Leadership for reducing medical errors via organizational culture: a literature review
Bibliographic record
Abstract
Purpose Medical errors have become the third leading cause of death in the USA. Two million deaths from preventable medical errors will occur annually worldwide each year. The purpose of this paper is to find themes from the literature relating leadership styles – leadership approaches in practice – with success in reducing medical errors and patient safety. Design/methodology/approach This review analyzed primary and secondary sources based on a search for the terms leadership OR leadership style AND medical errors OR patient safety using five high-quality health-care-specific databases: Healthcare Administration Database from Proquest, LLC, Emerald Insight from Emerald Publishing Limited, ScienceDirect from Elsevier, Ovid from Ovid Technologies and MEDLINE with Full-Text from Elton B. Stevens Company. After narrowing, the review considered 21 sources that met the criteria. Findings The review found three leadership approaches and four leadership actions connected to successfully reducing medical errors and improving patient safety. Transformational, authentic and shared leadership produced successful outcomes. The review also found four leadership actions – regular checks on the front line and promoting teamwork, psychological safety and open communication – associated with successful outcomes. The review concluded that leadership appeared to be the preeminent factor in reducing medical errors and improving patient safety. It also found that positive leadership approaches, regardless of the safety intervention, led to improving results and outcomes. Research limitations/implications This review was limited in three ways. First, the review only included sources from the USA, the UK, Canada and Australia. While those countries have similar public-private health-care systems and similar socioeconomics, the problem of medical errors is global (Rodziewicz and Hipskind, 2019). Other leadership approaches or actions may have correlated to reducing medical errors by broadening the geographic selection parameters. Future research could remove geographic restrictions for selection. Second, the author has a bias toward leadership as distinctive from management. There may be additional insights gleaned from expanding the search terms to include management concepts. Third, the author is a management consultant to organizations seeking to improve health-care safety. The author’s bias against limited action as opposed to strategic leadership interventions is profound and significant. This bias may generalize the problem more than necessary. Practical implications There are three direct practical implications from this review. The limitations of this review bound these implications. First, organizations might assess strategic and operational leaders to determine their competencies for positive leadership. Second, organizations just beginning to frame or reframe a safety strategy can perhaps combine safety and leadership interventions for better outcomes. Third, organizations could screen applicants to assess team membership and team leadership orientation and competencies. Originality/value This review is valuable to practitioners who are interested in conceptual relationships between leadership approaches, safety culture and reducing medical errors. The originality of this research is limited to that of any literature review. It summarizes the main themes in the selected literature. The review provides a basis for future considerations centered on dual organizational interventions for leadership and safety.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".