Literature DB >> 31039121

Digital innovation evaluation: user perceptions of innovation readiness, digital confidence, innovation adoption, user experience and behaviour change.

Tim Benson1,2.   

Abstract

BACKGROUND: Innovation spread is a key policy objective for health systems world-wide, but adoption success varies enormously. We have developed a set of short generic user-reported measures to help understand how and why healthcare innovations spread. This work builds on the literature and on practical experience in developing and using patient-reported outcome measures. MEASURES: The Innovation Readiness Score measures user perceptions of how much they are open to and up-to-date with new ideas, and whether their organisations are receptive to and capable of innovation. It is based on Rogers' classification of innovativeness (innovator, early adopter, early majority, etc).The Digital Confidence Score rates users' digital literacy and confidence to use digital products, with dimensions of familiarity, social pressure, support and digital self-efficacy.The Innovation Adoption Score rates the adoption process in terms of coherence and reflective thought before, during and after implementation. It is based on Normalisation Process Theory.The User Satisfaction measure assesses a digital product in terms of usefulness, ease of use, support and satisfaction.The Behaviour Change measure covers user perceptions of their capability, opportunity and motivation to change behaviour, based on the COM-B model.These measures have been mapped onto Greenhalgh's NASSS Framework (non-adoption, abandonment and challenges to scale-up, spread and sustainability of health and care technologies).
CONCLUSION: These tools measure different aspects of digital health innovations and may help predict the success of innovation dissemination, diffusion and spread programmes. © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Entities:  

Keywords:  behaviour change; computer literacy; consumer behaviour; innovation diffusion; program evaluation

Mesh:

Year:  2019        PMID: 31039121     DOI: 10.1136/bmjhci-2019-000018

Source DB:  PubMed          Journal:  BMJ Health Care Inform        ISSN: 2632-1009


  6 in total

1.  Adopting Patient Portals in Hospitals: Qualitative Study.

Authors:  Pauline Hulter; Bettine Pluut; Christine Leenen-Brinkhuis; Marleen de Mul; Kees Ahaus; Anne Marie Weggelaar-Jansen
Journal:  J Med Internet Res       Date:  2020-05-19       Impact factor: 5.428

2.  Adopt, adapt, or abandon technology-supported person-centred care initiatives: healthcare providers' beliefs matter.

Authors:  Kari Dyb; Gro Rosvold Berntsen; Lisbeth Kvam
Journal:  BMC Health Serv Res       Date:  2021-03-17       Impact factor: 2.655

3.  Readiness for five digital technologies in general practice: perceptions of staff in one part of southern England.

Authors:  Matthew Hammerton; Tim Benson; Andrew Sibley
Journal:  BMJ Open Qual       Date:  2022-06

Review 4.  Factors contributing to innovation readiness in health care organizations: a scoping review.

Authors:  Monique W van den Hoed; Ramona Backhaus; Erica de Vries; Jan P H Hamers; Ramon Daniëls
Journal:  BMC Health Serv Res       Date:  2022-08-05       Impact factor: 2.908

5.  The NASSS framework for ex post theorisation of technology-supported change in healthcare: worked example of the TORPEDO programme.

Authors:  Seye Abimbola; Bindu Patel; David Peiris; Anushka Patel; Mark Harris; Tim Usherwood; Trisha Greenhalgh
Journal:  BMC Med       Date:  2019-12-30       Impact factor: 8.775

6.  Deployment of artificial intelligence for radiographic diagnosis of COVID-19 pneumonia in the emergency department.

Authors:  Morgan Carlile; Brian Hurt; Albert Hsiao; Michael Hogarth; Christopher A Longhurst; Christian Dameff
Journal:  J Am Coll Emerg Physicians Open       Date:  2020-11-05
  6 in total

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