Barriers to data quality resulting from the process of coding health information to administrative data: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Administrative health data are increasingly used for research and surveillance to inform decision-making because of its large sample sizes, geographic coverage, comprehensivity, and possibility for longitudinal follow-up. Within Canadian provinces, individuals are assigned unique personal health numbers that allow for linkage of administrative health records in that jurisdiction. It is therefore necessary to ensure that these data are of high quality, and that chart information is accurately coded to meet this end. Our objective is to explore the potential barriers that exist for high quality data coding through qualitative inquiry into the roles and responsibilities of medical chart coders. METHODS: We conducted semi-structured interviews with 28 medical chart coders from Alberta, Canada. We used thematic analysis and open-coded each transcript to understand the process of administrative health data generation and identify barriers to its quality. RESULTS: The process of generating administrative health data is highly complex and involves a diverse workforce. As such, there are multiple points in this process that introduce challenges for high quality data. For coders, the main barriers to data quality occurred around chart documentation, variability in the interpretation of chart information, and high quota expectations. CONCLUSIONS: This study illustrates the complex nature of barriers to high quality coding, in the context of administrative data generation. The findings from this study may be of use to data users, researchers, and decision-makers who wish to better understand the limitations of their data or pursue interventions to improve data quality.
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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 | 0.107 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".