“What Else Could It Be?” A Scoping Review of Questions for Patients to Ask Throughout the Diagnostic Process
Bibliographic record
Abstract
BACKGROUND: Diagnostic errors are a global patient safety challenge. Over 75% of diagnostic errors in ambulatory care result from breakdowns in patient-clinician communication. Encouraging patients to speak up and ask questions has been recommended as one strategy to mitigate these failures. OBJECTIVES: The goal of the scoping review was to identify, summarize, and thematically map questions patients are recommended to ask during ambulatory encounters along the diagnostic process. This is the first step in a larger study to co-design a patient-facing question prompt list for patients to use throughout the diagnostic process. METHODS: Medline and Google Scholar were searched to identify question lists in the peer-reviewed literature. Organizational websites and a search engine were searched to identify question lists in the gray literature. Articles and resources were screened for eligibility and data were abstracted. Interrater reliability (K = 0.875) was achieved. RESULTS: A total of 5509 questions from 235 resources met inclusion criteria. Most questions ( n = 4243, 77.02%) were found in the gray literature. Question lists included an average of 23.44 questions. Questions were most commonly coded along the diagnostic process categories of treatment (2434 questions from 250 resources), communication of the diagnosis (1160 questions, 204 resources), and outcomes (766 questions, 172 resources). CONCLUSIONS: Despite recommendations for patients to ask questions, most question prompt lists focus on later stages of the diagnostic process such as communication of the diagnosis, treatment, and outcomes. Further research is needed to identify and prioritize diagnostic-related questions from the patient perspective and to develop simple, usable guidance on question-asking to improve patient safety across the diagnostic continuum.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.090 | 0.265 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.038 | 0.027 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".