The ‘wrong pocket’ problem as a barrier to the integration of telehealth in health organisations and systems
Bibliographic record
Abstract
The COVID-19 pandemic has accelerated the deployment of telehealth services in many countries around the world. It also revealed many barriers and challenges to the use of digital health technologies in health organisations and systems that have persisted for decades. One of these barriers is what is known as the 'wrong pocket' problem - where an organisation or sector makes expenditures and investments to address a given problem, but the benefits (return on investment) are captured by another organisation or sector (the wrong pocket). This problem is the origin of many difficulties in public policies and programmes (e.g. education, environment, justice and public health), especially in terms of sustainability and scaling-up of technology and innovation. In this essay/perspective, we address the wrong pocket problem in the context of a major telehealth project in Canada. We show how the problem of sharing investments and expenses, as well as the redistribution of economies among the different stakeholders involved, may have threatened the sustainability and scaling-up of this project, even though it has demonstrated the clinical utility and contributed to improving the health of populations. In conclusion, the wrong pocket problem may be decisive in the reduced take-up, and potential failure, of certain telehealth programmes and policies. It is not enough for a telehealth service to be clinically relevant and 'efficient', it must also be mutually beneficial to the various stakeholders involved, particularly in terms of the equitable sharing of costs and benefits (return on investment) associated with the implementation of this new service model. Finally, the wrong pocket concept offers a helpful lens for studying the success, sustainability, and scale-up of digital transformations in health organisations and systems. This needs to be considered in future research and evaluations in the field.
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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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.054 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.011 | 0.015 |
| 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".