· Psychiatric Mental Health Nursing. Scope and Standards of Practi.docx

· Psychiatric Mental Health Nursing. Scope and Standards of
Practice.
Review the Scope and Standards of Practice from APNA
(American Psychiatric Nurses Association). If you are an APNA
member you can access the book free of charge. The link in this
section will link you to the book but you will have to log in. It
is a good idea to join APNA. You can also buy a print copy if
you desire; it is inexpensive. The book is not a required
reading. I have provided the standards here.
The standards are taken directly from APNA Scope and
Standards of Practice 2ndedition (2014).
Assignment for this module:
Take each Standard and give several examples of how you will
follow these standards in your practice. Please, only list just a
few bullet points to address each standard. Ex: Standard 1:
Assessment—what screening tools will you use? Will you meet
with the pt and family together or separate or both? How much
time will you allow for a new patient eval?
As a NP will need to know your scope of practice. You cannot
rely on someone else to know your scope.
Standard 1: Assessment
· Collect and synthesize comprehensive health data that are
pertinent to the healthcare consumer’s health and/or situation.
Standard 2: Diagnosis
· Develop standard psychiatric and substance use diagnoses
Standard 3: Outcomes Identification
· Identify expected outcomes and the healthcare consumer’s
goals for a plan individualized to the healthcare consumer or to
the situation.
Standard 4: Planning
· Develop a plan that prescribes strategies and alternatives to
assist the healthcare consumer in attainment of expected
outcomes.

Standard 5: Implementation
· Implement the identified plan
· Coordinate care delivery
· Employ strategies to promote health and a safe environment
· Provide consultation to influence the identified plan, enhance
the abilities of other clinicians to provide services for the
healthcare consumers, and effect change.
· Use prescriptive authority, procedures, referrals, treatments
and therapies in accordance with state and federal laws and
regulations.
· Incorporate knowledge of pharmacological, biological, and
complementary interventions with applied clinical skills to
restore the healthcare consumer’s health and prevent further
disability
· Provide structures and maintains a safe, therapeutic, recovery-
oriented environment in collaboration with healthcare
consumers, families and other healthcare clinicians.
· Use the therapeutic relationship and counseling interventions
to assist healthcare consumers in their individual recovery
journeys by improving and regaining their previous coping
abilities, fostering mental health, and preventing mental
disorder and disability
· Conducts individual, couples, group, and family
psychotherapy using evidence based psychotherapeutic
frameworks and the nurse-client therapeutic relationship
Standard 6: Evaluation
· Evaluate progress toward attainment of expected outcomes
Standard 7: Ethics
· Integrate ethical provisions in all areas of practice
Standard 8: Education
· Attain knowledge and competence that reflect current nursing
practice.
Standard 9: Evidence-Based Practice and Research
· Integrate evidence and research findings into practice
Standard 10: Quality of Practice
· Systematically enhance the quality and effectiveness of

nursing practice
Standard 11: Communication
· Communicate effectively in a variety of formats in all areas of
practice.
Standard 12: Leadership
· Provide leadership in the professional practice setting and the
profession
Standard 13: Collaboration
· Collaborate with the healthcare consumer, family,
interprofessional health team and others in the conduct of
nursing practice
Standard 14: Professional Practice Evaluation
· Evaluate one’s own practice in relation to the professional
practice standards and guidelines, relevant statutes, rules and
regulations
Standard 15: Resource Utilization
· Consider factors related to safety, effectiveness, cost and
impact on practice in the planning and delivery of nursing
services
Standard 16: Environment Health
· Practice in an environmentally safe and healthy manner
O R I G I N A L P A P E R
What do physicians dislike about managed care?
Evidence from a choice experiment
Maurus Rischatsch • Peter Zweifel
Received: 10 October 2011 / Accepted: 21 May 2012 /
Published online: 21 June 2012
� Springer-Verlag 2012

Abstract Managed care (MC) imposes restrictions on
physician behavior, but also holds promises, especially in
terms of cost savings and improvements in treatment
quality. This contribution reports on private-practice phy-
sicians’ willingness to accept (WTA, compensation asked,
respectively) for several MC features. In 2011, 1,088 Swiss
ambulatory care physicians participated in a discrete choice
experiment, which permits putting WTA values on MC
attributes. With the exception of shared decision making
and up to six quality circle meetings per year, all attributes
are associated with non-zero WTA values. Thus, health
insurers must be able to achieve substantial savings in
order to create sufficient incentives for Swiss physicians to
participate voluntarily in MC plans.
Keywords Managed care � Physician preferences �
Willingness-to-accept values � Discrete choice experiment
JEL Classification C93 � D61 � I11 � J22
Introduction

