Self-Reported Medical Errors in 7 Countries: Implications for Pharmacy
Bibliographic record
Abstract
Purpose The purpose of this study was to determine the rate of self-reported errors in Canada compared with other countries, and to identify risk factors for medical error. Methods In 2007, the Commonwealth Fund surveyed a sample of adults in 7 industrialized nations: Australia, Canada, Germany, the Netherlands, New Zealand, the United Kingdom and the United States. The surveys were conducted by telephone by Harris Surveys and country affiliates, with an average interview time of 17 minutes. Data from this source was used to perform a bivariate analysis comparing those individuals who reported having experienced a medical error to those who had not, followed by a logistic regression model in order to delineate the relationship between medical error and several explanatory variables. The goodness of fit of the final model was assessed, as was the possible presence of multicollinearity. All data analysis was performed using SPSS Version 16.0. Results Overall, 11,910 respondents from 7 countries were included in the analysis. The rate of self-reported medical error ranged from 12%– 20% in the 7 nations. Approximately 1 in 6 (17%) Canadians reported having experienced at least 1 error in the previous 2 years, which translates to 4.2 million adult Canadians. Several variables were found to have a statistically significant relationship to self-reported medical errors in the final regression model, including high prescription drug use (4 or more medications), presence of a chronic condition, lack of physician time with the patient, age under 65, lack of patient's involvement in care, perceived inadequate nursing staffing and absence of a regular doctor. Conclusions This study has demonstrated that medical error is a commonly occurring problem from the perspective of patients in 7 industrialized countries. The risk factors for self-reported medical error that have been identified in this study should aid clinicians, including pharmacists, in the design and implementation of targeted strategies to address this issue. By proactively identifying patient, provider and system-related risk factors for medical error, the opportunity for improving the safety of Canada's health care system could be greatly enhanced. While not all of the errors identified in this study are related to medications, there are implications for pharmacy practice. More specifically, our results suggest that pharmacists should be extra vigilant with patients with high prescription drug use and chronic conditions and ensure that patients are given the opportunity to be involved in their care.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".