Medical Errors: Root Cause Analysis
sjd_
AB5.pdf
Risk factors for patient-reported medical errors in eleven countries
David L. B. Schwappach MPH PhD*� *Scientific Head, Swiss Patient Safety Foundation, Zuerich, Switzerland and �Institute of Social and Preventive Medicine (ISPM), Senior lecturer, University of Bern, Bern, Switzerland
Correspondence
David L. B. Schwappach MPH, PhD
Swiss Patient Safety Foundation
Asylstr. 77, 8032 Zuerich
Switzerland
E-mail: schwappach@
patientensicherheit.ch
Accepted for publication 12 October 2011
Keywords: medical errors,
patient-reported outcomes,
safety, survey
Abstract
Objectives The aim of this study was to identify common risk
factors for patient-reported medical errors across countries. In
country-level analyses, differences in risks associated with error
between health care systems were investigated. The joint effects of
risks on error-reporting probability were modelled for hypothetical
patients with different health care utilization patterns.
Design Data from the Commonwealth Fund�s 2010 lnternational Survey of the General Public�s Views of their Health Care System�s Performance in 11 Countries.
Setting Representative population samples of 11 countries were
surveyed (total sample = 19 738 adults). Utilization of health care,
coordination of care problems and reported errors were assessed.
Regression analyses were conducted to identify risk factors for
patients� reports of medical, medication and laboratory errors across countries and in country-specific models.
Results Error was reported by 11.2% of patients but with marked
differences between countries (range: 5.4–17.0%). Poor coordination
of care was reported by 27.3%. The risk of patient-reported error
was determined mainly by health care utilization: Emergency care
(OR = 1.7, P < 0.001), hospitalization (OR = 1.6, P < 0.001)
and the number of providers involved (OR three doctors = 2.0,
P < 0.001) are important predictors. Poor care coordination is the
single most important risk factor for reporting error (OR = 3.9,
P < 0.001). Country-specific models yielded common and country-
specific predictors for self-reported error. For high utilizers of care,
the probability that errors are reported rises up to P = 0.68.
Conclusions Safety remains a global challenge affecting many
patients throughout the world. Large variability exists in the
frequency of patient-reported error across countries. To learn from
others� errors is not only essential within countries but may also prove a promising strategy internationally.
doi: 10.1111/j.1369-7625.2011.00755.x
� 2012 John Wiley & Sons Ltd Health Expectations, 17, pp.321–331 321
Introduction
Patient safety remains a major challenge for
health care systems worldwide. 1 A recent chart
review study conducted in the Netherlands
reports the incidence of one or more adverse
events as 5.7% of all hospital admissions of
which 40% were deemed preventable. 2 In Swe-
den, the incidence of adverse events was 12.3%
of hospital admissions with 70% being judged as
preventable. 3 Similar data have been reported
for several countries recently, including the
United States, New Zealand, Canada and oth-
ers. 4–7
On the basis of these studies, it can be
concluded that approximately one of thousand
hospital patients dies as a result from prevent-
able adverse events. Many patients suffer from
adverse events after discharge and are therefore
not identified in record-based studies. 8
Less research has been conducted in the out-
patient care setting but the available studies
suggest that patients are at considerable risk as
well. In particular, preventable adverse drug
events are frequent among patients in outpatient
care. 9,10
Gurwitz et al. 11 report an overall rate of
adverse drug events among older patients in the
ambulatory setting of 50.1 ⁄ 1000 person-years, of which 28% were considered preventable.
Studies based on staff members� incident reports in the United Kingdom yielded an error report
rate of 75 ⁄ 1000 patient contacts in outpatient care.
12 In a similar study in the United States,
errors and preventable adverse events were
reported after 24% of outpatient visits. 13
In
Australia, the incidence of error reported to an
anonymous reporting system by general practi-
tioners was 0.24% per patient seen per year. 14
These setting-specific studies are valuable and
important to identify and understand specific
threats, e.g. hospital care or outpatient drug
therapy. However, the frequency and harm of
error is then investigated in isolation for specific
health care sectors, settings or even therapies or
treatments (e.g. medical errors in in-patient
cancer treatment). But many patients utilize
several types of health care in different settings,
and the associated risks accumulate or even
exponentiate because of coordination and com-
munication failures among different providers.
