Communicating laboratory results to patients and families
Bibliographic record
Abstract
People are increasingly able to access their laboratory results via patient portals. The potential benefits provided by such access, such as reductions in patient burden and improvements in patient satisfaction, disease management, and medical decision making, also come with potentially valid concerns about such results causing confusion or anxiety among patients. However, it is possible to clearly convey the meaning of results and, when needed, indicate required action by designing systems to present laboratory results adapted to the people who will use them. Systems should support people in converting the potentially meaningless data of results into meaningful information and actionable knowledge. We offer 10 recommendations toward this goal: (1) whenever possible, provide a clear takeaway message for each result. (2) Signal whether differences are meaningful or not. (3) When feasible, provide thresholds for concern and action. (4) Individualize the frame of reference by allowing custom reference ranges. (5) Ensure the system is accessible. (6) Provide conversion tools along with results. (7) Design in collaboration with users. (8) Design for both new and experienced users. (9) Make it easy for people use the data as they wish. (10) Collaborate with experts from relevant fields. Using these 10 methods and strategies renders access to laboratory results into meaningful and actionable communication. In this way, laboratories and medical systems can support patients and families in understanding and using their laboratory results to manage their health.
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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 | 0.028 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.013 |
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".