Bridging the feedback gap: a sociotechnical approach to informing clinicians of patients’ subsequent clinical course and outcomes
Bibliographic record
Abstract
To improve their diagnosis and management skills, clinicians need consistent, timely and accurate feedback. Feedback helps clinicians become better calibrated, leading to more appropriate clinical decisions. Miscalibration—when clinicians’ confidence in the accuracy of their decisions does not align with their actual accuracy—may lead to overconfidence and diagnostic error.1 Consistent structured feedback leads to improved outcomes such as accurate diagnosis of acute chest pain,2 improved prehospital emergency care3 and lower costs of hospitalisation.4 Despite its benefits, significant gaps exist in delivering feedback to clinicians. In particular, clinicians do not consistently receive patient outcome feedback, that is, information on the subsequent clinical course and outcomes of patients that they have diagnosed and treated. For example, emergency department (ED) physicians in Canada reported receiving outcome feedback on only 15% of cases they encountered.5 Among US internal medicine residents, 58% reported almost never or only sometimes ultimately learning about their patients’ outcomes.6 Scientific knowledge on how to effectively provide clinicians with patient outcome feedback is underdeveloped. In contrast with traditional audit and feedback where clinicians receive aggregated metrics of clinical performance compared against explicit standards,7 patient outcome feedback provides clinicians with objective clinical information on their patient’s eventual diagnoses, treatment and clinical course. Rather than conferring black-and-white external judgements, patient outcome feedback provides clinicians with narratives regarding what happened to the patients they treated, which are crucial data for self-evaluation of clinical performance. Developing effective feedback pathways is difficult. Fragmented healthcare systems with organisational and regulatory barriers, such as seen in the USA, make feedback-related information flow challenging. Even integrated healthcare networks such as the US Veterans Affairs health system8 and those in other countries, like the UK, Australia and Canada, only have sporadic disease-specific feedback programmes.9 To our knowledge, none of these systems have …
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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 | 0.125 | 0.212 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.007 | 0.023 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".