To assess patients� total risk, longitudinal observation of patient cohorts would be possible
in theory but is methodologically challenging
and has not yet been undertaken to the author�s knowledge. Another methodological approach
to the accumulated likelihood of error is the
survey of citizens or patients. As patients are the
only individuals physically present during every
treatment and consultation, they carry with
them important contextualized information in
particular with relation to transition between
different settings. 15,16
Surveying patients about
their experience of medical error across specific
types of health care consumed, e.g. hospital care,
can help to identify risk factors for error along
the care continuum and relative to specific
patient-level factors and the amount and type of
health care utilized.
In addition, such patient surveys of error
experience conducted in a multinational design
can inform health policy about common risk
factors across countries and those specific to
different health care systems. For example, some
countries may perform better in ensuring safe
transition and coordination of inpatient and
outpatient care than others. The main aim of
this analysis was the identification of risk factors
for patient-reported medical errors across sev-
eral countries. Country-level analyses were
conducted to investigate differences in risks
associated with error between different health
care systems. To evaluate the joint effects of the
identified risk factors, the probability that
hypothetical patients with different personal and
health-related profiles and health care utilization
patterns would report error in their care was
modelled.
Methods
Design
This analysis is based on data from �The Com- monwealth Fund�s 2010 lnternational Survey of the General Public�s Views of their Health Care System�s Performance in 11 Countries�, which was conducted in Australia, Canada, France,
Risk factors for patient-reported medical errors, D L B Schwappach
� 2012 John Wiley & Sons Ltd Health Expectations, 17, pp.321–331
322
Germany, the Netherlands, New Zealand, Nor-
way, Sweden, Switzerland, the United Kingdom
and the United States in 2010 [details are avail-
able at http://www.commonwealthfund.org/
Content/Surveys/2010/Nov/2010-International-
Survey.aspx]. Computer-assisted telephone
interviews were conducted with nationally rep-
resentative samples of adults aged 18 and above
in each of these countries. Samples were drawn
from residential phone number lists, random
number lists or random digit dialing. National
samples differ in the extent to which cell lines
were included. The interviewee in each house-
hold was selected at random based on the most
recent birthday in most countries. All sample
records were called eight times or more before
being abandoned as unusable. The interviews
were conducted by professional interviewing
staff and took on average 18–21 min across
countries. Response rates varied from 13% in
Norway to 54% in Switzerland.
Survey
The Commonwealth Fund�s 2010 lnternational Survey assessed public confidence in the health
care system including access to care, cost and
quality of care. Methods and results of earlier
versions of the survey have been published pre-
viously. 17–19
For the purpose of this analysis, the following
items relating to medical error experience are of
particular relevance: whether respondents were
ever been given the wrong medication or wrong
dose by a doctor, nurse, hospital or pharmacist in
the past 2 years (referred to as �medication error� hereinafter); whether there was a time in the past
2 years the responder thought a medical mistake
was made in her treatment or care (referred to as
�medical error� hereinafter); whether the responder has been given incorrect results for a
diagnostic or laboratory test in the past 2 years
(referred to as �lab error� hereinafter). The response categories were yes, no, not sure and
decline to answer. Participants that reported any
of the above errors were also asked whether the
error occurred while they were hospitalized (yes,
in the hospital, no, not sure, decline to answer).
Participants were also asked several questions
related to demographics, their health and utili-
zation of health care services. Responses to three
items that asked for experience of poor coordi-
nation of care in the past 2 years were also
included in the analysis: whether subjects
reported (i) test results or medical records were
unavailable at the time of a scheduled appoint-
ment; (ii) receiving conflicting information from
different providers; (iii) doctors ordered medical
tests that had already been performed.
Data analysis
Raw survey data were weighted for age, sex,
education and region according to the most
recent national census to reflect demographic
distributions. To dichotomize data for analysis,
�not sure� and �decline to answer� responses were recoded to missing.
