent to which a community of stakeholders currently exists within a region. Facilitating an increase in the connectedness and opportunities for collaboration between e-health stakeholders across the region should help to foster a sustainable environment of e-health innovation for the future. Aim We are assessing the social network impact of a 3-year intervention to develop an eHealth ecosystem. Results from the first 24 months will be presented. Methods Setting: EPIC (eHealth Productivity and Innovation in Cornwall and the Isles of Scilly (CIoS)) is a European Regional Development Fund project developing an eHealth innovation ecosystem. EPIC was funded in recognition of challenges of delivering high quality and cost effective healthcare in a highly rural area with an increasingly ageing population. EPIC’s aim is to stimulate economic growth and create a sustainable eHealth sector in this underdeveloped area. EPIC has held multiple networking activities and events to connect e-health innovators and stakeholders, and aims to establish mechanisms to sustain the developing ecosystem. Data Collection: Network data comprising awareness and collaboration ties were collected through online surveys at multiple timestamps throughout the project from individuals representing stakeholder groups. Data analysis: Network data was analysed descriptively in the social network package ORA and modelled in the R package RSiena to explore how the network structures within the ecosystem develops over time. Tailored interventions: Subsequent network interventions were implemented to connect organisations that were not but which we believed should be connected based on their stage of business development and their field of work. Results and Discussion Baseline findings showed limited rates of connectivity and collaboration within the ecosystem between technology companies, local clinical commissioners, healthcare organisations and groups representing patients. If not addressed, poor networking may lead to: i) difficulty for technology companies in the region to establish themselves or diversify product lines into the eHealth market; ii) lack of user-led idea generation for new products and an inability for technology companies to conduct usability testing within the region; iii) challenges with getting products commissioned; and iv) potential resistance to adoption and successful implementation of new technology developments. Conclusion Social network analysis seems a useful way of monitoring and evaluating the impact of network activities and events on developing an innovation ecosystem. By the conference we will present the latest findings on the success or otherwise of EPIC in developing the network.

Publication Date

2019-06-18

Embargo Period

9999-12-31

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