Feasibility of prospective error reporting in home palliative care: A mixed methods study
Bibliographic record
Abstract
BACKGROUND: Prospectively tracking errors can improve patient safety but little is known about how to successfully implement error reporting in a home-based palliative care context. AIM: Explore the feasibility of implementing an error reporting system in a home-based palliative care program in Toronto, Canada, and describe the possible factors that may influence uptake. DESIGN: A convergent mixed-methods approach was used. Participants prospectively documented errors using a novel reporting tool and completed monthly surveys. Following the reporting period, we conducted a semi-structured interview exploring participants' experiences and perceived factors influencing reporting behaviors. Error, survey, and interview data were analyzed separately, then integrated for comparison. SETTING AND PARTICIPANTS: Thirteen palliative care physicians from a single home-based palliative care organization in Toronto, Canada anonymously reported errors between October 2021 and September 2022. Of these, six participated in the exit interview. RESULTS: = 65) involved internal staff or systems. Three themes describe the factors impacting the likelihood of reporting errors: (1) High levels of cognitive burden decreases the likelihood of error reporting; (2) Framing errors as opportunities to learn rather than reason for punishment improves likelihood of error reporting; (3) Knowing that error data will improve patient safety motivates individuals to report errors. CONCLUSIONS: Physicians are amenable to error reporting activities so long as data is used to improve patient safety. The collaborative nature of care in a home-based palliative care context may present unique challenges to translating error reporting to improved patient safety.
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How this classification was reachedexpand
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".