An aggregate measure was computed that
captures experience of any of the specific error
items. We report descriptive analysis for all
individual error items and the aggregate measure
per country. To identify potential predictors,
several demographic, health-related and heath
care utilization variables were tested for their
individual association with error experience in
bivariate analyses: age, gender, education,
income (relative to national averages), general
health status, presence of chronic conditions
(out of a specified list of conditions), having a
regular doctor, number of doctors seen in the
past 12 months, specialist care in the past
2 years, elective surgery in the past 2 years,
hospital stay in the past 2 years, emergency care
use in the past 2 years, medical tests (laboratory,
X-ray, etc.) in the past 2 years and current reg-
ular use of prescription drugs. Responses to
three coordination of care items were used to
compute an indicator variable indicating expe-
rience of none vs. any of these three events. All
individual variables that were significantly
associated with error experience in bivariate
analyses at the 0.1 level were entered into the
logistic regression model. Logistic regression
was conducted for the aggregate measure, i.e.
report of �any error�, and for each of the
Risk factors for patient-reported medical errors, D L B Schwappach
� 2012 John Wiley & Sons Ltd Health Expectations, 17, pp.321–331
323
individual error items as dependent variables.
Multicollinearity of the predictor variables was
assessed using the variance inflation factor
(VIF). VIFs > 10 were inspected, and multi-
collinear variables were omitted from the mod-
els. Model fit was assessed using the Archer–
Lemeshow goodness-of-fit statistic, a F-adjusted
mean residual goodness-of-fit test under com-
plex sampling. 20
To evaluate the joint effects of
the identified risk factors across all countries, we
predicted the probability that hypothetical sub-
jects (patients A–F) with different personal and
health-related profiles and health care utilization
patterns would report any error in their care. We
also conducted country-specific analyses for
three countries (United States, United Kingdom
and Germany) that represent prototypes of
health care system organization, i.e. market-
driven, public and social insurance-based health
care systems. Country-specific analyses were
conducted using logistic hierarchical backward
selection with the aggregate measure as outcome
variable. This approach was selected because of
the limited size of the country-specific samples.
Hierarchical stepwise regression differs to com-
mon stepwise regression in that potential pre-
dictors are grouped and ordered based on
theory. The sequence in which groups are tested
is not arbitrary. Guided by theoretical consid-
erations, predictors were tested in the following
blocks and sequences for each of the three
country-specific models: (gender) (age) (income,
education) (poor health, number of chronic
conditions) (specialist care, number of doctors
seen) (number of prescriptions drugs) (emer-
gency care) (surgery, hospital) (coordination of
care). Beginning with the first grouping (i.e.
gender), the effect of each block was tested
backwards and the entire block discarded if non-
significant. Significant blocks were included as a
whole. Data were analysed using the software
package STATASTATA v11.2. 21
Results
Interviews were completed with 19 738 adults
aged 18 and above. Sample characteristics are
provided in Table 1. Self-reported error in
health care was common in all countries but
with marked differences even within European
countries (Table 2). For example, only 2.2% of
responders in the United Kingdom but 8.6% of
French participants reported a medication error
in the past 2 years. Overall, one of ten citizens
self-reported a medical or medication error
during the last 2 years. 18.8% of responders
across countries reported that the last error in
their care occurred in hospital, but this fraction
varied considerably between countries and
ranged from 12.3% in Sweden to 31.3% in
Switzerland (P < 0.001). Across countries, the
Table 1 Sample characteristics, weighted data (n = 19 738)
