Describing Health Service Platform Architectures: A Guiding Framework
Bibliographic record
Abstract
Health service platforms (HSPs) facilitate new ways of delivering health care, in order to improve care effectiveness and efficiency. HSPs are a sub-type of digital platforms that have a different purpose compared to market-oriented platforms for product innovation or economic transactions. Hence, traditional descriptions of digital platform architecture, such as the layered modular architecture, may not be sufficient to capture the essential features of HSPs. We create an initial framework for describing HSP architectures in a manner that reflects their purpose, drawing from literature on Service-Dominant Logic and architectural patterns. The framework will be used to guide a multiple-case study of HSP architecture. The results of this research will extend current conceptualizations of digital platform architectures and provide a systematic approach for designing or evolving HSP platform architectures in a manner that better meets patients’ and other health care stakeholders’ needs.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".