Seeking Meaningful Innovation: Lessons Learned Developing, Evaluating, and Implementing the Electronic Patient-Reported Outcome Tool
Bibliographic record
Abstract
Digital health solutions, in particular information communication technologies, often experience implementation failures leading to slower adoption than expected. This implementation challenge has spurred the development of frameworks to help navigate this uncertain and messy process. These frameworks point to environmental, organizational, individual, and technological factors that can drive or hinder implementation, with some in the field suggesting that perceived value may play a pivotal role. However, the concept of value can have varying meanings and be challenging to operationalize as a means to support implementation. Attending to philosophical and psychological meaningfulness for users and organizations in which technologies are adopted may offer a useful lens, by linking perceived value to individual behavior changes often required as part of implementing digital health technologies. Lessons learned from developing, evaluating, and implementing the electronic Patient-Reported Outcome (ePRO) tool demonstrate how qualitative methods can be used to uncover meaningfulness. By drawing from this example and other similar studies, this viewpoint offers suggestions on how future inquiry could deepen an understanding of meaningful innovation to help drive the implementation of digital health technologies.
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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 | 0.134 | 0.144 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".