Provenance-based Trust Model for Assessing Data Quality during Clinical Decision Making
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
Data quality is a critical requirement for data-driven clinical decision making in modern healthcare. It is a key prerequisite to many clinical analytics applications, yet much of the research to date focuses on assessing data quality of electronic health records for secondary use. This paper proposes a trust model and provenance-based assessment method for considering data quality during clinical decision making at the point of care. The method uses fuzzy logic to infer data quality trust from a data user's trust preferences with respect to data producers, data production methods, verification of data items, and certification of data production methods. Implementation with an existing “SMART on FHIR” app in primary care demonstrates the feasibility of model and method. An extension to FHIR resources for data quality trust allows for platform interoperability across system contexts. We consider dual process theories in designing a user interface that supports data quality trust for clinical decisions in heuristic and systematic cognitive processing modes. Model and method are adaptable to other application domains that rely on data quality for decision making.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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