Using Z Codes to Document Social Risk Factors in the Electronic Health Record
Bibliographic record
Abstract
INTRODUCTION: Individual-level social risk factors have a significant impact on health. Social risks can be documented in the electronic health record using ICD-10 diagnosis codes (the "Z codes"). This study aims to summarize the literature on using Z codes to document social risks. METHODS: A scoping review was conducted using the PubMed, Medline, CINAHL, and Web of Science databases for papers published before June 2024. Studies were included if they were published in English in peer-reviewed journals and reported a Z code utilization rate with data from the United States. RESULTS: Thirty-two articles were included in the review. In studies based on patient-level data, patient counts ranged from 558 patients to 204 million, and the Z code utilization rate ranged from 0.4% to 17.6%, with a median of 1.2%. In studies that examined encounter-level data, sample sizes ranged from 19,000 to 2.1 billion encounters, and the Z code utilization rate ranged from 0.1% to 3.7%, with a median of 1.4%. The most reported Z codes were Z59 (housing and economic circumstances), Z63 (primary support group), and Z62 (upbringing). Patients with Z codes were more likely to be younger, male, non-White, seeking care in an urban teaching facility, and have higher health care costs and utilizations. DISCUSSION: The use of Z codes to document social risks is low. However, the research interest in Z codes is growing, and a better understanding of Z code use is beneficial for developing strategies to increase social risk documentation, with the goal of improving health outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".