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Record W4409337335 · doi:10.5334/ijic.9488

Care Everywhere: Implementing and Evaluating a Network-level Digital Transformation to Improve Coordination and Access across a Health System

2025· article· en· W4409337335 on OpenAlexaboutno aff
Aurelia Di Fabrizio, Erin Cook, Amath Thiam

Bibliographic record

VenueInternational Journal of Integrated Care · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsnot available
Fundersnot available
KeywordsIntegrated careHealth careDigital healthProcess managementHealthcare systemDigital transformationBusinessKnowledge managementComputer scienceWorld Wide WebEconomic growthEconomics

Abstract

fetched live from OpenAlex

Context: In the context of reduced capacity through a severely burnt-out workforce, and struggling to provide appropriate care to populations, health systems are turning to innovative approaches to health services. The West-Central Montreal Integrated Health and Social Services University Network (CCOMTL) has adopted an innovation-based philosophy, centered on transformation via digital health solutions with tangible impacts for the population served by the network. A foundational pillar of this transformation is the establishment of a network-level Command Centre (“C4”): the digital “heart” of the system that leverages local and provincial data to generate real-time insights, including predictive algorithms that allow team members to anticipate system capacity. Of note, the C4 extends beyond bed flow management, instead providing an overview of patients across their entire care trajectory. This includes helping people access the right resources to avoid unnecessary hospitalizations, e.g., facilitating primary care and mental health care connections, and making sure patients who are hospitalized can be appropriately directed to the right place. All sectors of the CCOMTL have been engaged as partners in shaping the C4, whether determining data needs for their respective domains, or co-creating roles in response to shared learnings generated through collaboration. Ultimately, the C4 is a digital intervention aimed at re-imagining system-wide coordination. To date, the C4 has resulted in improvements in indicators related to access and flow, such as reduced average length of stay by 0.5 days for hospitalized inpatients and 1.4 days for surgical patients; as well as a reduction of over 50% in the number of people awaiting community mental health services. While some of these indicators arguably represent hospital-centric markers of efficiency, they are accompanied by improved care coordination processes that make sure people don’t fall through the cracks of the system once they leave the hospital. Challenge: As sectors learn to collaborate, and even share physical infrastructure, cultural siloes must be addressed and broken down to ensure the C4's sustainment beyond initial implementation. Furthermore, as C4 itself represents a highly complex intervention, stakeholders must learn which activities help or hinder progress. To address these challenges, we are conducting a two-year developmental evaluation, aimed at providing C4 stakeholders tools and information on the processes, activities, and outputs that support its long-term implementation and sustainability. While C4’s objectives of improving patient trajectories of care and coordination are clear, we also highlight the critical objective of improving the experience of clinicians themselves, who are navigating through incredible duress and often insufficient support. Developmental Evaluation Process and Impacts: The evaluation is presently ongoing, though interim findings are being collected throughout the evaluation period. We will present the impacts of the evaluation across three dimensions: 1)Describing quality improvement activities (e.g., workshops and facilitated training) that have been developed in response to engagement with and evaluation feedback from C4 stakeholders. 2)Quantitative and qualitative insights related to team- and culture-based dimensions known to impact implementation of complex interventions. 3)Process and outcome measures established to date, including a logic model summarizing and expanding upon the above points.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.067
metaresearch head score (Gemma)0.086
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.355

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0670.086
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.002
Science and technology studies0.0030.007
Scholarly communication0.0080.011
Open science0.0050.008
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0070.001

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 head0.027
GPT teacher head0.345
Teacher spread0.318 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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