Navigating the Systemic Conditions of a Digital Health Ecosystem in Alberta, Canada (Preprint)
Bibliographic record
Abstract
BACKGROUND 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. OBJECTIVE 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. METHODS 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. RESULTS 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. CONCLUSIONS 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.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".