Study into training around remote services highlights value of “on-the-job” experience

An article published in the British Journal of General Practice has explored the training needs for staff providing remote services in general practice, finding that current training tends to be didactic in nature although participants say they value experience, informal discussion and on-the-job learning methods such as shadowing.

For the purposes of the study a subset of data was created for targeted analysis, made up of descriptions of the training people received or would like to receive, alongside reflections on training needs and preferred learning methods.  This data was supplemented with other sources including case studies, stakeholder interviews, the ongoing Remote by Default 2 (RBD2) dataset and a national survey of nearly 800 primary care staff from Health Education England (merged with NHS England in April 2023).

Findings highlighted that “time, headspace, and resources” for training in remote service provision is “very limited”, with challenges relating to workload pressures, staff shortages and negative response from “a small minority of patients”. The authors indicated that this stress could lead to a loss of motivation and receptivity toward training.

Perceived training needs identified by the study included technical skills around the use of remote technology; communication and clinical skills using remote technology; implementation skills relating to the embedding of remote encounters, and pedagogical skills on how to train staff or patients. Staff often felt they had quickly developed skills and confidence after being “thrown in the deep end”, or that they knew how to use the technologies but not confidently or at the required pace.

On participants’ experiences of training, the study found that provision “varied considerably” across its sample of 11 general practices, with some offering formal training linked with locality-wide digital capacity-building initiatives; some having no dedicated time for training; and some senior or digitally-confident staff taking on the responsibility for training others as digital champions or super users. Trainees especially felt they “learnt a lot from shadowing experienced clinicians undertaking telephone consultations” or by having sessions to follow up on how to use the technology within practice workflows, and “assistance transcended traditional hierarchies”, with observations including doctors asking receptionists to explain new systems.

Discussing findings in relation to existing research on the topic, authors considered how new clinical trainees identified training priorities including “acquiring basic technological skills, becoming proficient in triage, mastering issues such as privacy, consent, and information governance, and developing their communication and clinical skills”. Current training was found to be “didactic and focused on particular digital technologies”, with participants valuing didactic training to acquire basic competence, but feeling that their capability and confidence around complex decision-making was generally acquired through experience, informal conversation and on-the-job methods such as shadowing.

The authors’ concluding remarks were that “the distributed nature of remote and digital work mean that team training and system learning must be part of the overall training strategy”, and that “training programmes and policies need to reflect these important pedagogical insights”.

Citation: Trisha Greenhalgh, Rebecca Payne, Nina Hemmings, Helen Leach, Isabel Hanson, Anwar Khan, Lisa Miller, Emma Ladds, Aileen Clarke, Sara E Shaw, Francesca Dakin, Sietse Wieringa, Sarah Rybczynska-Bunt, Stuart D Faulkner, Richard Byng, Asli Kalin, Lucy Moore, Joseph Wherton, Laiba Husain and Rebecca Rosen. Training needs for staff providing remote services in general practice: a mixed-methods study. 

Elsewhere, a journal published in The Lancet recently introduced the ‘Medical AI Data for All (MAIDA)’ initiative, exploring the challenges of representativeness in AI interpretation of medical imaging.