Navigating the Systemic Conditions of a Digital Health Ecosystem in Alberta, Canada (Preprint)
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> Digital health promises numerous value-creating outcomes. These include improved health, reduced costs, and the creation of lucrative markets, which in turn provide high quality employment, productivity growth, and a climate that attracts investment. For this value creation and capture to occur, the activities of a diverse set of stakeholders within a digital health ecosystem need coordination. However, the antecedents the coordination needed for an effective digital health ecosystem are not well understood. </sec> <sec> <title>OBJECTIVE</title> The purpose of this study is to investigate the systemic conditions of the digital health ecosystem in Alberta, Canada as critical antecedents to ecosystem coordination. </sec> <sec> <title>METHODS</title> We employed a qualitative case study of the systemic conditions within the digital health ecosystem in Alberta, Canada using semi-structured interviews with 36 stakeholders representing innovators-entrepreneurs, health system leaders, support partners, and funders. Data were coded for key themes and synthesized around five propositions. </sec> <sec> <title>RESULTS</title> The findings indicate varying levels of support for each proposition, including accessing real problems, data, training, and space for evaluations. However, the most foundational gap appears to be in ecosystem navigation. In particular, the absence of intermediaries to provide guidance on available support services and dependencies among the various ecosystem actors and programs. </sec> <sec> <title>CONCLUSIONS</title> Navigating the systemic conditions of the digital health ecosystem is extremely challenging for entrepreneurs without prior healthcare experience, and this remains an issue even for those with such experience. Policy interventions aimed at increasing collaboration among ecosystem support providers, along with tools and incentives to ensure coordination, are essential as the ecosystem grows. </sec>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".