ICD coding training worldwide
Bibliographic record
Abstract
IntroductionThe International Classification of Diseases (ICD) is globally used for coding morbidity statistics, however, its use, as well as the training provided to individuals assigning codes, varies greatly across countries. Objectives and ApproachThe goal is to understand the quality of coder training worldwide. After an in-depth grey and academic literature review, an online survey was created to poll the 194 World Health Organization (WHO) member countries. Questions focused on hospital data collection systems and the training provided to the coding professionals. The survey was distributed to potential participants that meet the specific criteria, as well as to organizations specialized in the topic, such as WHO-CC (WHO Collaborating Centers) and IFHIMA (International Federation of Health Information Management Association), to be forwarded to their representatives. Answers will be analyzed using descriptive statistics. ResultsThis ongoing project aims to capture responses from as many countries as possible, and thus far, data from 45 respondents from 20 different countries has been collected. Initial results reveal worldwide use of ICD, with variations in the maximum allowable coding fields for diagnoses and interventions. Coding specialists are the main personnel assigning codes, followed by physicians, and although minimum training is not mandatory in all countries (Sweden, Italy, Germany and Thailand), in those where it is, college/university degree is the most common requirement. Coding certificates most frequently entail passing a certification exam. Continuing education for coders is offered in all countries except one (Nigeria). Once more information is available, countries will be ranked and those depicting a better performance will be highlighted. Conclusion/ImplicationsThese survey data will establish the current state of ICD use and coding training internationally, which will ultimately be valuable to the WHO for the promotion of ICD and the rollout of ICD-11, for better international comparisons of health data, and for further research on how to improve ICD coding.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.129 | 0.039 |
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".