An Exploratory Case Study to Understand Primary Care Users and Their Data Quality Tradeoffs
Bibliographic record
Abstract
Primary care data is an important part of the evolving healthcare ecosystem. Generally, users in primary care are expected to provide excellent patient care and record high-quality data. In practice, users must balance sets of priorities regarding care and data. The goal of this study was to understand data quality tradeoffs between timeliness, validity, completeness, and use among primary care users. As a case study, data quality measures and metrics are developed through a focus group session with managers. After calculating and extracting measurements of data quality from six years of historic data, each measure was modeled with logit binomial regression to show correlations, characterize tradeoffs, and investigate data quality interactions. Measures and correlations for completeness, use, and timeliness were calculated for 196,967 patient encounters. Based on the analysis, there was a positive relationship between validity and completeness, and a negative relationship between timeliness and use. Use of data and reductions in entry delay were positively associated with completeness and validity. Our results suggest that if users are not provided with sufficient time to record data as part of their regular workflow, they will prioritize spending available time with patients. As a measurement of a primary care system's effectiveness, the negative correlation between use and timeliness points to a self-reinforcing relationship that provides users with little external value. In the future, additional data can be generated from comparable organizations to test several new hypotheses about primary care users.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".