Many governments try to limit the rise in health care
expenditure by prescribing or encouraging managed care
(MC) programs. Promoting MC is one alternative to tackle
expenditure; the other usually is increased copayments
(Trottmann et al. [40] for a discussion of cost sharing in
deregulated social health insurance). The term MC
encompasses very different institutional arrangements, and
its complexity does not allow one single broadly accepted
definition (see Glied [11]). The present study uses the
expression MC to describe the nature of the contract
between ambulatory care physicians playing the role of
health-care providers and health insurers as payers of care.
In this case, MC contracts are defined by their specific
obligations included in the contract, e.g., mandatory par-
ticipation in quality circles (see ‘‘Study design’’ section). In
mixed systems permitting choice, consumer participation
in MC can be encouraged by lowered contributions to
health insurance (for evidence about the reduction required

to induce voluntary participation by consumers, see e.g.
Zweifel et al. [44]). However, health service providers
must also be won over to MC to avoid quality problems, in
particular due to a lack of participating physicians. For
instance, expansion of MC plans in the US has been
hampered by difficulties in recruiting service providers. In
Germany, the creation of so-called Integrated Care centers
has been slow for the same reason. These difficulties are
compounded in countries with a shortage notably of gen-
eral practitioners (GPs), who play a crucial role in MC as
gatekeepers. In the case of Switzerland, only about 10 % of
medical students intend to become GPs, while retiring GPs
have difficulties finding a successor for their practice [4].
Hence, health-care reforms designed to foster MC need to
address the issue of sufficient attractiveness of MC practice
particularly to general practitioners.
Incentives for providers to participate in MC programs
are mixed. On the one hand, they have to accept limitations

of their professional autonomy, and possibly increased
financial risk (especially if they participate in the financial
success of the scheme). On the other hand, they can benefit
from regular work hours, shared investment costs, and
easier exchange of information within a network. This
article purports to provide information about physicians’
M. Rischatsch (&) � P. Zweifel
Department of Economics, University of Zurich,
Hottingerstrasse 10, 8032 Zurich, Switzerland
e-mail: [email protected]
123
Eur J Health Econ (2013) 14:601–613
DOI 10.1007/s10198-012-0405-8
preferences, expressed as their compensation asked (will-
ingness to accept, WTA) for departing from their con-
ventional job characteristics without MC obligations. The
evidence comes from a stated preference experiment of the
discrete-choice type (DCE), performed with 1,088 Swiss

private-practice physicians working in ambulatory care in
2011. The majority of respondents work in independent
private practice while participating voluntarily in some MC
schemes, which however account for a small share of their
patients (see ‘‘Data’’ section). While evidence based on
actual behavior would be preferable in principle, market
experiments can inform policy makers and health insurers
about the chances of success of planned changes, helping
them avoid costly failures.
This article is organized as follows. The ‘‘Literature
review’’ section contains an overview of the existing lit-
erature on physicians’ preferences, with special reference
to evidence from DCEs. The theoretical background to
understand DCEs and the methods to derive WTA values
are given in the ‘‘Methods’’ section. The ‘‘Study design’’
section outlines the study design and discusses the MC
attributes of interest. The ‘‘Data’’ section describes the
data. The estimation results are discussed in the ‘‘Estima-

tion results’’ section, and conclusions are drawn in the
‘‘Conclusions’’ section.
Literature review
The existing literature on physician behavior mainly
revolves around the impacts of different reimbursement
systems [18, 25, 27]. The precise nature of physician
preferences usually is not addressed because they do not
seem to affect predictions in a substantial way. Some
authors have nevertheless posited particular preferences by
including professional ethics, which in principle should
motivate physicians to hail MC treatment concepts such as
shared decision making (SDM) and critical incident
reporting (CIR) [7, 9, 43]. Attributes of professional
activity originally received little attention, except for the
rate of return associated with specialization [38]. More
recently, Gagne and Leger [10] have examined the choice
of specialty in Canada from 1976 to 1991 in response
to changes in fee-for-service rates. They find income

differences to be a significant factor. However, gender,
mother tongue, medical school attended, state laws, and
geographic conditions have a bearing on the choice of
specialty as well. With the spread of MC, research into the
determinants of choice of type of medical practice received
new impetus. Hypothesized attributes are reputation and
status [8, 29], properties of the medical practice [1], and
intellectual satisfaction [8, 9]. Kristiansen [15] has claimed
professional autonomy to be an additional attribute that
needs to be taken into consideration. However, the rele-
vance of these attributes, especially the non-pecuniary
ones, has been little investigated.
Against the background of undersupply in rural areas of
Norway, Kristiansen [16] analyzed the determinants of the
decision where to locate. Place of birth, place of residency,
and spouse’s place of origin were found to be significant
factors. However, they are not of overriding importance,
causing the author to conclude that the problem of under-

provision could be solved through the use of financial
incentives. In addition, non-pecuniary motives might be
enhanced in order to relieve the public budget, e.g. by
favoring medical students with a rural background (who
are particularly likely to settle there). The same conclusion
is drawn by Benarroch and Hugh [2], who investigate the
migration of physicians in Canada. Urbanization has a
significantly positive effect on migration, whereas distance
between major cities of a province has a significantly
negative effect. While this research is valuable for
informing policy makers about what motivates physicians
to opt for existing alternatives, it is silent about their
choices with regard to alternatives that are being consid-
ered but not available yet. In this situation, surveys and
market experiments can fill the gap.
The effects of non-pecuniary job characteristics on
physicians’ labor supply decisions have mainly been sur-
veyed in the psychological and medical literature [36].

