Implementing Large-Scale Data-Driven Quality Improvement in Assisted Living
Bibliographic record
Abstract
OBJECTIVES: Develop and evaluate the implementation of a proposed model for large-scale data-driven quality improvement in assisted living. DESIGN: We conducted a mixed-methods evaluation of the implementation of a large-scale data-driven quality improvement collaborative of Wisconsin assisted living communities (ALCs). SETTING AND PARTICIPANTS: The model has been voluntarily implemented by 810 Wisconsin-licensed ALCs serving >20,000 residents. METHODS: The model was codesigned iteratively 2009-2012 by a public-private multistakeholder advisory group. Using system usage statistics and project records, we evaluated implementation outcomes: appropriateness, acceptability, adoption, feasibility, fidelity, penetration, and sustainability. RESULTS: Implementation for ≥1 quarter was feasible for 92% of the 810 ALCs that enrolled. The model has been deemed appropriate and acceptable by public-private stakeholders representing residents, providers, regulators, and payers, and appropriateness for ALCs serving different populations has been iteratively improved through targeted workgroups. The model is currently adopted in Wisconsin by 31% of the 1573 ALCs in provider associations. Among adopters, 88% on average implemented the model with fidelity to key membership rules per quarter. The model achieved demographic and institutional penetration by currently reaching 24% of Wisconsin ALC residents and by leveraging initial grant funding to become integrated in Wisconsin's annual Medicaid budget and being central to Wisconsin's incentive program to managed care organizations. Model implementation for 8 years has been sustained by member enrollment for nearly 4 years on average, with 71% of members enrolled >2 years and sustained early adopters representing 37% that have been enrolled >5 years. CONCLUSIONS AND IMPLICATIONS: This is the first implementation study of large-scale data-driven quality improvement in assisted living, despite its demonstrated value in other health care sectors. The article proposes a model with core components and implementation strategies drawing on a decade-long public-private collaboration. The implementation study findings establish a promising path and future directions for wider implementation.
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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 | 0.093 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".