Reflections on Digital Health Tools for Respiratory Applications
Bibliographic record
Abstract
Background: In May 2019, the International Society for Aerosols in Medicine (ISAM) and the International Pharmaceutical Aerosol Consortium on Regulation and Science (IPAC-RS) held a one-day workshop on digital health tools for respiratory therapies, with speakers representing views of patients, pharmaceutical and technology companies, physicians, and payors. Methods: Workshop presentations, audience discussions, and research of relevant literature form the basis of this article. Results: Conversations and publications to date illustrate the broad, and growing, interest in digital technologies, but also highlight a few gaps. The key messages are: (1) There are many great technologies, from simple to complex, but there is still an imperfect understanding of the problems that digital tools need to solve. The current approach often starts with a technology “solution” and then identifying the problems that it could address. This is a poor design practice: the offered technology may not represent the optimum solution to that problem; and that “problem” itself may not represent issues that patients or the health care system deem to be of high priority. (2) Respiratory medicine currently lacks widely accepted and easily measured biomarkers or hand-held diagnostic technologies that could be commercialized for complex/heterogeneous diseases such as asthma (unlike in other areas, e.g., diabetes). (3) There are no obvious solutions that integrate the perspectives of the many stakeholders—from patients to physicians to payors—to make the new field commercially viable, and the incentives of various parties in the health care system are misaligned. (4) A continued dialog, information sharing, mutual education, and collaborations are important in bringing the promise of digital respiratory medicine to fruition. Conclusions: Digital tools for diagnosis and management of respiratory conditions are an active area of research and development but long-term success in this field will depend on identifying real needs, and integrating the often-diverging interests of the various partners in each country's health care system.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".