Utility and Perceived Value of a Provincial Digital Diagnostic Imaging Repository: Multimethod Study (Preprint)
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> Timely and comprehensive diagnostic image sharing across institutional and regional boundaries can produce multiple benefits while supporting integrated models of care. In Ontario, Canada, the Diagnostic Imaging Common Service (DICS) was created as a centralized imaging repository to enable the sharing and viewing of diagnostic images and associated reports across hospital-based and community-based clinicians throughout the province. </sec> <sec> <title>OBJECTIVE</title> The aims of this study were as follows: (1) to explore real-world utilization and perceived clinical value of the DICS following the provision of system-wide access and (2) to identify strategies to optimize the technology platform functionality and encourage adoption. </sec> <sec> <title>METHODS</title> This multimethod study included semistructured interviews with physicians and administrative stakeholders and descriptive analysis of the current DICS usage data. </sec> <sec> <title>RESULTS</title> In this study, 41 participants were interviewed, that is, 34 physicians and 7 administrative stakeholders. The following 4 key themes emerged: (1) utilization of the DICS depended on the awareness of the technology and the preferred channels for accessing images, which varied widely, (2) clinical responsibilities and available institutional resources were the drivers of utilization (or lack thereof), (3) centralized image repositories were perceived to offer value at the patient, clinician, and health care system levels, and (4) the enabling factors to realize value included aspects of technology infrastructure (ie, available functionality) alongside policy supports. High-volume DICS usage was not evenly distributed throughout the province. </sec> <sec> <title>CONCLUSIONS</title> Suboptimal adoption of the DICS was driven by poor awareness and variations in the clinical workflow. Alignment with physician workflow, policy supports, and investment in key technological features and infrastructure would improve functionality and data comprehensiveness, thereby optimizing health system performance, patient and provider experience, population health, and health care costs. </sec>
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".