Special Interest: What are we really communicating? Digital Health and Data Enabling Integrated Care Special Interest Group Workshop
Bibliographic record
Abstract
Digital Health and Data Enabling Integrated Care SIG members have been working together on the priority area of interoperability. A key international challenge is around standardizing information sharing, including identifying what data is shared and how information maintains its value as it moves across professional and organizational boundaries.To refine and build on the previous two SIG meetings that have focused on the challenge of interoperability, this workshop will help to establish a process for creating Minimum Data Sets for integrated care to streamline interoperability efforts. Delegates will not only engage in an exercise to help identify a minimum data set from the International Patient Summary, but will learn a transferable process that they can apply to other data and information resources in their own networks.AudienceAll existing and newly interested members of the SIG are welcome. Our growing membership consists of patients and family caregivers, researchers, frontline providers, managers, system leaders and decision-makers, policy makers, informaticians, and industry partners. We are particularly keen to grow our North American membership at NACIC24 in Calgary. Approach: The session will begin with a short introduction from SIG leads (C. Steele Gray, L. Lewis, I. Meyer) who will provide a summary of current global standardization efforts, such as the use of openEHR and SNOMED-CT including a specific example from Estonia. SIG guest Matthew Meyer will then present the approach being taken by the Middlesex London Ontario Health Team around developing a Minimum Data Set to guide their interoperability efforts across organizational partners.Delegates in the session will then break into small working groups to go through the exercise where they work from an existing standard, the International Patient Summary, and identify what information is needed for which type of partner (patient/carer, primary care hospital, home care, social and community services, etc...), to establish a Minimum Data Set for this summary. Delegates will then be asked to review the standard and discuss the ease of standardization, and the benefits/risks of including different components for different partners. Finally, delegates will recommend prioritization of elements. Facilitators will guide delegates through the process, using sticky notes and stickers to record and prioritize ideas.Structure: ) Introduction and data standardization overview (5 minutes); 2) Matt Meyer presentation (5 minutes); 3) Table Discussions (25 minutes); 4) Report back (5 minutes)OutcomesWhile there is only 5 minutes set aside for a report back, the facilitators will collect all the work generated by the tables to be included in a white paper report that is being generated by the SIG leads in collaboration with members on "Interoperability for Integrated Care" which is being built from the SIG meetings on this topic from the last two years. Patient and family carer involvement has be significant in past workshops, including the ICIC24 workshop which was co-developed and co-led with a patient partner who is an ongoing active member. And we will be inviting patient and family carer delegates interested in digital health to support this workshop activity. This final session will round out the report, offering what the delegates worked on in the session, but also presenting a potentially transferable process for developing minimum data sets that can be applied by other organizations working on this challenge. The SIG anticipates releasing the white paper by the end of 2024.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.041 | 0.018 |
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".