Goal-Oriented care: A catalyst for person-centred system integration
Bibliographic record
Abstract
Context: Person-centred integrated primary care delivery is often at odds with how current health care systems are structured, resulting in slower than expected uptake of the model. Adopting goal-oriented care, an approach which uses patient priorities, or goals, to drive what kinds of care are appropriate and how care is delivered, may offer a way to improve implementation. Objective: This case report presents three international cases of community-based primary health care models in Ottawa (Canada), Vermont (USA) and Flanders (Belgium) that adopted goal-oriented care to stimulate clinical, professional, organizational and system integration. The Rainbow Model of Integrated Care is used to demonstrate how goal-oriented care drove integration at all levels. Study design: Theoretical concept mapping using comparative case studies to illustrate theoretical connections. Setting: 3 interprofessional primary care practices delivering goal-oriented care in Ottawa (Canada), Flanders (Belgium), and Vermont (United States). Population studied: Interprofessional primary care practices. Intervention: Interprofessional teams delivering goal-oriented care. Outcome measures: Describe how goal-oriented care advances clinical, professional, organizational and system level integration. Results: The three cases demonstrate how goal-oriented care has the potential to catalyze integrated care. In Ontario, goal-oriented care enabled integration at clinical and professional levels, helping clinicians to work together across professional boundaries. In Vermont, the model enabled professional and organizational integration, helping professionals to work across organizational boundaries to support high needs patients. In Flanders, goal-oriented care is being adopted as part of a larger primary care system transformation. In all cases goal-oriented care served to activate formative and normative integration mechanisms; supporting processes that enable integrated care, while providing a framework for a shared philosophy of care. Conclusions: The framework presented in this work helped to demonstrate how goal-oriented care can be used to establish a common vision and philosophy to drive shared processes, acting as a powerful tool to enable integrated care delivery. This comparative case work provides lessons for other systems seeking to provide integrated care within and across primary care teams.
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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 | 0.024 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".