Developing and Measuring the Reliability and Validity of the Factors Influencing the Implementation of ICD-10-AM and Clinical Coding in Saudi Public Hospitals
Bibliographic record
Abstract
BACKGROUND: The introduction of a mandatory health insurance system contributing towards the funding of national healthcare in Saudi Arabia necessitates the implementation of clinical coding and a unified health classification system, which has previously not been a feature of Saudi healthcare. As the Ministry of Health (MOH) moves to introduce ICD-10-AM, the Australian modification of the WHO ICD-10, in the Kingdom’s public hospitals, it is important to understand the factors that will influence its successful implementation. OBJECTIVE: The purpose of this article is to develop and evaluate the internal consistency reliability and validity of a questionnaire establishing the factors influencing the the implementation ICD-10-AM and clinical coding in Saudi public hospitals. METHOD: The content validity method was initiated by sending the whole draft questionnaire to a panel of experts to indicate values for each item based on a scale of content validity created by the researchers and, subsequently, using the internal consistency reliability and factorial validity methods to estimate the internal reliability of clusters of items, which were assumed to measure the same factors, grouped in this study into three factorial categories, health information (clinical documentation, classification, and coding requirements), organization (the implementation preparation in individual organizations), and national (institutional support through the national hierarchical structure). RESULTS: The content validity identified all items of the proposed questionnaire to be valid. Based on the content validity test, several items were removed as they did not meet the proposed model and the final questionnaire was created in accord with the pilot study result. The pilot study utilized Cronbach's α and factor analysis to examine the reliability and validity of Part 2 of the questionnaire and the findings indicated high internal consistency reliability and factorial validity.
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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 | 0.068 | 0.144 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".