Back

Digital genetic counselling services for cascade cardiogenetic testing: a focus group study on proband, relative, and provider perspectives

van Lingen, M. N.; van Till, S. A. L.; Giesbertz, N. A. A.; Beinema, T. C.; Ausems, M. G. E. M.; Klaassen, R.; Cornel, M. C.; van den Heuvel, L. M.; van Tintelen, J. P.

2024-12-05 genetic and genomic medicine
10.1101/2024.11.27.24318108 medRxiv
Show abstract

Digital interventions are potentially promising to improve accessibility and efficiency of genetic counselling services. However, current literature on stakeholder perspectives towards digital tools for cascade testing is limited. Therefore, this focus group study aimed to gain insights into the attitude and perspectives of probands, at-risk relatives (ARR), and genetic healthcare professionals (HCP) towards digital innovations for assistance with both pre-test and post-test counselling and cascade genetic testing in cardiogenetics. We conducted seven online focus groups, which where transcribed and thematically analysed. In total, 37 individuals participated (10 probands, 11 ARR and 16 HCP). Thematic analysis of focus group transcripts showed a first theme of (1) acceptability of digital tools. Other identified themes were defined as domains where digital tools impact traditional, in-person clinical genetic care, being: (2) family communication, (3) decision-making, (4) care relations, and (5) the genetic care system. Stakeholders expressed a predominantly positive attitude towards digitisation of (parts of) the predictive genetic counselling in cardiogenetics, under the condition that access to human contact is preserved. In the clinical setting of predictive counselling, efforts should be made to ensure access to genetic services for all ARR and to protect in-person involvement of HCP.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.