Medical Errors: Root Cause Analysis

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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: [email protected]

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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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

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