People, process and technology: A model for digital transformation of healthcare
Bibliographic record
Abstract
COVID-19 has brought forward unprecedented challenges for healthcare systems worldwide. As healthcare moves into a state of COVID-19 recovery, we must reflect on lessons learned from the COVID-19 pandemic to create a more resilient future. One of the most critical outcomes this pandemic has highlighted, is the role of digital technology and digital health in transforming health care services. We suggest that by focusing on people, processes and technologies, healthcare systems can not only recover from the impact of COVID-19, but also transform healthcare from the traditional disease management system of today into the post pandemic health system of tomorrow, one that is modernised to align with the needs of the populations that health systems serve. Currently, health care systems are predominantly transactional, delivering care focused on disease management to restore health and/or manage acute health conditions. A digital health ecosystem offers a strategy to realise the full health potential of every human everywhere, from the smallest village to the most complex health care environments in health systems. Digital health ecosystems identify and track progress towards health goals, tracking outcomes and health risks at the individual and population level, which informs care approaches that are personalised to people and populations and are focused on mitigating risks in order to support and sustain health and wellness. The sustainability of healthcare systems and the health of global populations will be influenced by the rate of adoption and scalability of digital health. This paper will examine digital health transformation as a strategy to overcome the challenges health systems are facing and to advance the sustainability of health systems globally.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.021 | 0.033 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".