Time to listen: a review of methods to solicit patient reports of adverse events
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Patients have been shown to report accurate observations of medical errors and adverse events. Various methods of introducing patient reporting into patient safety systems have been published with little consensus among researchers on the most effective method. Terminology for use in patient safety reporting has yet to be standardised. METHODS: Two databases, PubMed and MEDLINE, were searched for literature on patient reporting of medical errors and adverse events. Comparisons were performed to identify the optimal method for eliciting patient initiated events. RESULTS: Seventeen journal publications were reviewed by patient population, type of healthcare setting, contact method, reporting method, duration, terminology and reported response rate. CONCLUSION: Few patient reporting studies have been published, and those identified in this review covered a wide range of methods in diverse settings. Definitive comparisons and conclusions are not possible. Patient reporting has been shown to be reliable. Higher incident rates were observed when open-ended questions were used and when respondents were asked about personal experiences in hospital and primary care. Future patient reporting systems will need a balance of closed-ended questions for cause analysis and classification, and open-ended narratives to allow for patient's limited understanding of terminology. Establishing the method of reporting that is most efficient in collecting reliable reports and standardising terminology for patient use should be the focus of future research.
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.
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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it