Quality assurance in psychiatry: quality indicators and guideline implementation.

Abstract

In many occasions, routine mental health care does not correspond to the standards that the medical profession itself puts forward. Hope exists to improve the outcome of severe mental illness by improving the quality of mental health care and by implementing evidence-based consensus guidelines. Adherence to guideline recommendations should reduce costly complications and unnecessary procedures. To measure the quality of mental health care and disease outcome reliably and validly, quality indicators have to be available. These indicators of process and outcome quality should be easily measurable with routine data, should have a strong evidence base, and should be able to describe quality aspects across all sectors over the whole disease course. Measurement-based quality improvement will not be successful when it results in overwhelming documentation reducing the time for clinicians for active treatment interventions. To overcome difficulties in the implementation guidelines and to reduce guideline non-adherence, guideline implementation and quality assurance should be embedded in a complex programme consisting of multifaceted interventions using specific psychological methods for implementation, consultation by experts, and reimbursement of documentation efforts. There are a number of challenges to select appropriate quality indicators in order to allow a fair comparison across different approaches of care. Carefully used, the use of quality indicators and improved guideline adherence can address suboptimal clinical outcomes, reduce practice variations, and narrow the gap between optimal and routine care.

Full text

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Quality assurance in psychiatry: quality indicators
and guideline implementation
T. Wobrock•S. Weinmann•P. Falkai•
W. Gaebel
? Springer-Verlag 2009
Abstract
does not correspond to the standards that the medical
profession itself puts forward. Hope exists to improve the
outcome of severe mental illness by improving the quality
of mental health care and by implementing evidence-based
consensus guidelines. Adherence to guideline recommen-
dations should reduce costly complications and unneces-
sary procedures. To measure the quality of mental health
care and disease outcome reliably and validly, quality
indicators have to be available. These indicators of process
and outcome quality should be easily measurable with
routine data, should have a strong evidence base, and
should be able to describe quality aspects across all sectors
over the whole disease course. Measurement-based quality
improvement will not be successful when it results in
overwhelming documentation reducing the time for clini-
cians for active treatment interventions. To overcome dif-
ficulties in the implementation guidelines and to reduce
guideline non-adherence, guideline implementation and
In many occasions, routine mental health care
quality assurance should be embedded in a complex pro-
gramme consisting of multifaceted interventions using
specific psychological methods for implementation, con-
sultation by experts, and reimbursement of documentation
efforts. There are a number of challenges to select appro-
priate quality indicators in order to allow a fair comparison
across different approaches of care. Carefully used, the use
of quality indicators and improved guideline adherence can
address suboptimal clinical outcomes, reduce practice
variations, and narrow the gap between optimal and routine
care.
Keywords
Quality management ? Provider performance ?
Patient outcome
Quality assurance ? Treatment guidelines ?
