Exploration of benefits realisation management for teledermatology scale-up framework development and sustainable scaling
Bibliographic record
Abstract
Introduction Realisation of proven telemedicine scale-up benefits is a key consideration for South Africa, a developing country with a quadruple disease burden , inequitable access to healthcare, and ineffective and inefficient specialist referral pathways. Proven benefits of teledermatology include virtually enhancing access of rural communities to scarce urban specialist dermatologists, reducing time to triage of skin lesions , frequently an initial sign of underlying disease, and timely treatment initiation. Benefits realisation management (BRM) is a recognised means of managing how resources are invested into making effective and desirable changes, and enhancing project and programme success. The need for this study was identified in a recent review and critique of teledermatology and related scale-up frameworks. This study explores the use of BRM as a whole life-cycle approach applied to ehealth or teledermatology related scale-up framework development, and to sustain benefits of scaling ehealth or healthcare service delivery interventions. Material and methods A structured search of academic literature was performed using Scopus, Science Direct, PubMed, IEEE Explore, Web of Science, and Google Scholar. The key terms Benefits Realisation Management or BRM were linked with: a) ehealth or telehealth or telemedicine or teledermatology related scale-up framework development, and b) sustainability of interventions such as scale-up. Subsequent Google searching explored grey literature evidence for BRM in ehealth. Results The academic literature searches could not identify peer-reviewed literature to support the use or consideration of BRM as a whole life-cycle approach within ehealth or teledermatology related scale-up framework development. Discussion However, the results showed that BRM has been used in related domains to promote sustainability of non-healthcare interventions. In contrast the grey literature provided evidence of limited use of BRM within healthcare and within ehealth. Conclusions There is renewed support for the use of BRM as a whole life-cycle approach for management disciplines that focus on change, project, programme, and portfolio management. Although limited, the academic and grey literature provides support for consideration of BRM in ehealth, and for the use of BRM to ensure sustainability. Future research should explore the use of BRM as a whole life-cycle approach for ehealth implementation, and teledermatology scale-up framework development in particular, including its possible contribution to sustaining scaled-up teledermatology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.079 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".