Key considerations when developing and implementing digital technology for early detection of dementia causing diseases: a qualitative study
Authors: Wilson S , Tolley C , Mc Ardle R , Beswick E , Slight S 
Published: November 2023

Introduction

There has been substantial growth in research exploring use of digital health technologies (DHT) to improve global population health. Harnessing the potential of DHT for the early detection of neurodegenerative diseases is one such area.1 According to the National Institute for Health and Care Excellence, DHTs need to be credible for UK Healthcare Professionals (HCPs); HCPs should be involved in the development and implementation process of DHTs to ensure that their needs are met and to promote the successful adoption into healthcare systems.2

Aim

To explore HCPs’ perspectives on key considerations of developing and implementing DHT for the early detection of dementia causing diseases, such as Alzheimer’s, within the National Health Service (NHS).

Methods

Ethical approval was granted from Newcastle University Ethics Committee. HCPs with patient-facing roles within primary or secondary care NHS settings were recruited through various online networks alongside a snowball sampling approach. Participants took part in a semi-structured audio-recorded interview exploring their experience of current diagnostic practices, opinions on the early detection of dementia causing diseases and the use of DHT to aid this approach, and the challenges of implementing such interventions into healthcare. The framework approach was used to thematically analyse the verbatim transcripts to identify core concepts and themes emerging within the interviews. A list of core concepts and themes was applied systematically to the whole data set using QSR N-Vivo (Version 1.6.1). The research team then used the ‘constant comparison’ technique to move backwards and forwards between these data and evolve explanations until a fit was made.

Results

Eighteen interviews were conducted with 11 primary and seven secondary care HCPs. Our analysis highlighted three core concepts, centring around considerations for healthcare service users, HCPs, and the DHT when developing and implementing DHT for the early detection of dementia causing diseases. HCPs recognised the potential benefits of providing an early detection of dementia causing diseases to healthcare services users, but only if a suitable intervention was available to allow individuals to potentially reduce their risk. Additionally, HCPs were concerned about the impact on health inequity, such as accessibility of this type of service to individuals who have disabilities or could be possibly digitally excluded. Many HCPs were also concerned about the impact on the healthcare system, with fears that it could overburden the NHS if sufficient resources and support were not provided for its implementation. In regard to the DHT, HCPs were positive about the possibility of a DHT to remotely capture real time data and felt it would be important to capture different modalities to help differentiate between dementia causing diseases.

Discussion/Conclusion

This study highlights the need to design a DHT for the early detection of dementia causing diseases that is accessible to users, to promote health equity. This study also highlights potential barriers for researchers when implementing a DHT into the NHS, although it cannot be assumed that these are the only barriers that may be encountered; developers will need to consider this so that a future DHT meet HCPs’ needs.

References

1. Frey AL, Karran M, Jimenez RC, Baxter J, Adeogun M, Bose N, Chan D, Crawford J, Dagum P, Everson R, Hinds C. Harnessing the Potential of Digital Technologies for the Early Detection of Neurodegenerative Diseases (EDoN).

2. National Institute of Health Care Excellence (NICE). (2018, December 10). Evidence standards framework for Digital Heath Technologies (ECD7). https://www.nice.org.uk/corporate/ecd7/resources/evidence-standards-framework-for-digital-health-technologies-pdf-1124017457605