Facilitating delivery of goal-oriented care through the collection, presentation, and use of meaningful data
Bibliographic record
Abstract
Background: Use of routinely collected health and social care data has potential to drive population health improvements. Many organizations are data rich but information poor; collecting large amounts of data but having limited ability to transform data into actionable insights. Learning health systems aim to produce these types of insights not through stand-alone research, but as by-products of care delivery, using the care environment as a living lab to generate and apply new knowledge to improve both care delivery and population health. To effectively leverage data to support evidence-informed decision making, point-of-care clinicians and organizational leaders require access to data that is both reliable and meaningful, in a format that supports its real-time use. Audience: SE Health is a large Canadian not-for-profit social enterprise delivering care across the continuum. Embedded within SE Health is the SE Research Centre, a consortium of applied and impact-oriented health services researchers, who together with experts-by-experience work to develop, test, implement, evaluate, scale, and spread evidence to support transformative health system change. We invite anyone interested in this type of learning health system environment providers, decision-makers, researchers, patients, and families to come together in this workshop to learn about how we can use data to facilitate the delivery of goal-oriented care. Approach: Grounded in case-based learning methodology and using an example of facilitating goal-oriented care delivery, we will share learnings from our journey towards becoming a community-based learning health system. Leveraging a mix of presentation, applied activities and small group discussions, attendees will be guided through the three steps of the learning health system cycle - data-to-knowledge, knowledge-to-practice, and practice-to-data - using resources and tools created by the SE Research Centre to facilitate the delivery of goal-oriented, integrated care. First, a brief (~0 min) introductory presentation will provide important background on goal-oriented care and the development, testing and implementation of data collection instruments (e.g., Client Experience Survey for Integrated-Home and Community Care) and other resources (e.g., Holistic Health Needs Report) featured in the session. Following this, an evidence-informed client case study will be presented (~0 min), serving as the foundation for hands-on activities throughout the workshop. Two small group activities will be facilitated, each with 20 minutes for engagement in the activity and 0 minutes for sharing small group insights with all workshop attendees. Activity focuses on data use at the micro- or practice level, with participants leveraging the Holistic Health Needs Report to engage in goal-oriented care planning. In Activity 2, participants will use data at the meso- or organizational level to plan data-informed workforce development initiatives based on unit-level reports of patient experience and population health needs. A final presentation (~0m) will focus on moving knowledge into practice, summarizing results of a recent scoping review of best practices in case-based learning and providing examples of how person-level data can support both micro and meso-level education and training initiatives. Outcomes: After attending this workshop, participants will be able to ) describe key considerations for the collection, presentation, and use of data at the micro- and meso- level, 2) explain how to apply summaries of point-of-care data to support holistic, goal-oriented collaborative care planning through case-based learning and 3) identify how data from point-of care activities can generate meaningful insights to support operational management and workforce development. Take home messages will be summarized by workshop facilitators using the three steps of the learning health system cycle as a framework. Participants will have an opportunity to take away research summaries, workshop handouts and copies of the materials presented for further sharing and reflection in their own practice contexts and organizations.
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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 | 0.128 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".