Perceptions of Patients and Health Care Professionals about Factors Contributing to Medication Errors and Potential Areas for Improvement
Bibliographic record
Abstract
ABSTRACT Background: Significant efforts have been directed to understanding medication errors in recent years, but there has been little work to compare the perceptions of health care professionals and patients regarding such errors. Objective: To determine the factors contributing to medication errors and related areas for improvement, as perceived by health care professionals and patients, and to compare and contrast these perceptions. Methods: Medication errors documented at South Shore Health hospitals in Nova Scotia from February 2002 to June 2004 were compiled and analyzed to identify trends. Trends and examples of medication errors were presented to 2 focus groups, the first consisting of health care professionals and the second consisting of patients. Participants were asked to identify factors perceived as contributing to errors using the nominal group technique and to identify possible areas for improvement using an Ishikawa (fishbone) diagram. Results: Health care professionals and patients identified different factors as contributing to errors. Health care professionals identified factors related to individuals, whereas patients identified both individual and system-wide factors. According to the fishbone diagram, participants felt that “people” and “procedures and management” are the areas where interventions to reduce medication errors should be primarily directed. Conclusions: A wide range of factors perceived as contributing to medication errors were identified. These results provide valuable information that could be used to improve the medication use system at South Shore Health. ABSTRACT Historique : Des efforts considerables ont ete deployes ces dernieres annees pour comprendre les erreurs de medication, mais peu pour comparer les perceptions des professionnels de la sante et celles des patients sur ce sujet. Objectif : Determiner les facteurs qui contribuent aux erreurs de medication et les elements connexes a ameliorer, d’apres les perceptions des professionnels de la sante et des patients, et comparer ces perceptions et en faire ressortir les differences. Methodes : Les erreurs de medication documentees dans les hopitaux de la regie de la sante South Shore Health de NouvelleEcosse entre fevrier 2002 et juin 2004 ont ete compilees et analysees pour en degager les tendances. Ces dernieres et des exemples d’erreurs de medication ont ete presentes a deux groupes de discussion, le premier forme de professionnels de la sante, le second compose de patients. On a demande aux participants de cerner les facteurs qui a leurs yeux contribuaient a la survenue des erreurs de medication, en utilisant la technique du groupe nominal, et les elements connexes qui pourraient etre ameliores, en utilisant un diagramme d’Ishikawa (causes-effet). Resultats : Les professionnels de la sante et les patients ont defini differents facteurs qui contribuaient a la survenue des erreurs. Les professionnels de la sante ont cerne des facteurs lies aux personnes, alors que les patients ont releve des facteurs lies a la fois aux personnes et aux systemes en place. D’apres le diagramme de causes-effet, les participants ont estime que les interventions visant a reduire les erreurs de medication devaient etre principalement dirigees sur deux categories de causes: « personnes » et « methodes et gestion ». Conclusions : Un large eventail de facteurs percus comme contribuant a la survenue des erreurs de medication a ete defini. Les resultats fournissent des renseignements precieux qui pourraient servir a ameliorer les systemes de distribution des medicaments a la regie regionale de la sante South Shore Health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".