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Western Journal of Medicine
Article . 2000 . Peer-reviewed
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Article
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BMJ
Article . 2000 . Peer-reviewed
Data sources: Crossref
BMJ
Article . 2000
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Epidemiology of medical error

Authors: N Saul Weingart; Ross McL Wilson; Bernadette T Harrison; Robert W. Gibberd;

Epidemiology of medical error

Abstract

Newspaper and television stories of catastrophic injuries occurring at the hands of clinicians spotlight the problem of medical error but provide little insight into its nature or magnitude.1 Clinicians, patients, and policymakers may underestimate the magnitude of risk and the extent of harm. We review the epidemiology of medical error, concentrating primarily on the prevalence and consequences of error, which types are most common, which clinicians make errors, and the risk factors that increase the likelihood of injury from error. #### Summary points The Harvard and Australian studies into medical error remain the only studies that provide population level data on the rates of injuries to patients in hospitals and they identified a substantial amount of medical error In the United States medical error results in 44 000–98 000 unnecessary deaths each year and 1 000 000 excess injuries Errors often occur when clinicians are inexperienced and new procedures are introduced Extremes of age, complex care, urgent care, and a prolonged hospital stay are associated with more errors ### Benchmark studies The Harvard study of medical practice is the benchmark for estimating the extent of medical injuries occurring in hospitals. Brennan et al reviewed the medical charts of 30 121 patients admitted to 51 acute care hospitals in New York statein 1984.2 They reported that adverse events—injuries caused by medical management that prolonged admission or produced disability at the time of discharge—occurred in 3.7% of admissions. A subsequent analysis of the same data found that 69% of injuries were caused by errors.3 In a study of the quality of Australian health care, a population based study modelled on the Harvard study, investigators reviewed the medical records of 14 179 admissions to 28 hospitals in New South Wales and South Australia in 1995.4 An adverse event occurred in 16.6% of admissions, resulting in …

Keywords

Outpatient Clinics, Hospital, Medical Errors, Iatrogenic Disease, Age Factors, Workload, Middle Aged, Hospitalization, Risk Factors, Medical Staff, Hospital, Prevalence, Humans, Aged

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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    543
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 0.1%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
543
Top 1%
Top 0.1%
Top 1%
gold