Development and Implementation of a System Accountability Framework for Nova Scotia
Bibliographic record
Abstract
Population growth and comorbidity are driving demand for episodic care and extended community supports creating backlogs in access, particularly for emergency care in Nova Scotia. In an effort to optimize access to care, Nova Scotia Health implemented a cross-sectoral framework to help move patients through their acute care journey. The Framework was led by the Integrated Acute and Episodic Clinical Services Network. IImplementation success requires close partnerships within Nova Scotia Health (e.g. Access and Flow Network, Primary Health Care Network, Perioperative Network, Performance and Analytics, Research and Innovation, Continuing Care) as well as external partners (e.g. Emergency Health Services, Department of Health and Wellness, Department of Seniors and Long Term Care, Department of Community Services). Within each zone of accountability, Network and operational teams collaborated to develop and implement shared metrics and local models and policies that can be mapped to each metric. Ambulance offload time was selected as a state variable that reflects overall flow in the acute care system with a Phase goal of reducing 90th percentile ambulance offload time by 50% (0% per month over 5 months. Tools included models of care, leaning processes, integrating Action for Health Initiatives, policies and reporting templates. Weekly communications and check-in meetings including operational leaders across health sectors provide an opportunity to report on progress and share early successes and opportunities for spread and scale. A dashboard (in development) provides a line of site to weekly performance. The Framework has been integrated as a key support for a larger provincial initiative called Operational Excellence which provides structured reporting and operational grip. Currently in week 4, implementation has already had impact including: This week, there were ,46 inpatient admissions and ,069 discharges. Out of ,372 ambulance offloads in week 4, only 65 took longer than the designated time cut off at the site, a drop from 3 in 2023. 30.6% of ambulance offloads were completed within 30 minutes (compared with 27.8% last year), and 90% were completed within 46.3 minutes (compared with 237 minutes last year). Over 228 ambulance hours were returned to the communities. EHS has reported a decrease in their ambulance response time over the past six months. Current response times for emergency calls are averaging 20 minutes. Efforts continue to address surge capacity, with a focus on implementing strategies and processes to improve provider response times and patient stays. This involves streamlining discharge processes for smoother patient flow.The challenges that the Framework aims to address are common to many health jurisdictions. This initiative demonstrates a real-life implementation of the recommendations supported by the national CAEP EM-POWER report and a view to how providing a system-wide framework can help teams at all levels of care develop and implement improvements that are based on a common shared goal: each patient receives the right care in the right setting, from the right provider. The poster will share challenges and opportunities related to the development and implementation of the Framework.The Framework may inform modification of existing Strategic Deployment Review (SDR) meetings to align more closely with System Accountability. The Framework, and the supporting network of metrics, will help to inform further improvements including frontline care and processes and broader health service planning.
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 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 | 0.030 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".