Lessons Learned From Provider Minder: A Provider Tracking Application for Improving Stroke Risk Screening in Sickle Cell Anemia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it