Feedback System in Healthcare: The Why, What and How
Bibliographic record
Abstract
Background: A lively, dynamic and interactive feedback system that can connect all the stakeholders and engage them in a sustainable loop of seamless information flow is quintessential for any organization with significant public interface. It is important to balance the asymmetry in the patient doctor relationship and empower the patients. Methodology: In this regard, process mapping of existing system was done. Ethnographic methods were used with triangulation of data for validity. Three key bottlenecks were identified; the system to collect feedback, the feedback form itself and the overall mindset about the feedback system. A de novo, systematic approach akin to the PDSA (Plan-Do-Study-Act) steps in quality improvement was adopted to create a new feedback system and its continuous incremental improvements thereon. Results: Short, attractive form with interactive emoticons graded over a likert scale was created and made accessible through standalone kiosks across the institute. Key attention to ease of access, minimizing the number of clicks, removing human interface and providing SMS/E-mail acknowledgement to feedbacks have all contributed to sustainability of the new system with consistently high turn-out/participation. Conclusion: Designing a customized, hospital specific feedback system rooted more in the experiences on ground is more sustainable and reliable rather than standardized surveys. The same has been reiterated in literature. They can also be used to assess the impact of new interventions. Robust feedback systems can be envisioned as part of accreditation and continuous quality improvement.
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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 | 0.048 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".