How trainees would disclose medical errors: educational implications for training programmes.

  • White A A
  • Bell K S
  • Krauss J M
 et al. 
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Medical Education 2011: : 372-380 The disclosure of harmful errors to patients is recommended, but appears to be uncommon. Understanding how trainees disclose errors and how their practices evolve during training could help educators design programmes to address this gap. This study was conducted to determine how trainees would disclose medical errors. We surveyed 758 trainees (488 students and 270 residents) in internal medicine at two academic medical centres. Surveys depicted one of two harmful error scenarios that varied by how apparent the error would be to the patient. We measured attitudes and disclosure content using scripted responses. Trainees reported their intent to disclose the error as 'definitely' (43%), 'probably' (47%), 'only if asked by patient' (9%), and 'definitely not' (1%). Trainees were more likely to disclose obvious errors than errors that patients were unlikely to recognise (55% versus 30%; p < 0.01). Respondents varied widely in the type of information they would disclose. Overall, 50% of trainees chose to use statements that explicitly stated that an error rather than only an adverse event had occurred. Regarding apologies, trainees were split between conveying a general expression of regret (52%) and making an explicit apology (46%). Respondents at higher levels of training were less likely to use explicit apologies (trend p < 0.01). Prior disclosure training was associated with increased willingness to disclose errors (odds ratio 1.40, p = 0.03). Trainees may not be prepared to disclose medical errors to patients and worrisome trends in trainee apology practices were observed across levels of training. Medical educators should intensify efforts to enhance trainees' skills in meeting patients' expectations for the open disclosure of harmful medical errors.

Author-supplied keywords

  • Adult
  • Chi Square Test
  • Confidence Intervals
  • Cross Sectional Studies
  • Data Analysis Software
  • Data Analysis, Statistical
  • Descriptive Statistics
  • Education, Medical -- Trends
  • Female
  • Fisher's Exact Test
  • Funding Source
  • Human
  • Male
  • Missouri
  • Odds Ratio
  • P-Value
  • Student-Patient Relations
  • Students, Medical -- Psychosocial Factors
  • Surveys
  • Treatment Errors -- Evaluation
  • Truth Disclosure -- Evaluation
  • Two-Tailed Test

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  • Andrew White A

  • Sigall Bell K

  • Melissa Krauss J

  • Jane Garbutt

  • Dunagan W, Claiborne

  • Victoria Fraser J

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