Identification of serious and reportable events in home care: a Delphi survey to develop consensus
Bibliographic record
Abstract
OBJECTIVE: To assess which client events should be considered reportable and preventable in home care (HC) settings in the opinion of HC safety experts. BACKGROUND: Patient safety in acute care settings has been well documented; however, there are limited data about this issue in HC. While many organizations collect information about 'incidents', there are no standards for reporting and it is challenging to compare incident rates among organizations. DESIGN: A 29-item electronic survey that included potential HC safety issues was used in a two-round Delphi study. SETTING AND PARTICIPANTS: Twenty-four pan-Canadian HC safety experts participated in an electronic survey. MAIN OUTCOME MEASURES: Perceived reportability and preventability of patient safety events, HC. RESULTS: The events that were perceived as being most reportable and preventable included the following: a serious injury related to inappropriate client service plan (e.g. incomplete/inaccurate assessments, poor care plan design, flawed implementation); an adverse reaction requiring emergency room visit or hospitalization related to a medication-related event; a catheter-site infection (e.g. a new peritoneal dialysis infection or peritonitis); any serious event related to care or services that are contrary to current professional or other practice standards (e.g. incorrect treatment regimen, theft, retention of a foreign object in a wound, individual practicing outside scope or competence). CONCLUSION: These data represent an important step in the development and validation of standard metrics about client safety in HC. The results address an expanding area of health services where there is a need to improve standardization and reporting.
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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 | 0.114 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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 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".