Introduction
A growing body of evidence suggests that routine mental
health care varies strongly between different regions and
providers, and that in many occasions, it does not corre-
spond to the standards that the medical profession itself
puts forward [12, 25, 29]. Besides existing gaps between
clinical practice and guideline recommendations, improv-
ing quality of care presents itself as an avenue to restrain
the growth of medical expenditures by reducing costly
complications and unnecessary procedures. These eco-
nomic forces increase the desire for information evaluating
the health benefits of investments in mental health care. In
other words, better organisation and management of med-
ical care would allow countries to spend their health budget
more prudently. To improve care for their citizens and to
realise these potential efficiency gains, policymakers are
looking for methods to measure and benchmark the
T. Wobrock and S. Weinmann contributed equally to this paper.
T. Wobrock (&) ? P. Falkai
Department of Psychiatry and Psychotherapy,
Georg-August-University Go ¨ttingen, Von-Siebold-Straße 5,
37075 Go ¨ttingen, Germany
e-mail: twobroc@gwdg.de
S. Weinmann
Institute for Social Medicine, Epidemiology,
and Health Economics, Charite ´ University Medicine Berlin,
Luisenstr. 57, 10117 Berlin, Germany
W. Gaebel
Department of Psychiatry and Psychotherapy,
Heinrich Heine-University Du ¨sseldorf,
LVR-Klinikum Du ¨sseldorf Bergische Landstr. 2,
40629 Du ¨sseldorf, Germany
123
Eur Arch Psychiatry Clin Neurosci (2009) 259 (Suppl 2):S219–S226
DOI 10.1007/s00406-009-0072-7

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performance of their health care systems as a precondition
for evidence-based health policy reforms. Five mental
disorders are among the ten leading causes of disability:
depression, bipolar disorder, schizophrenia, obsessive-
compulsive disorder, and alcohol abuse [33]. The direct
and indirect costs for societies are high. For example, about
5–6% of National Health Service inpatient costs in England
have been estimated to be attributable to schizophrenia
[24]. Efforts to reduce these enormous costs and the burden
for patients and relatives are urgently needed.
With the development of treatment guidelines, there is
growing hope that the quality of care will improve by
diminishing inadequate care and increasing evidence-
based practices. In mental health care, guidelines are
intended for use by all physicians investigating, diag-
nosing and treating patients with mental illness, espe-
cially those with severe mental illness and a supposed
unfavourable natural disease course. From the beginning,
these practice guidelines have been used as standards
against which routine care has been compared. Thus,
they also served as tools to detect practice variations
across settings and across geographical areas, and to
evaluate over- and underuse of services and interventions
[25]. The reasons for guideline non-adherence and for
the gaps in quality detected by a variety of studies are
complex and include areas that are only partly or com-
pletely out of the control of physicians and other
healthcare providers. However, the methods for mental
health quality assessment and improvement have been
refined in the last years supported by increasing degrees
of computerisation. Although there seem to exist some
structural measures of health care that have been shown
to influence patient outcomes with sufficient reliability
and validity [31], a paradigm shift has begun from
developing and implementing measures of structural and
process quality towards outcome quality. However, the
challenging problem how to assess treatment quality now
needs to be addressed in order to decide what type of
measure will be best used for which specific purpose.
To document the disease course, treatment effects and
provider performance, many measures have been devel-
oped. Physician organisations, healthcare agencies, gov-
ernments and other payers, consumers and researchers have
created and implemented process measures that are typi-
cally rate-based and indicate the percentage of persons
among the eligible population receiving adequate care.
These quality indicators are being increasingly used in all
fields of medicine including psychiatry [21]. Efforts to
improve the quality of medical care must be measured with
simple and reliable criteria. Our article summarises the
efforts and problems to develop quality indicators assessing
the treatment of severe mental illness, and critically dis-
cusses whether increased measure performance and
guideline implementation is likely to lead to better treat-
ment quality and disease outcome.
Development of quality indicators in mental health
In general, quality is a theoretical construct. Psychiatric
interventions take place in complex bio-psycho-social
interactions and settings that are not very well understood
[10]. Various efforts have been undertaken to find valid
parameters of the quality of care including the structural
attributes of the settings in which care occurs, the processes
of care, and the outcomes of care [8]. Before assessing
quality, one has to decide how quality of care should be
defined and this depends on whether one assesses only
provider performance or also the contributions of patients
and of the health care system. Quality assessment may
differ according to how broadly health and responsibility
for health are defined; whether the maximum effective or
average care is sought; and whether individual or social
preferences define the optimum [8].
Today, psychiatric hospitals are, for example, often
required to report their performance on standardised core
measures, and to conduct both internal measurement-based
as well as external quality improvement activities. Public
and private health care providers, consumers and accredi-
tors may use the results several ways to encourage hospi-
tals to improve their quality. In many countries, hospitals
are provided with feedback systems comparing their per-
formance with peer organisations, disclosing results pub-
licly in an effort to influence purchaser or consumer
decisions, or linking financial incentives to improved per-
formance [14]. Their criteria, however, may vary according
to the domains of care regarded as important, the available
data sources, and the basis of comparison for the deter-
mination of quality.
