Care Everywhere: Implementing and Evaluating a Network-level Digital Transformation to Improve Coordination and Access across a Health System
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".