Characteristic n (%) of participants
Country
Australia 3552 (18.0)
Canada 3302 (16.7)
France 1402 (7.1)
Germany 1005 (5.1)
Netherlands 1001 (5.1)
Norway 1058 (5.4)
New Zealand 1000 (5.1)
Sweden 2100 (10.6)
Switzerland 1306 (6.6)
United Kingdom 1511 (7.7)
United States 2501 (12.7)
Female gender 11 537 (51.5)
Age, mean 48.4 years
18–29 years 2212 (17.6)
30–49 years 6467 (36.9)
50–64 years 5632 (24.6)
65 years and above 5427 (20.9)
Education (recoded from nation-specific response codes)
High school or less 9984 (58.4)
Some college 4266 (21.4)
College graduate or higher 5150 (20.3)
Income (relative to national averages)
Much below average 3275 (17.1)
Somewhat below average 3412 (18.9)
Average 4854 (26.9)
Somewhat above average 4441 (24.6)
Much above average 2365 (12.5)
Self-rated health
Excellent ⁄ very good 10 522 (53.9) Good 6262 (31.5)
Fair ⁄ poor 2876 (14.6) Chronic conditions
None 7429 (42.0)
1 condition 5137 (26.0)
2 or more conditions 7119 (32.0)
Risk factors for patient-reported medical errors, D L B Schwappach
� 2012 John Wiley & Sons Ltd Health Expectations, 17, pp.321–331
324
fraction of respondents that reported experience
of two different types of error was 2.5%, and
0.5% reported all three types of errors. Poor
coordination of care was also common in all
countries: 10.9% reported that test results or
medical records were not available, 19.6% per-
ceived to have received conflicting information
by care providers and 10.5% reported that tests
were ordered although they had been performed
before. A quarter of citizens (27.3%) reported
any of these coordination problems in the past
2 years.
A number of variables were associated with
patient-reported error in bivariate analysis
(Fig. 1). Across all countries, health status and
health care utilization variables were associated
with all three types of self-reported errors (and
the aggregate measure) with different levels of
strength. Associations between demographic
variables and errors were less systematic: Higher
age was inversely related to all types of reported
errors, except medication errors. Female gender
was associated with medical error, medication
error and the aggregate measure, but not the
Table 2 Frequency of self-reported errors by country, weighted data
Country
Medical error
n (%)
Medication
error n (%)
Either medical
or medication
error n (%)
Laboratory
error* n (%)
Either medical,
medication or
laboratory error
(aggregate
measure) n (%)
Australia 282 (8.3) 155 (4.5) 350 (10.1) 69 (2.4) 395 (11.4)
Canada 212 (7.7) 179 (6.0) 322 (10.9) 106 (4.1) 372 (12.2)
France 87 (5.9) 110 (8.6) 157 (11.6) 39 (2.8) 178 (12.5)
Germany 54 (5.9) 20 (2.2) 64 (7.0) 12 (1.7) 73 (7.8)
Netherlands 52 (4.8) 45 (4.5) 82 (7.8) 25 (3.0) 97 (9.3)
Norway 101 (10.8) 79 (8.1) 147 (15.7) 29 (3.4) 161 (17.0)
New Zealand 59 (5.6) 39 (4.6) 82 (8.3) 19 (2.4) 92 (9.0)
Sweden 118 (6.1) 92 (4.9) 173 (8.9) 26 (1.9) 184 (9.5)
Switzerland 81 (8.0) 61 (5.3) 123 (11.4) 31 (3.2) 136 (11.9)
United Kingdom 39 (3.2) 25 (2.2) 55 (4.7) 21 (2.6) 66 (5.4)
United States 204 (9.7) 150 (6.4) 295 (12.9) 83 (5.0) 331 (14.3)
*Based on those that reported blood test, X-rays or other tests in the past 2 years.
***
**
***
***
***
***
***
***
***
***
***
***
***
***
***
Age > 65 years Female gender
Education, high school or less Income much below average
Poor self−rated health 1 chronic cond.
2 or more chronic cond. Regular doctor Specialist care
1−2 doctors 3 or more doctors
Elective surgery Inpatient stay
Emergency care 1 prescription drug
2 or more prescription drugs Poor care coordination
0 1 2 3 4 5 6 7 8 Odds ratio
Figure 1 Bivariate (unadjusted) asso-
ciations between demographic, health
and health care utilization variables
and experience of any error (aggregate
measure), weighted data. Stars indi-
cate significant associations
(*P < 0.05; **P < 0.01;
***P < 0.01).
Risk factors for patient-reported medical errors, D L B Schwappach
� 2012 John Wiley & Sons Ltd Health Expectations, 17, pp.321–331
325
subset of laboratory errors. Low income was
associated with all types of reported errors,
again except laboratory errors. Education was
only weakly associated with reporting medical
error.
