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Institutional resilience in healthcare systems
Open accessAbstract
A recent report for the President of the United States described the impact of preventable medical errors as a “national problem of epidemic proportions”.1 Similar concerns have been echoed in the report of an expert group chaired by the Chief Medical Officer.2 In this report it was estimated that 400 people in the UK die or are seriously injured each year in adverse events involving medical devices, and that harm to patients arising from medical errors occurs in around 10% of admissions—or at a rate in excess of 850 000 per year. The cost to the NHS in additional hospital stays alone is estimated at around £2 billion a year. Safety has two faces. The negative face is very obvious and is revealed by adverse events, mishaps, near misses, and so on. This aspect is very easily quantified and so holds great appeal as a safety measure. The other, somewhat hidden, aspect offers a more satisfactory means of assessing safety. This positive face can be defined as the system's intrinsic resistance to its operational hazards. Some organisations will be more robust in coping with the human and technical dangers associated with their daily activities. This will be as true for healthcare institutions as it is for other systems engaged in hazardous activities. In short, some organisations will be in better “safety health” than others. The ideas of resistance and vulnerability can be represented as the extremes of a notional space termed the “safety space” (fig 1). The horizontal axis of the space runs from an extreme of maximum attainable resistance (to operational hazards) on the left to a maximum of survivable vulnerability on the right. A number of hypothetical organisations are located along this resistance vulnerability dimension. The cigar-shaped space shows that most organisations will occupy an approximately …
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Cited by
Feasibility and Acceptability of a Resilience Training Program for Intensive Care Unit Nurses
Mealer, M., Conrad, D., Evans, J., Jooste, K., Solyntjes, J., Rothbaum, B., & Moss, M. (2014) · American Journal of Critical Care · doi:10.4037/ajcc2014747 · Cited by 316
The Ongoing Quality Improvement Journey: Next Stop, High Reliability
Chassin, M. R., & Loeb, J. M. (2011) · Health Affairs · doi:10.1377/hlthaff.2011.0076 · Cited by 294
High reliability organizations (HROs)
Sutcliffe, K. M. (2011) · Best Practice & Research Clinical Anaesthesiology · doi:10.1016/j.bpa.2011.03.001 · Cited by 272
General attributes of safe organisations
Schulman, P. R. (2004) · Quality and Safety in Health Care · doi:10.1136/qshc.2003.009613 · Cited by 104
Resilient practices in maintaining safety of health information technologies
Smith, M. W., Ash, J. S., Sittig, D. F., & Singh, H. (2014) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/1555343414534242 · Cited by 26
“…to disruptions, learning from past experiences (Holl-nagel, 2009), management commitment, flexibility, buffering capacity, awareness of risk (Carthey, De Leval, & Reason, 2001; Costella, Saurin, & de Macedo Guimarães, 2009; Woods, 2006), and using control systems to maintain functioning in…”
Background
Metadata
- Authors
- Carthey, J., de Leval, M. R., & Reason, J. T.
- Venue
- Quality in Health Care
- Publisher
- BMJ
- Published
- 2001-03
- Volume
- 10
- Issue
- 1
- Pages
- 29-32
- ISSN
- 1475-3898 (ISSN-L)
- 0963-8172
- 1475-3901
- OpenAlex
- W2105206982
- Cited by
- 191
- References
- 0
- FWCI
- 7.74
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.