To increase comparability, the members of the OECD
mental health care panel, consisting of international experts
in mental health care, suggested a quality indicator set
covering the most relevant domains of mental health care
(primary care) by selecting four key aspects (http://www.
oecd.org/els/health/technicalpapers): treatment, continuity
of care, coordination of care, and patient outcomes. Based
on a framework outlined by Hermann et al. [20, 21], the
panel decided that the indicators should meet the following
screening criteria: the indicator measures the technical
quality provided, not interpersonal or consumer perspec-
tives; the indicator is focused on quality of care, not on cost
or health care utilisation; the indicator is built on a single
item, not on a multi-item scale; and the indicator is likely
to be useful in quality assessment at the health care system
level, rather than the provider level. In addition, Hermann
et al. argued that an indicator should likely be constructed
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from administrative data using uniform coding systems
(e.g., ICD or DSM codes), rather than requiring dedicated
data collection or non-standardised data elements. The
importance of a selected indicator was assumed to be made
up of three different dimensions: the impact on health
addressing the areas in which there is a clear gap between
the actual and potential levels of health, the policy
importance, and the susceptibility to be influenced by the
health care system. For the latter, the question has to be
addressed whether changes in the indicator will give
information about the likely success or failure of policy
changes.
Selection of quality indicators
The scientific soundness of the indicators can be broken
down into two dimensions: face validity and content
validity [20]. The face validity indicates if the measure is
meaningful in a logical and clinical sense. The face validity
of an indicator should be based on its basic clinical ratio-
nale, and on its past usage in national or other quality
reporting activities. The content validity of an indicator
addresses if the measure captures meaningful aspects of the
quality of care. In addition, the feasibility of an indicator
reflects the question of data availability and the burden of
reporting [20]. This last dimension should in particular
address the issue whether the value of the information
contained in an indicator outweighs the costs of data col-
lection and reporting. For mental healthcare, some of the
measure attributes conflict with each other. Indicators with
the best measurement properties such as utilisation data
may not represent the breadth and diversity of the mental
health care system in terms of processes, modalities, set-
tings and interventions. For example, whereas psycho-
pharmacological interventions have the broadest evidence
base, other areas based on less rigorous studies may be
similarly relevant for the long-term course of psychiatric
disorders.
Specifications of quality indicators often require the use
of approximations, and some measures are a proxy for a
broader concept. For example, using hospital re-admission
rates as a proxy for the quality of discharge planning
assumes that hospital admissions are an unintended out-
come. This builds on the ethics of a mental health care
system that offers the least restrictive care which is effec-
tive. However, there may be research studies showing
significant relations between re-admission rates and other
measures of quality [1, 45]. Targeted short-term hospital
readmissions may even be an indicator of good quality
avoiding long-term hospitalisations. Thus, readmissions as
quality indicators need to be evaluated in a more-differ-
entiated manner. One of the indicators for provider
performance selected by The Mental Health Panel for
primary care is the timely ambulatory follow-up after
mental health hospitalisation, because the continuity of
care was seen as an important aspect of quality in mental
health [20]. This indicator can be measured by the number
of persons hospitalised due to a primary mental health
diagnosis with an ambulatory mental health encounter or
with a mental health practitioner within (a) 7 days, or (b)
30 days of hospital discharge. The importance of the
indicator is outlined by the fact that most patients with a
psychiatric disorder treated in an inpatient setting require
follow-up ambulatory care to promote further recovery and
preventrelapse.Scheduling
proximally to discharge is generally recommended to
address side effects that can result from inpatient medica-
tion changes, and to support compliance with the treatment
plan. Data indicate that there is wide variability in the
duration between hospital discharge and the first ambula-
tory follow-up visit, some of which is related to patient
factors (e.g., severity of illness) and some to system factors
(e.g., availability of outpatient appointments). Shorter gaps
between discharge and aftercare may contribute to greater
continuity of care and a decreased risk of relapse, although
research evidence on this question is mixed [15]. More
continuity of care may be achieved by improvements in
discharge planning interventions that have been shown to
be effective in reducing rehospitalisation and in improving
adherence to aftercare [39], making this indicator poten-
tially useful in assessing the quality of an integrated care
delivery system [37].
