Graphics help patients distinguish between urgent and non-urgent deviations in laboratory test results
Bibliographic record
Abstract
OBJECTIVE: Most electronic health record systems provide laboratory test results to patients in table format. We tested whether presenting such results in visual displays (number lines) could improve understanding. MATERIALS AND METHODS: We presented 1620 adults recruited from a demographically diverse Internet panel with hypothetical results from several common laboratory tests, first showing near-normal results and then more extreme values. Participants viewed results in either table format (with a "standard range" provided) or one of 3 number line formats: a simple 2-color format, a format with diagnostic categories such as "borderline high" indicated by colored blocks, and a gradient format that used color gradients to smoothly represent increasing risk as values deviated from standard ranges. We measured respondents' subjective sense of urgency about each test result, their behavioral intentions, and their perceptions of the display format. RESULTS: Visual displays reduced respondents' perceived urgency and desire to contact health care providers immediately for near-normal test results compared to tables but did not affect their perceptions of extreme values. In regression analyses controlling for respondent health literacy, numeracy, and graphical literacy, gradient line displays resulted in the greatest sensitivity to changes in test results. DISCUSSION: Unlike tables, which only tell patients whether test results are normal or not, visual displays can increase the meaningfulness of test results by clearly defining possible values and leveraging color cues and evaluative labels. CONCLUSION: Patient-facing displays of laboratory test results should use visual displays rather than tables to increase people's sensitivity to variations in their results.
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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 | 0.001 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".