Bibliographic record
Abstract
The adoption and use of Electronic Medical Records (EMRs) and Electronic Health Records (EHRs) is continuing to rise in North America. These systems contain data of varying degrees of quality, including poor quality or "dirty" data. Data entered into EMRs need to be clean or of high quality for them to be useful for a variety of reasons, including quality improvement, clinical decision support, population management, research and system management. There are two potential solutions to obtaining clean data from EMRs: data discipline and data cleansing. Data discipline focuses on ensuring that entry of data into EMRs is of high quality, while data cleansing focuses on cleaning data in the database. Clean data are necessary for healthcare providers to effectively manage chronic diseases and should lead to a reduction in the costs associated with those diseases. The objective of this paper is to compare the costs involved in implementing the two different data cleaning approaches by performing a Budget Impact Analysis (BIA) using diabetes as the exemplar in Canada. The BIA revealed that the cost to implement data discipline is $65 million whereas the cost to implement the data cleansing approach would be $21 million. Even though the cost may seem high, the cost of dirty data is even higher. Data discipline, data cleansing, or a combination of both approaches should be considered going forward.
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.
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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".