A further quality indicator is the hospital readmission
rate for psychiatric patients measured by the quota of the
total number of readmissions to psychiatric inpatient care
that occurred within (a) 7 days or (b) 30 days, divided by
the total number of discharges from psychiatric inpatient
care during a 12-month reporting period. Hospital read-
mission rates are widely used as proxies for relapse or
complications following an inpatient stay for psychiatric
and substance use disorders [20]. Since they indicate pre-
mature discharge or lack of coordination with outpatient
care, high readmission rates have led some inpatient
facilities to examine factors associated with readmissions,
including patient characteristics, length of stay, discharge
planning, and links with outpatient care [27]. Given the
high cost of institutional care, reducing readmission rates
can have a substantial effect on mental health spending.
However, the relation between readmission rates and other
quality criteria are far from consistent. One study showed
no association between readmission rates and clinical
measures of treatment quality [27] suggesting that it is not
the success of the hospital intervention per se which
influences the likelihood of readmission. This measure is
therefore not a useful measure for one institution but rather
outpatient appointments
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for a whole mental health care delivery system, and indi-
cates the quality of post-discharge outpatient treatment as
well as the quality of inpatient treatment and information
management between mental healthcare sectors.
A further process measure proposed for provider per-
formance measurement is the rate of persons with a mental
illness receiving continuous medication treatment in the
maintenance phase for disorders like major depression. In
this case, the indicator may be specified as the number of
persons treated with anti-depressant medication for a per-
iod of at least 180 days divided by the number of persons
who are diagnosed with a new episode of depression.
Depressive disorders can impair personal, social and family
functioning, decrease work productivity, and increase the
risk of suicide. They are highly prevalent and very dis-
abling. The World Health Organisation has estimated that
by the year 2020, major depression will be the second
leading disorder in terms of the global burden of disease
[33]. Studies have consistently demonstrated that compared
with their non-depressed counterparts, individuals with
depression experienced impaired physical and role func-
tioning, more workdays lost, and decreased productivity.
Many studies show heavy utilisation of health services,
with hospitalisations accounting for a high proportion of
costs. A range of antidepressant medications have been
shown to be effective in ameliorating symptoms, and in
improving quality of life and social functioning. However,
adherence remains a problem when the choice for a med-
ication trial has been taken with patients who discontinue
their antidepressant early having a high risk to experience
relapse or recurrence [30]. Randomised clinical trials have
provided evidence that antidepressants need to be contin-
ued for 4–9 months after initiation to minimise the likeli-
hood of relapse. The health system has considerable
influence on this indicator of medication adherence with
clinicians playing an important role in influencing patient
attitudes to treatment by providing education, addressing
concerns, and evaluating and treating side effects [6, 36].
The indicator has good face and content validity. It
assesses the effectiveness of clinical management in
achieving medication adherence as the basis of the effec-
tiveness of an established dosage regimen by determining
the percentage of adults who complete a period of con-
tinuation phase treatment adequate for defining a recovery
according to the US Agency for Health Care Research and
Quality (AHRQ) criteria. Adherence indicators can be
constructed from pharmacy data, which may be easily
useful to identify patients who need assistance with med-
ication adherence [43]. At the same time, there is no con-
sistentevidencethat adherence
medication dosages and other guideline recommendations
are sufficient to improve patient outcomes. In one study,
the authors found no differences in mean endpoint
to antidepressant
depression scores between a depression guideline inter-
vention group and the control group, so that depression
scores were only marginally better in the intervention
group [44]. Other studies, however, showed considerable
advantages of guideline-based intervention focusing on
structured medication treatment and adherence compared
to a group with treatment as usual in self-reported and
physician-assessed depression scores after 12 months [41].
