The ‘wrong pocket’ problem as a barrier to the integration of telehealth in health organisations and systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it