Practical strategies for achieving system change in the US: lessons and insights from the CONQUEST quality improvement programme
Bibliographic record
Abstract
BACKGROUND: Quality improvement programmes (QIPs) are designed to enhance patient outcomes by systematically introducing evidence-based clinical practices. The CONQUEST QIP focuses on improving the identification and management of patients with COPD in primary care. The process of developing CONQUEST, recruiting, preparing systems for participation, and implementing the QIP across three integrated healthcare systems (IHSs) is examined to identify and share lessons learned. APPROACH AND DEVELOPMENT: This review is organized into three stages: 1) development, 2) preparing IHSs for implementation, and 3) implementation. In each stage, key steps are described with the lessons learned and how they can inform others interested in developing QIPs designed to improve the care of patients with chronic conditions in primary care.Stage 1 was establishing and working with steering committees to develop the QIP Quality Standards, define the target patient population, assess current management practices, and create a global operational protocol. Additionally, potential IHSs were assessed for feasibility of QIP integration into primary care practices. Factors assessed included a review of technological infrastructure, QI experience, and capacity for effective implementation.Stage 2 was preparation for implementation. Key was enlisting clinical champions to advocate for the QIP, secure participation in primary care, and establish effective communication channels. Preparation for implementation required obtaining IHS approvals, ensuring Health Insurance Portability and Accountability Act compliance, and devising operational strategies for patient outreach and clinical decision support delivery.Stage 3 was developing three IHS implementation models. With insight into the local context from local clinicians, implementation models were adapted to work with the resources and capacity of the IHSs while ensuring the delivery of essential elements of the programme. CONCLUSION: Developing and launching a QIP programme across primary care practices requires extensive groundwork, preparation, and committed local champions to assist in building an adaptable environment that encourages open communication and is receptive to feedback.
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 | 0.058 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".