A possible outcome indicator on the population level
in primary care could be the mortality of persons with
severe psychiatric disorders specified as the standardised
mortality rate for persons with particular psychiatric
disorders.Individuals with
severe mental illnesses have higher age- and sex-adjusted
mortality rates than members of the general population
[35]. Studies in some countries have found medical
conditions and co-morbidities to be under-detected and
under-treated in individuals with psychiatric disorders
[11]. Such relative mortality rates, which are frequently
used in cancer epidemiology studies, are well-accepted
and plausible measures to indicate and evaluate the
excess mortality in subgroups with certain diseases. They
may also provide an estimate of the impact on longevity
of these diseases. As there is no a priori biological
reason why patients with mental health disorders should
die prematurely, a large survival difference between
different regions and different mental health care systems
could point to shortfalls in the overall quality of medical
care, not just mental health care, for this especially
vulnerable group of patients. This may also provide a
starting point for further investigation.
Aggregate-level measures such as mortality or suicide
rates are reliant on external and aggregate sources of
data for interpreting results. Inferences can only be made
for a whole population and not for the individual patient.
When there is no intrinsic standard, a process or out-
come measure may be used to identify problems or
outliers in the performance of a mental health care
system, although such a measure cannot determine the
appropriateness of single interventions. Measure perfor-
mance is heavily influenced by patient characteristics, the
case-mix. For example, when hospitals are to be com-
pared, statistical case-mix adjustment is needed to
remove the influence of patient characteristics on the
results in order to avoid unfair comparisons [23]. In
other areas, adjustment of process measures is not
required such as suicidality assessment in depression.
schizophreniaand other
Data sources for measurement
Quality measurement is limited by the availability of data.
To assess patient outcome, documentation systems for
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primary care as well as documentation systems for sec-
ondary mental health care may be used. This assessment
should include instruments to record the first consultation/
admission, weekly consultations, and discharge [18]. For
specialised care, these could assess sociodemographic
data, diagnosis, disease-specific history, treatment course,
and outcome. For major depressive disorder, such out-
come assessments have been suggested to include the
Clinical Global Impressions (CGI) and the Global
Assessment of Functioning Scale (GAF), the rating of
depression scores by the self-rating Patient Health Ques-
tionnaire (brief PHQ-D), and the Beck Depression
Inventory (BDI) as well as expert ratings using, i.e., the
Hamilton Depression Scale (HAM-D). General aspects of
patients’ satisfaction could be measured by the Client
Satisfaction Questionnaire [18]. However, a major prob-
lem has been identified in routine assessment of patient
outcomes. A therapist who is responsible for delivery of a
clinical intervention, and thus has a stake in the outcome
of the intervention, is not in a position to objectively
assess the outcome. Incentives will probably affect rating
behaviour most strongly when the individual provider’s
job performance is directly assessed [4]. Thus, a rationale
approach for quality measurement may combine the
assessment of ‘‘hard’’ outcome parameters such as mor-
tality, job integration, and long-term disease course
including relapse and remission rates combined with an
evaluation of process measures and treatment adherence.
Data suggest that the assessment of treatment adherence
might have a more positive effect on clinical practice than
routine outcome measurements [13].
A cautionary note appears also necessary for payment
for performance schemes in psychiatry using indicator
systems. Research indicates that clinicians are influenced
by the implications of a new quality and outcomes
framework indicator when recording diagnoses, disease
severity, or treatment processes [9]. Non-incentivised
activities and patients’ concerns may receive less clinical
attention [28]. Thus, pay-for-performance or prospective
payment schemes based on quality indicator performance
may hold their promise to improve outcome quality only
when such measures are valid and feasible, adopt a lon-
gitudinal perspective on quality management, and are
successful at ensuring that all providers who are respon-
sible for a particular patient’s care are held accountable for
the quality of care they provide [5].
