An intraorganizational model for developing and spreading quality improvement innovations
Bibliographic record
Abstract
BACKGROUND: Recent policy reforms encourage quality improvement (QI) innovations in primary care, but practitioners lack clear guidance regarding spread inside organizations. PURPOSE: We designed this study to identify how large organizations can facilitate intraorganizational spread of QI innovations. METHODOLOGY/APPROACH: We conducted ethnographic observation and interviews in a large, multispecialty, community-based medical group that implemented three QI innovations across 10 primary care sites using a new method for intraorganizational process development and spread. We compared quantitative outcomes achieved through the group's traditional versus new method, created a process model describing the steps in the new method, and identified barriers and facilitators at each step. FINDINGS: The medical group achieved substantial improvement using its new method of intraorganizational process development and spread of QI innovations: standard work for rooming and depression screening, vaccine error rates and order compliance, and Pap smear error rates. Our model details nine critical steps for successful intraorganizational process development (set priorities, assess the current state, develop the new process, and measure and refine) and spread (develop support, disseminate information, facilitate peer-to-peer training, reinforce, and learn and adapt). Our results highlight the importance of utilizing preexisting organizational structures such as established communication channels, standardized roles, common workflows, formal authority, and performance measurement and feedback systems when developing and spreading QI processes inside an organization. In particular, we detail how formal process advocate positions in each site for each role can facilitate the spread of new processes. PRACTICE IMPLICATIONS: Successful intraorganizational spread is possible and sustainable. Developing and spreading new QI processes across sites inside an organization requires creating a shared understanding of the necessary process steps, considering the barriers that may arise at each step, and leveraging preexisting organizational structures to facilitate intraorganizational process development and spread.
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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 | 0.022 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".