Lessons Learned From Provider Minder: A Provider Tracking Application for Improving Stroke Risk Screening in Sickle Cell Anemia
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: We developed a novel web-based application, Provider Minder, for providers to track and monitor stroke risk screening in children with sickle cell anemia. Here, we describe the development of the application, the process evaluation during implementation, and our lessons learned. METHODS: An iterative development process was used to develop the Provider Minder application and its functionalities. For our process evaluation, our team conducted surveys and interviews with study teams across 13 sites that used Provider Minder as part of a multi-intervention trial for the Dissemination and Implementation of Stroke Prevention Looking at the Care Environment study. Surveys and interviews were conducted with providers and coordinators at midpoint (1 year) and end point (2 years). Results were integrated and organized according to themes. RESULTS: The process evaluation indicated factors critical for implementation success, such as coordination across stakeholders. Successes of the intervention included high adaptability for unique site needs, ease of use, low costs of implementation, and perceived effectiveness at capturing missed screenings. Key challenges were the time burden for use, redundancy of data capture, and lack of integration, as Provider Minder was distinct from the electronic medical record. CONCLUSIONS: While providers and coordinators described multiple barriers to implementing Provider Minder, results indicated that perceived successes outweighed barriers. Future efforts to reduce the burden associated with health care complexity and improvement in interoperability of electronic medical records will be important for improving the success of similar tracking applications for complex conditions.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".