Quality management measures are necessary to analyse
weak points in routine care, detect opportunities for
improvement of care, and check the implementation of
guidelines. Because some studies show that adherence to
evidence-based guidelines in psychiatry can improve out-
comes [32], whereas others have failed to do so [3], we
further discuss this point in the following section.
Improving quality of care by guideline implementation
Throughout the 1990s, educational initiatives for imple-
menting guidelines were begun, and evidence-based
guidelines have been formulated in many countries. Sev-
eral countries have initiated national clinical guideline
programmes [16, 18, 36]. There is an ongoing debate
whether evidence-based guidelines improve patient out-
comes. Clinical guidelines aim to improve quality of care
by advocating best-practice models and reducing treatment
variation [22]. Some authors have even claimed that the
term evidence-based guideline should only be used if a
positive impact of guideline implementation on patient
outcomes following guideline implementation has been
shown [26, 42]. However, there is little evidence that
guideline dissemination alone affects the behaviour of
mental health clinicians or general practitioners. Guideline
implementation programmes using complicated and mul-
tifaceted procedures or participatory approaches appear to
have an impact on professional behaviour [7].
In a systematic review of psychiatric guideline imple-
mentation studies, the observed effects on provider per-
formance or patient outcome after implementation were
moderate and temporary in most cases. The studies with
positive outcomes used complex multifaceted interventions
or specific psychological methods to implement guidelines
[42]. Interventions associated with better provider perfor-
mance were multifaceted interventions with ongoing expert
consultation, ongoing supervision, or interventions using
marketing techniques and psychological theories to over-
come guideline implementation obstacles. Only one study
using an intensive and costly intervention strategy showed
a consistent positive and substantial improvement in the
self- and physician-rated psychopathology of depression
[41]. Most studies were statistically underpowered to show
small intervention effects.
There is, however, some evidence from observational
studies that guideline adherence is associated with better
outcomes. In a German multisite hospital study, treatment
processes and patient outcomes were compared across
seven psychiatric hospitals using case-mix adjustment
models [23]. Patient structure and treatment processes
showed a great variability between hospitals with mental
state, chronicity of the disease, and other patient factors
being the strongest predictors of clinical outcome. Bench-
marking hospitals, a poorer average clinical outcome was
associated with lower guideline conformity in a variety of
treatment domains, although it is not clear whether better
guideline adherence in the hospitals with the best results
was a causal factor for their enhanced performance.
Many quality indicators are derived from clinical prac-
tice guidelines. Evidence-based guidelines incorporate
research evidence and clinical consensus. They provide a
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useful foundation for quality improvement. The paucity of
studies showing positive effects of guideline implementa-
tion on patient outcome in mental health care should not
discourage quality improvement initiatives based on
guideline recommendations. It should be noted that pro-
grammes, which are directed only towards increasing
guideline adherence, are too simplistic. Measurement-
based quality improvement needs organisational changes
and may only be successful when positive incentives exist
to continually improve treatment quality.
Discussion
Quality indicators are only one method to measure treat-
ment quality [38]. They have the advantage of being
databased and enabling scientific analyses. They can be
used to address suboptimal clinical outcomes, reduce the
variability of care, and close the gaps between evidence-
based guidelines and routine care. However, most indica-
tors in mental health care are not empirically validated
themselves, but are rather based on recommendations for
interventions that have been evaluated in efficacy studies.
Further research may broaden the validation base for single
quality indicators. Due to a variety of national and other
professional efforts, there is an extensive set of indicators
available in mental health care that can be adapted for
multiple purposes [19, 21].
Among the basic setting cornerstones for quality indi-
cators are inadequate variations in routine mental health
care and a high degree of variability in guideline confor-
mance rates. As an example, in severe mental disorders
such as schizophrenia, there has been a trend towards
polypharmacy in routine care not supported by evidence-
based guidelines [2, 17]. Reducing polypharmacy may
reduce complications and side effects of antipsychotic
treatment and therefore improve patient outcome. Anti-
psychotic polypharmacy was suggested as an indicator of
guideline adherence in the audits of the National Institute
of Clinical Excellence [34], specified as the number of
individuals receiving only one antipsychotic at a time.
