Disclosure of adverse events in the United States and Canada: an update, and a proposed framework for improvement
AbstractThere is consensus that physicians, health professionals and health care organizations should discuss harm that results from health care delivery (adverse events), including the reasons for harm, with patients and their families. Thought leaders and policy makers in the USA and Canada support this goal. However, there are gaps in both countries between patients and physicians in their attitudes about how errors should be handled, and between disclosure policies and their implementation in practice. This paper reviews the state of disclosure policy and practice in the two countries, and the barriers to full disclosure. Important barriers include fear of consequences, attitudes about disclosure, lack of skill and role models, and lack of peer and institutional support. The paper also describes the problem of the second victim, a corollary of disclosure whereby health care workers are also traumatized by the same events that harm patients. The presence of multiple practical and personal barriers to disclosure suggests the need for a comprehensive solution directed at multiple levels of the health care system, including health departments, institutions, local managers, professional staff, patients and families, and including legal, health system and local institutional support. At the local level, implementation could be based on a translating-evidence-into-practice framework. Applying this framework would involve the formation of teams, training, measurement and identification of local barriers to achieving universal disclosure of adverse events.
PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.
Copyright (c) 2013 Albert W. Wu, Dennis J. Boyle, Gordon Wallace, Kathleen M. Mazor
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.