Are quality indicators predictive of compensated injury
Pietro Giorgio Lovaglio
Published online: 18 June 2016
Ó Springer Science+Business Media Dordrecht 2016
The seminal US report ‘‘To err is human: building a safer health system’’ of the Institute of
Medicine (Kohn et al. 2000) has dramatically inﬂuenced the debate over clinical errors in
healthcare, both in the United States and internationally, establishing healthcare safety as a
fundamental issue for health stakeholders and the public opinion.
The interest in monitoring adverse events—deﬁned as unintentional injuries or com-
plications caused by healthcare management—resulting in disability, death or prolonged
hospital stay for hospitalized patients (Harvard Medical Practice Study Investigators 1990),
is essentially motivated by the priority to reduce the incidence of medical errors in the
health sector, as well as by the ﬁnancial pressure related to risk and liability insurance costs
and reimbursements for damages to patients (Vincent 1997, 2001; Kohn et al. 2000).
Although it is difﬁcult to obtain a reliable estimate of errors, there is international
consensus that, among hospitalized patients worldwide, 3–16 % suffer injury as a result of
medical interventions and at least half of these are preventable adverse events (Leape et al.
1991; Leape 2008; de Vries et al. 2008; Oyebode 2013).
Considering the difﬁculty in collecting reliable data on adverse events and claims, the
imperative need is to develop instruments aimed at drastically reducing the occurrence of
errors of organizational/medical nature, even in advanced health systems.
The principal approach to patient safety in the UK, the United States, and many other
countries has been to establish local and national reporting systems.
In 2010 the Italian ministries of labor and social policy and of health set up a data
stream—the information system for monitoring errors in healthcare (SIMES)—with the
aim to detect information related to sentinel events and claims.
& Pietro Giorgio Lovaglio
Department of Statistics and Quantitative Methods for Business Economic Sciences, University
Bicocca-Milan, Via Bicocca degli Arcimboldi 8, 20126 Milan, Italy
Qual Quant (2017) 51:1903–1919