The Translational Work of Interoperability: Digital Health and Data Enabling Integrated Care Special Interest Group Workshop
Bibliographic record
Abstract
Introduction: Members of the Digital Health and Data Enabling Integrated Care Special Interest Group came together at ICIC22 to discuss priority issues. One critical international struggle is the challenge of interoperability to support information and data sharing and communication as cornerstones of integrated health and social care systems. Aims and Objectives: The emphasis of SIG work in 2023-2024 will be to deeply engage with the critical challenge of interoperability. From an integrated care perspective what is required is understanding the translational work needed to make the sharing of information and data useful and meaningful to those who need it. In this meeting, delegates will work together to unpack the translation problem of interoperability in terms of translating information between stakeholders (e.g. between patients, families and their care teams), between disciplines (e.g. between different clinicians and providers), and between sectors (e.g. between health and social care sectors), with the aim to share current work and identify knowledge and practice gaps that we can address as a SIG for the next year. Audience: All existing and newly interested members of the SIG are welcome to join for this discussion. Our membership consists of patients and family caregivers, researchers, frontline providers, managers, system leaders and decision-makers, policy makers, informaticians, and industry partners. Structure and engagement: This session will use the hour largely to engage with delegates to meet session objectives. To set up the discussion we will begin with a short introduction from SIG leads (C. Steele Gray, L. Lewis, I. Meyer) followed by SIG member (J. Piera-Jiménez) to present the case example of Catalonia’s Digital Health Strategy and their efforts towards improved interoperability that addresses limitations of current information systems, reduces loss of meaning in information exchange, and advances standardization of care processes. Delegates will next break into small groups (5-6 per table with one facilitator), to discuss what translation needs would be required to implement an interoperable system (like Catalonia’s), and how those needs could be met (e.g. consensus group discussions, technological solutions like Artificial Intelligence and Natural Language Processing). Facilitators will use a live Google Jamboard to record ideas shared by the individual groups. Structure: 1) Introduction (10 minutes); 2) Catalonia Example (10 minutes); 3) Table Discussions (25 minutes); 4) Identification of current work and gaps (15 minutes) Summarizing take home messages: In the final 15 minutes of the session the full group will reconvene to review the live Google Jamboards and engage in priority setting of translation needs. Delegates will be asked to identify where they may already be working on identified needs, and where current gaps might exist. This exercise will allow us to bring together sub-groups to tackle translation problems who would then engage in webinars and/or discussion groups between ICIC23 and ICIC24 with the aim to generate a white paper, report, or journal special issue that would bring together current and new knowledge generated by SIG members of the translational work of interoperability.
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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 | 0.030 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".