Results of the regression model for all 11
countries and three country-specific models are
presented in Table 3. All VIFs were <2.0 indi-
cating no substantial multicollinearity. The
Archer–Lemeshow goodness-of-fit statistic did
not indicate any overall model departure from
the observed data. Across countries, the risk of
patient-reported error is determined mainly by
health care utilization. Emergency care, hospi-
talization and the number of providers involved
are among the most important predictors.
Having seen three or more doctors doubles the
risk for reporting any error when other factors
are controlled for, e.g. health status and use of
prescription drugs. Experience of poor care
coordination is the single most important risk
factor, associated with a four-fold increase in
reporting error. Responders with chronic con-
ditions and poor health are at considerably
higher risk for reporting errors in their care,
even after adjusting for a large variety of health
care utilization. After controlling for health and
health care utilization, patients younger than
65 years were nearly twice as likely to report any
medical error.
The joint influence of the risk factors on the
probability that patients report error in their
care is substantial (illustrated in Fig. 2). For
example, the differences between hypothetical
patients B and F (chronic conditions, emergency
care, prescription drugs, number of doctors seen,
specialist care and coordination of care prob-
lems) account for a 14-fold increase in proba-
bility of reporting error, keeping younger age,
low income, poor self-reported health, hospital
stay and surgery constant (pB = 0.049,
pF = 0.679, P < 0.001).
Three country-specific models yield common
and country-specific predictors for self-reported
error. Poor coordination of care experiences was
the strongest predictor for patient-reported error
in all three countries. Hospital care in the past
2 years was associated with reporting error in
the United Kingdom and Germany, but not in
the United States. On the contrary, poor health,
specialist care and emergency care increase the
likelihood of self-reported error in the United
States, but not in the United Kingdom and
Germany. Use of prescription drugs was a sig-
nificant predictor only in the United Kingdom.
Having a much below average income was a
strong predictor for reporting error experience
in Germany.
Discussion
This study reports new data on patients� per- ceptions of error in 11 countries and identified a
number of important risk factors. Overall, one
of ten surveyed patients reported either medical,
medication or laboratory errors in their care but
this risk varies markedly by a factor of 3 across
countries (5.4% in the United Kingdom and
17.0% in Norway). Several factors may help to
explain this finding: Different health care sys-
tems may in fact perform better in preventing
errors and can thus deem to be safer. However,
observed differences between countries may also
stem from differences in patients� likelihood to identify and report error, rather than differences
in true incidences. While evidence shows that
patients� reports of adverse events are often in well concordance with other detection methods,
e.g. record review, it is unclear whether this
degree of concordance is similar across coun-
tries. 22–25
For example, safety in health care may
be an issue of high public awareness in some
countries and largely unrecognized in others. As
a result, patients may be more or less vigilant
and educated about safety and have different
abilities or motivation to detect errors. �Medical error� may also be defined differently in diverse cultural contexts. In addition, patients� reports of errors are likely to be affected by official
standards and cultural norms among health care
workers on how openly to communicate errors
towards patients. Thus, patients� reports of error do not only reflect incidence of error but are also
�contaminated� by identification and reporting biases. Reporting effects rather than differences
in frequency may also help to explain why
Risk factors for patient-reported medical errors, D L B Schwappach
� 2012 John Wiley & Sons Ltd Health Expectations, 17, pp.321–331
326
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0 .0
0 1
4 .5
6 2
.3 8
– 8
.7 3
< 0
.0 0
1 4
.2 8
2 .3
1 –
7 .9
2 <
0 .0
0 1
n 1
7 8
2 5
2 4
2 6
1 4
8 3
9 5
2
A rc
h e
r– L e
m e
s h
o w
te s t
s ta
ti s ti
c 1
.2 5
9 0
.2 5
4 0
.7 6
2 0
.6 5
2 0
.3 6
0 0
.8 7
6 0
.0 9
1 0
.9 9
7
Risk factors for patient-reported medical errors, D L B Schwappach
� 2012 John Wiley & Sons Ltd Health Expectations, 17, pp.321–331
327
younger patients were systematically more likely
to report errors compared to respondents aged
65 and above, a finding that has been reported in
the previous studies. For example, in a recent
survey study among Swiss hospital patients, the
likelihood for reporting adverse events during
hospital stay decreased significantly with higher
age by a comparable magnitude. 26
Younger
patients may be more aware of safety problems
and less reluctant to report these.
