Putting out fires: a qualitative study exploring the use of patient complaints to drive improvement at three academic hospitals
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Recent years have seen increasing calls for more proactive use of patient complaints to develop effective system-wide changes, analogous to the intended functions of incident reporting and root cause analysis (RCA) to improve patient safety. Given recent questions regarding the impact of RCAs on patient safety, we sought to explore the degree to which current patient complaints processes generate solutions to recurring quality problems. DESIGN/SETTING: Qualitative analysis of semistructured interviews with 21 patient relations personnel (PRP), nursing and physician leaders at three teaching hospitals (Toronto, Canada). RESULTS: Challenges to using the patient complaints process to drive hospital-wide improvement included: (1) Complaints often reflect recalcitrant system-wide issues (eg, wait times) or well-known problems which require intensive efforts to address (eg, poor communication). (2) The use of weak change strategies (eg, one-off educational sessions). (3) The handling of complaints by unit managers so they never reach the patient relations office. PRP identified giving patients a voice as their primary goal. Yet their daily work, which they described as 'putting out fires', focused primarily on placating patients in order to resolve complaints as quickly as possible, which may in effect suppress the patient voice. CONCLUSIONS: Using patient complaints to drive improvement faces many of the challenges affecting incident reporting and RCA. The emphasis on 'putting out fires' may further detract from efforts to improve care for future patients. Systemically incorporating patients' voices in clinical operations, as with co-design and other forms of authentic patient engagement, may hold greater promise for meaningful improvements in the patient experience than do RCA-like analyses of patient complaints.
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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 | 0.027 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".