Mobilizing registry data for quality improvement: A convergent mixed-methods analysis and application to spinal cord injury
Bibliographic record
Abstract
Introduction: The rising prevalence of complex chronic conditions and growing intricacies of healthcare systems emphasizes the need for interdisciplinary partnerships to advance coordination and quality of rehabilitation care. Registry databases are increasingly used for clinical monitoring and quality improvement (QI) of health system change. Currently, it is unclear how interdisciplinary partnerships can best mobilize registry data to support QI across care settings for complex chronic conditions. Purpose: We employed spinal cord injury (SCI) as a case study of a highly disruptive and debilitating complex chronic condition, with existing registry data that is underutilized for QI. We aimed to compare and converge evidence from previous reports and multi-disciplinary experts in order to outline the major elements of a strategy to effectively mobilize registry data for QI of care for complex chronic conditions. Methods: This study used a convergent parallel-database variant mixed design, whereby findings from a systematic review and a qualitative exploration were analyzed independently and then simultaneously. The scoping review used a three-stage process to review 282 records, which resulted in 28 articles reviewed for analysis. Concurrent interviews were conducted with multidisciplinary-stakeholders, including leadership from condition-specific national registries, members of national SCI communities, leadership from SCI community organizations, and a person with lived experience of SCI. Descriptive analysis was used for the scoping review and qualitative description for stakeholder interviews. Results: There were 28 articles included in the scoping review and 11 multidisciplinary-stakeholders in the semi-structured interviews. The integration of the results allowed the identification of three key learnings to enhance the successful design and use of registry data to inform the planning and development of a QI initiative: enhance utility and reliability of registry data; form a steering committee lead by clinical champions; and design effective, feasible, and sustainable QI initiatives. Conclusion: This study highlights the importance of interdisciplinary partnerships to support QI of care for persons with complex conditions. It provides practical strategies to determine mutual priorities that promote implementation and sustained use of registry data to inform QI. Learnings from this work could enhance interdisciplinary collaboration to support QI of care for rehabilitation for persons with complex chronic conditions.
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 | 0.227 | 0.272 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".