Across 11 countries, our data clearly show
that risk of self-reported error increases steadily
with the amount and categories of health care
consumed. However, across countries, patients
with poor health and low income are at
increased risk even after adjusting for various
health care utilization-related variables. It is not
surprising that poor care coordination experi-
ence is the most important single risk factor for
reporting errors across countries and in our
country-specific analyses. Unavailable records,
conflicting information and repetition of tests
can signal, cause or coincide with safety events
and can themselves be regarded as �error�, even if they may not cause harm. Thus, it seems likely
that an unknown fraction of responders had the
same event in mind when reporting coordination
of care problems and error. This would lead to
an overestimation of the association of coordi-
nation of care problems with error. Indeed,
Rathert et al. 27
recently reported from a quali-
tative study that patients seem to share a
broader interpretation of safety compared with
health professionals and often include commu-
nication and coordination failures. Our country-
level analyses reveal that the risk associated with
different health care services varies considerably
between countries. This strengthens the
assumption that systems differ in their abilities
to manage specific threats for patient safety.
This view is also supported by the large variance
observed in reported occurrence of error. Hos-
pital-associated error was much more frequent
in some countries (e.g. Switzerland) compared to
the cross-national average. These results may
reflect differences between countries in how care
is organized. For example, access to specialist
outpatient care is far more restrictive in some
countries compared to others.
While our results clearly indicate that various
types of health care consumed increase the risk
of error, the relative magnitude of predictor
variables should be compared with care. As with
all surveys, health care utilization had to be
operationalized for measurement and this oper-
ationalization may interact with specific forms
of care organizations and is thus important for
interpretation: For example, a single hospital
stay is longer and patients are exposed to risk
(and error identification) simply for a longer
0 .0
0 .2
0 .4
0 .6
0 .8
1 .0
P ro
b a
b ili
ty o
f p
a tie
n t−
re p
o rt
e d
e rr
o r
A B C D E F
Hypothetical patients
Figure 2 Predicted probability for patient-reported error
(aggregate measure) across 11 countries for six hypothetical
patients (A–F), weighted data. Six hypothetical patients (A–F)
were modelled with the following characteristics: Patient A:
Aged >65 years, average income, good self-reported health,
two or more chronic conditions, emergency care, no hospital
or surgery, two or more prescription drugs, one or two
doctors, no specialist, no coordination of care problems.
Patient B: Aged <65 years, much below average income,
poor self-reported health, no chronic conditions, no emer-
gency care, hospital stay and surgery, no prescription drugs,
no doctors seen, no specialist, no coordination of care
problems. Patient C: Aged <65 years, average income, poor
self-reported health, one chronic condition, no emergency
care, hospital stay, no surgery, one prescription drug, one or
two doctors, specialist, no coordination of care problems.
Patient D: Aged >65 years, much below average income,
poor self-reported health, no chronic conditions, emergency
care, hospital stay and surgery, no prescription drugs, one or
two doctors, no specialist, poor coordination of care. Patient
E: Aged <65 years, average income, good self-reported
health, one chronic condition, no emergency care, no hospital
stay or surgery, two or more prescription drugs, three or
more doctors, specialist, poor coordination of care. Patient F:
Aged <65 years, much below average income, poor self-
reported health, two or more chronic conditions, emergency
care, hospital stay and surgery, two or more prescription
drugs, three or more doctors, specialist, poor coordination
of care.
