Using Social Networks for Tele-consultation on the Covid-19 Clinical Coding: A Quality Improvement Approach for Health Policy Making
Bibliographic record
Abstract
Objective: The accuracy of clinical coding in COVID-19 is essential for quality of care, disease surveillance, as well as research and reporting. This study aims to describe and categorize consultations between medical coders based on the social media network in Iran on the COVID-19 coding. Method : A clinical coding group in the social network at the national level in Iran was established to follow consultation regarding COVID-19 among coders. We also utilized an online survey, which was designed to extract the problems coders encountered during clinical coding and their opinion on whether these consultations were effective. Herein, we report messages and communication records exchanged among members of this network obtained between 21 February 2020 and 20 November 2020. Finally, we categorised the obtained information and identified the problems for COVID-19 accurate coding in Iran. Results : A total of 1,340 messages in 332 consultations were exchanged amongst 76 coders. We categorised topics of consultations into 11 categories. Most consultations dealt with “suspected or probable” (n = 71), “clinical coding and diagnosis” (n=59) within 332 conversations. In 47% of consultations, the first reply was less than 10 ± 3 min. “Maternal and infant ” and “procedures and drugs ” were the most common subjects with specific answer. Based on the viewpoints of coders, online consultations can reduce the time of clinical coding and increase coding accuracy. Conclusion: The establishment of social networks among medical record coders is an efficient strategy to deal with coding issues during the COVID-19 pandemic and improve the quality of hospital records.
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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 | 0.054 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".