Key characteristics and critical junctures for successful Interprofessional networks in healthcare – a case study
Bibliographic record
Abstract
BACKGROUND: The use of networks in healthcare has been steadily increasing over the past decade. Healthcare networks reduce fragmented care, support coordination amongst providers and patients, improve health system efficiencies, support better patient care and improve overall satisfaction of both patients and healthcare professionals. There has been little research to date on the implementation, development and use of small localized networks. This paper describes lessons learned from a successful small localized primary care network in Southwestern Ontario that developed and implemented a regional respiratory care program (The ARGI Respiratory Health Program - ARGI is a not-for-profit corporation leading the implementation and evaluation of a respiratory health program. Respiratory therapists (who have a certified respiratory educators designation), care for patients from all seven of the network's FHTs. Patients rostered within the network of FHTs that have been diagnosed with a chronic respiratory disease are referred by their family physicians to the program. The RTs are integrated into the FHTs, and work in a triad along with patients and providers to educate and empower patients in self-management techniques, create exacerbation action plans, and act as a liaison between the patient's care providers. ARGI uses an eTool designed specifically for use by the network to assist care delivery, choosing education topics, and outcome tracking. RTs are hired by ARGI and are contracted to the participating FHTs in the network.). METHODS: This study used an exploratory case study approach. Data from four participant groups was collected using focus groups, observations, interviews and document analysis to develop a rich understanding of the multiple perspectives associated with the network. RESULTS: This network's success can be described by four characteristics (growth mindset and quality improvement focus; clear team roles that are strengths-based; shared leadership, shared success; and transparent communication); and five critical junctures (acknowledge a shared need; create a common vision that is flexible and adaptable depending on the context; facilitate empowerment; receive external validation; and demonstrate the impacts and success of their work). CONCLUSIONS: Networks are used in healthcare to act as integrative, interdisciplinary tools to connect individuals with the aim of improving processes and outcomes. We have identified four general lessons to be learned from a successful small and localized network: importance of clear, flexible, and strengths-based roles; need for shared goals and vision; value of team support and empowerment; and commitment to feedback and evaluations. Insight from this study can be used to support the development and successful implementation of other similar locally developed networks.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".