Risk factors for patient-reported medical errors, D L B Schwappach
� 2012 John Wiley & Sons Ltd Health Expectations, 17, pp.321–331
328
duration as compared to a short outpatient
consultation. The number of doctors seen does
reflect the increasing need for coordination, but
not necessarily treatment intensity. Thus, for
countries that restrict access to the number of
providers involved, treatment intensity per pro-
vider may be more important.
This study has some limitations: First, the
samples for each of 11 countries have been
drawn and weighted to be representative for
each individual country.
The sample sizes did not allow more extensive
analyses of country-level data, e.g. selection of
predictors based on bivariate analyses or
including the same predictors in all country-
specific models irrespective of their significance.
In addition, the reasons for and potential effects
of the very different survey response rates
remain unclear. For example, Norway had the
lowest response rate (13%) and the highest
fraction of patients that reported any error in
their care (17%). It seems likely that individuals
that experienced error were more likely to par-
ticipate than others. Second, we used an aggre-
gate measure of error as outcome variable in
regression analyses. Distinct associations with
specific types of errors, i.e. medication or labo-
ratory errors, may thus have gone undetected.
Finally, owing to the nature of the data, we
cannot demonstrate causal or temporal rela-
tionship between health care utilization and
error. While responders were asked to consider
the past 2 years in most of the questions, we do
not know whether health care was utilized
before or after the reported events occurred and
how they are connected.
Despite these limitations, the results of this
study are alarming. Our modelling of hypo-
thetical patients shows that for high utilizers of
health care that unify multiple risk factors it is
nearly rule rather than exception that errors
occur. Patients who utilize various types of
health care in different settings accumulate a
high risk of errors, which is largely underesti-
mated in isolated setting-specific adverse event
studies. Despite the associated health-related
harm, the common experience of error in these
populations may also cause considerable loss of
trust in the health care system as a whole.
Medical error is communized with poor coor-
dination of care experiences, and obviously,
health care systems fail to overcome risks asso-
ciated with the segmentation of health care. This
is also indicated by the fact that having a regular
doctor had no substantial protecting effects on
patient safety. These results emphasize that
patient safety remains a global challenge affect-
ing many patients throughout the world. How-
ever, large variability exists in the frequency of
patient-reported error across countries. Taking
the opportunity to learn from others� errors is not only essential within individual institutions
or systems but may also prove a promising
strategy internationally.
Ethics approval
Ethics approval was not necessary for this study.
Acknowledgements
The author thanks the Commonwealth Fund for
permission to analyse the data. The support by
Markus Weber (Swiss Federal Office of Public
Health, BAG) is highly appreciated. Three
anonymous referees are acknowledged for their
thorough comments on an earlier draft. The
contents are the sole responsibility of the author
and do not represent the views of the Com-
monwealth Fund or local agencies of the par-
ticipating countries.
Competing interests
None.
Funding
The author obtained no funding for this partic-
ular research. Core funding for the �Common- wealth Fund�s 2010 lnternational Survey of the General Public�s Views of their Health Care System�s Performance in Eleven Countries� was by the Commonwealth Fund with co-funding
from the following organizations: the Australian
Commission on Safety and Quality in Health
Risk factors for patient-reported medical errors, D L B Schwappach
� 2012 John Wiley & Sons Ltd Health Expectations, 17, pp.321–331
329
Care; the Ontario Health Quality Council; the
Health Council of Canada; the Quebec Health
Commission; La Haute Autorité de Santé; the
Caisse Nationale d�Assurance Maladie Ces Travailleurs Salaries; the German lnstitute for
Quality and Efficiency in Health Care; the Dutch
Ministry of Health, Welfare and Sport; the Sci-
entific lnstitute for Quality of Healthcare, Rad-
boud University Nijmegen; the Norwegian
Knowledge Centre for the Health Services; the
Health Foundation; the Swedish Ministry of
Health and Social Affairs; the Swiss Federal
Office of Public Health.
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