Based on reviews and meta-analyses, the research evidence
to date is consistent with the goal of avoiding antipsychotic
polypharmacy in patients who lack guideline-recom-
mended indications for its use [14]. Two of the measures
implemented by the Joint Commission on Accreditation of
Healthcare Organisations as a core measure set for Hos-
pital-Based Inpatient Psychiatric Services address anti-
psychotic polypharmacy. The first measure assesses the
overall rate. The second measure determines whether
clinically appropriate justification has been documented
supporting the use of more than one antipsychotic drug
[14]. This seems to be a pragmatically based measure of
provider performance related to patient outcome.
Other quality indicators have to consider the regional
mental healthcare system and need case-mix adjustment to
avoid unfair comparisons. In general, quality indicators
have to be meaningful, feasible and actionable [21], and
address different dimensions of the mental health care
system. Many indicators rely on psychiatric guideline
recommendations. Studies show that there may be a dif-
ferential effect of structured guideline implementation on
the quality of provider performance and patient outcome,
while the underlying causes for these differential effects
are neither obvious nor easily explainable. For example, it
is not clear why the Texas Medication Algorithm Project
(TMAP) intervention resulted in sustained improvement of
patient outcomes in the depression study [41], but not in
the schizophrenia study [32] and not in the mania study
[40]. Based on the results of guideline implementation
studies, three implementation components may be neces-
sary to improve patient outcomes by guideline implemen-
tation and other measurement-based quality improvement
efforts [42]: (1) ongoing support or feedback with an
option to use expert consultation, (2) the use of specific
psychological models to overcome obstacles to guideline
implementation, or (3) social marketing techniques. Rather
than primarily relying on information, education and pro-
motion of better quality of care, multifaceted guideline
interventions should probably be specifically tailored to
raising clinicians’ willingness to change, encouraging
behaviour change through motivational techniques, reduc-
ing barriers through system reconfiguration, ensuring con-
tinued change, and establishing behavioural reinforcers.
Behaviour-driven education appears to be more effective
than strategies driven by knowledge. It should be noted that
measures of rate-based processes and outcomes represent a
subset of a broader range of approaches to quality assess-
ment in mental health care [20].
Conflict of interest statement
speaking engagements from AstraZeneca, Bristol-Myers Squibb,
Janssen-Cilag, Eli Lilly, Novartis, Organon, Pfizer, and Sanofi-Syn-
thelabo/Aventis, and travel or hospitality not related to a speaking
engagement from Astra Zeneca, Eli Lilly, Janssen Cilag, and Sanofi-
Synthelabo, and received a research grant from Astra Zeneca.
Dr. Gaebel has participated in speakers bureaus for AstraZeneca,
Bristol-Myers Squibb, GlaxoSmithKline, Janssen-Cilag, Eli Lilly,
Lundbeck, and Sanofi-Synthelabo/Aventis; has been a member of
advisory boards for Eli Lilly, Lundbeck, Novartis, and Wyeth; and
has received grant/research support from Bristol-Myers Squibb, Eli
Lilly, Wyeth, Janssen-Cilag, and Lundbeck.
Dr. Falkai is a member of the speakers bureaus for AstraZeneca, Eli
Lilly, Janssen-Cilag and Lundbeck, and has accepted paid speaking
engagements in industry-sponsored symposia from AstraZeneca,
Bristol-Myers-Squibb, Eli-Lilly, Janssen-Cilag, Lundbeck and Pfizer,
and travel or hospitality not related to a speaking engagement from
Astra Zeneca, Bristol-Myers-Squibb, Eli Lilly, Janssen Cilag,
Dr. Wobrock has accepted paid
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Lundbeck and Sanofi-Synthelabo, and received a research grant from
Astra Zeneca.
Dr Weinmann reports no financial or other relationships relevant to
the subject of this article.
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