Prescribing errors and uncertainty: Coping strategies of physicians and pharmacists in a tertiary university hospital
Bibliographic record
Abstract
Introduction: Prescribing errors and uncertainty are of increasing concern to health professionals due to their prevalence and implications for patient safety and wellness.Objectives: To assess the coping strategies of doctors and pharmacists who experienced or observed prescribing errors and uncertainty in a tertiary university hospital, and the implications for therapeutic outcomes.Methods: A self-assessment questionnaire was used to elicit information from a convenience sample of 94 physicians and 35 pharmacists of at least 2 years working experience in a tertiary hospital in Lagos, Nigeria, from October to December 2014. Ethical approval was sought and obtained for the study. The research instrument was validated by experts in the field of medicine, hospital pharmacy, and strategic management, and pilot-tested. Concerns and attitudes to committing/observing prescription errors and at different uncertainty levels were assessed. Also the outcomes of their encounters, specific actions taken by the two professional groups when faced with prescribing errors, causes of, and non-detection of prescribing errors, methods used to deal with the errors, and the extent to which pharmacy intervention was successful, were evaluated.Results: Doctors and pharmacists (35.1% vs. 40%) admitted committing medication errors, while both professional groups (10.6% vs. 20%) admitted having avenues to discuss prescription errors. They also admitted prescribing or dispensing more, respectively when decision uncertainty was least. None of the doctors and few pharmacists admitted telling the patient about any prescription errors committed or observed respectively. There were varied responses on the causes of errors and non-detection of prescription errors. Coping strategies in terms of the use of technologies, medium and mode of communication, and use of continuing education to minimize errors, all fall below expectations for mitigating errors in prescribing and uncertainty.Discussion and Conclusions: A number of variables assessed on good prescribing decisions and uncertainty were at variance with the studies from other countries. An organizational culture and structure that promote collaboration in prescribing decisions, infrastructural facilities, effective communication, enabling decision support systems, and relevant continuing education are needed to foster a care-process that is less prone to prescribing errors and uncertainty.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".