Using a data entry clerk to improve data quality in primary careelectronic medical records: a pilot study
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: The quality of electronic medical record (EMR) data is known to be problematic; research on improving these data is needed. OBJECTIVE: The primary objective was to explore the impact of using a data entry clerk to improve data quality in primary care EMRs. The secondary objective was to evaluate the feasibility of implementing this intervention. METHODS: We used a before and after design for this pilot study. The participants were 13 community based family physicians and four allied health professionals in Toronto, Canada. Using queries programmed by a data manager, a data clerk was tasked with re-entering EMR information as coded or structured data for chronic obstructive pulmonary disease (COPD), smoking, specialist designations and interprofessional encounter headers. We measured data quality before and three to six months after the intervention. We evaluated feasibility by measuring acceptability to clinicians and workload for the clerk. RESULTS: After the intervention, coded COPD entries increased by 38% (P = 0.0001, 95% CI 23 to 51%); identifiable data on smoking categories increased by 27% (P = 0.0001, 95% CI 26 to 29%); referrals with specialist designations increased by 20% (P = 0.0001, 95% CI 16 to 22%); and identifiable interprofessional headers increased by 10% (P = 0.45, 95 CI -3 to 23%). Overall, the intervention was rated as being at least moderately useful and moderately usable. The data entry clerk spent 127 hours restructuring data for 11 729 patients. CONCLUSIONS: Utilising a data manager for queries and a data clerk to re-enter data led to improvements in EMR data quality. Clinicians found this approach to be acceptable.
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.
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 | 0.077 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| 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