Navigating the Systemic Conditions of a Digital Health Ecosystem in Alberta, Canada: Embedded Case Study
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, the activities of a diverse set of stakeholders within a digital health ecosystem require coordination. However, the antecedents of the coordination needed for an effective digital health ecosystem are not well understood. OBJECTIVE: The purpose of this study was to investigate the systemic conditions of the digital health ecosystem in Alberta, Canada, as critical antecedents to ecosystem coordination from the perspective of the authors as applicants to an innovative digital health funding program embedded within the larger digital health ecosystem of innovators or entrepreneurs, health system leaders, support partners, and funders. METHODS: We employed a qualitative embedded case study of the systemic conditions within the digital health ecosystem in Alberta, Canada (main case) using semistructured interviews with 36 stakeholders representing innovators or entrepreneurs, health system leaders, support partners, and funders (subcases). The interviews were conducted over a 2-month period between May 26 and July 22, 2021. Data were coded for key themes and synthesized around 5 propositions developed from academic publications and policy reports. RESULTS: The findings indicated varying levels of support for each proposition, with moderate support for accessing real problems, data, training, and space for evaluations. However, the most fundamental gap appears to be in ecosystem navigation, in particular, the absence of intermediaries (eg, individuals, organizations, and technology) to provide guidance on the 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, especially those without prior health care 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 and those dependent on it grow.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".