Open Innovation and Involvement of End-Users in the Medical Device Technologies’ Design & Development Process: End-Users’ Perspectives
Bibliographic record
Abstract
Literature and Regulatory bodies growing interest in End-Users’ implication in Medical Device Technologies (MDTs) development processes are a clear proof of the importance of this involvement and the positive impacts it can have on the development, implementation and use of MDTs, thus subsequent improvements in healthcare services’ delivery. However, existing research has mainly been focused on the theoretical importance of this involvement, and the manufacturers’ views and attitudes, with little attention focused on End-Users’ concerns and thoughts concerning this process. The aim of this paper is to identify the perspectives of Nurses and Doctors as the best representatives of MDT End-Users, regarding their own involvement in MDT development processes. The results of 49 semi-structured interviews conducted with End-Users, helped identify a number of high-level themes: 1) End-Users’ conflicting perspective with that of manufacturers regarding the impact of their involvement in MDT development; 2) End-Users’ concerns regarding the nature of their contribution, its level and their suggestions for a potential amelioration. These results reveal the importance End-Users attach to their involvement in MDT development processes, and the added value they perceive for the proper development as well as upgrade of MDTs. It also underlines many concerns they have regarding the current patterns of involvement, and suggests their recommendations for a standardization of this process, with input on forms and levels of involvement.
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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 | 0.038 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".