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Record W4409337153 · doi:10.5334/ijic.icic24578

Person-Centred Interoperability: Digital Health and Data Enabling Integrated Care Special Interest Group Workshop

2025· article· en· W4409337153 on OpenAlexaboutno aff
Carolyn Steele Gray, Ingo Meyer, Leo Lewis, Hilary Horlock

Bibliographic record

VenueInternational Journal of Integrated Care · 2025
Typearticle
Languageen
FieldComputer Science
TopicPersona Design and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsInteroperabilityIntegrated careDigital healthHealth careGroup (periodic table)Special Interest GroupKnowledge managementComputer scienceProcess managementNursingData scienceWorld Wide WebMedicineBusinessPolitical science

Abstract

fetched live from OpenAlex

Introduction: Members of the Digital Health and Data Enabling Integrated Care Special Interest Group came together at ICIC23 to discuss priority issues around interoperability and information data sharing. This year we will advance this conversation by focusing on how we can embed interoperable digital solutions and data systems into caring communities of patients, caregivers, and and decision merks in a way that maximizes fit for purpose and ultimate impact. Aims and Objectives: The SIG will engage participants in a critically important discussion around how and why interoperable data and digital systems need to be embedded in their communities to support person-centred care delivery. Last year’s session included a roundtable discussion about how patients, caregivers, and communities need to be able to access data, interpret and understand information. Three core issues came from our patients and caregiver partners in that session including: 1) the need to standardise information sharing with both patients and caregivers in a way that is meaningful and useful; 2) how we ensure interoperable technologies support, maintain, and grow important interactions and relationships; and 3) building a person-centred lens into policy drivers for interoperable systems. We will build on these and share current and emerging work around these three areas of interest; focusing on how to best collect and integrate the needs of community members into the ongoing development of upcoming and existing digital and integrated care programs. Audience: All existing and newly interested members of the SIG are welcome to join this discussion. Our growing 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) who will talk about each of the three issues in relation to current work and evidence. SIG member and patient partner Hilary Horlock will then provide her perspective on these three issues, to help understand what standardised information can look like, how relationships can be supported and maintained, and building from her experience driving policy in Canada, how digital health policies can maintain service users at the centre. Delegates in the session will then break into working groups to address each of the three topic areas to generate a preliminary set of recommendations that can be applied at organizational and system levels. Facilitators will use a live Google Jamboard to record ideas shared by the individual groups. Structure: 1) Introduction (15 minutes); 2) Hilary’s reflection (15 minutes); 3) Table Discussions (20 minutes); 4) Report back (10 minutes) Summarizing take home messages: In the final 10 minutes of the session the full group will reconvene to review the live Google Jamboards and recommendations. This exercise will help to generate a short report we can share with our networks, while allowing us to broker collaborations between delegates working on shared challenges.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.937
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.060
GPT teacher head0.317
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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