Connolly, Justin, Anthony Staines, Regina Connolly, Andrew Boilson, Paul Davis, Dale Weston, & Natasha Bloodworth (2018, September 6-8). Evaluating impact of an emerging big health data platform: A logic model and Q-methodology approach. In Proceedings of the ENTRENOVA – ENTerprise REsearch InNOVAtion Conference, Split, Croatia (pp. 127-133). Zagreb, Croatia: IRENET – Society for Advancing Innovation and Research in Economy. (doi: https://www.econstor.eu/handle/10419/183823) (Link: http://hdl.handle.net/10419/183823) (Download: https://www.econstor.eu/bitstream/10419/183823/1/16-ENT-2018-Connolly-127-133.pdf)
Abstract: Despite advances in technology and medical science, modern health-based projects are open to systemic failure due to many factors. These include I.T. developer’s lack of awareness with regard to end-user needs, poor communication amongst all parties concerned and inappropriate or inadequate tests of the emerging system. Other issues may be external (e.g. political and legal) such as sharing of patient data and issues surrounding consent. The goal of this paper is to take a major health-based European model in current development and explore how it addresses the needs of four institutions in four different countries, and how it will meet their respective needs. The evaluation was designed within a Logic Model, and uses the Framework approach, and Q-Methodology to assess both impact and evaluation. Data will be collected through longitudinal semi-structured interviews and Q-scoring with principal stakeholders and developers at each stage of the project. This approach, recurring interviews with the same key players in the project, will help ensure that there is mutual understanding between I.T. developers and end-users of the system. The final system is meant to provide effective health-based decision support systems for policy makers.
Justin Connolly <firstname.lastname@example.org> is in School of Nursing and Human Sciences, Dublin City University, Dublin, Ireland.