Buddeberg-Fischer and Klaghöfer [3] examine career paths
of 497 last-year medical students over a period of 8 years in
Switzerland. Respondents described versatility of the field
(96 %), intensive patient contact (87 %), positive experi-
ences during their studies (86 %), compatibility of work
with family (83 %), and possibilities of self-employment
(61 %) as determinants of their choice of specialty. In
addition, male students exhibit a preference for specialties
with a scientific orientation, whereas females, for settings
with intensive patient contacts. With regard specifically to
MC alternatives, Nordt [26] find that conflicts due to a
changed perception of the physician’s professional role put
more strain on practitioners in solo than in group practice.
Similarly, incompatibility of work and family may be more
of a problem in solo practice (2.8 out of a maximum of
5 points) than in group practice (2.3 points, difference not
statistically significant).
Market experiments of the discrete-choice type (see

‘‘Study design’’ section below) have been performed by
Scott [35] to investigate the preferences of practitioners in
the UK with regard to working hours, work load, time
spent on administration per week, out-of-hours appoint-
ments, and use of guidelines. Performing a DCE as well,
Ubach et al. [41] report WTA values for an extra working
hour per week and on being on call an extra day per month.
Wordsworth et al. [42] find differences between principal
602 M. Rischatsch, P. Zweifel
123
and so-called sessional GPs.
1
On the whole, the evidence is
in accordance with the theoretical predictions by Marinoso
and Jelovac [22], who compare the performance of gate-
keeping and traditional settings, emphasizing the impor-
tance of non-financial motives for the payment of GPs to
create favorable incentives.

While this research is valuable for pointing to job
attributes that may be particularly valued (or resisted) by
physicians, it fails to inform about their attitudes with
regard to non-marginal changes. However, the transition
from conventional independent private practices to con-
tractual obligations with insurers constitutes such a non-
marginal change. Policy makers considering increasing
the market share of MC through regulation as currently
discussed in Switzerland need to know how much it takes
to win physicians over.
Methods
Based on random utility theory [20, 21, 23, 24], discrete
choice experiments (DCEs) are designed to allow indi-
viduals to express their preferences for non-marketed
goods or goods that do not yet exist. The number of
applications of DCEs to the valuation of health-care pro-
grams has been increasing during the past few years
[13, 33, 34]. For a review of the literature on discrete

choice experiments in health economics, see [5]. In a DCE,
individuals are given a hypothetical choice between many
or just two (binary choice) commodities. From these
choices, the importance (more precisely, the expected
utility) of product characteristics can be inferred. Inclusion
of a cost or price attribute allows determining the valuation
of the remaining product attributes in terms of money. In
the present context, the price attribute is an extra payment
per insured and month. The fact that respondents have to
weigh several attributes simultaneously makes biases that
plague contingent valuation (where individuals are asked
about their willingness to pay directly, holding all other
attributes constant) less likely than in a DCE [32].
The first step of a DCE involves the definition of the
attributes of the commodity and the levels assigned to them
[19, 33]. Here, attributes of MC are chosen to describe the
physicians’ work situation (for more details, see ‘‘Study
design’’). When comparing hypothetical alternative MC

contracts, a rational subject will choose the alternative
with the higher level of utility. The decision-making pro-
cess in a DCE can be seen as a comparison of utilities
Uni ¼ Vni þ eni and Unj ¼ Vnj þ enj; where Vni represents
the deterministic indirect utility of individual n from
alternative i, and eni denotes the pertaining unobserved
error term. Thus, individual n chooses alternative i (MC)
rather than alternative j (conventional practice) if (and
only if) Uni [ Unj, which implies Vni þ eni [ Vnj þ enj so
that Pni ¼ Prðenj � eniVni � Vnj; 8j 6¼ iÞ. Therefore, the
probability of choosing i rather than j implies that the error
term is dominated by the systematic difference in utility.
In this study, physicians’ preferences are estimated with
the aid of a random-coefficient logit model (RCM) esti-
mated by simulated maximum likelihood. The RCM has
three important advantages over the standard logit model.
2
First, it allows for random taste variation across physicians.
Second, the RCM model permits unrestricted substitution
patterns.

3
Third, it allows for correlation of unobserved
factors over time. The choice probabilities for the RCM are
given by
Pni ¼
Z YT
t¼1
eb
0
xnit
PJ
j¼1 e
b0xnjt
fðbjhÞdb; ð1Þ
where the logit probability is called the mixed function and
f(b|h) the mixing distribution with distribution parameters h
(see Train [39], Chap. 6). Subscript n identifies the physi-
cian and i the MC alternative at choice situation t. Prefer-
ence heterogeneity is reflected by the mixing distribution
f(b|h), which is usually assumed to be normal or log-nor-
mal. The log-normal distribution serves to model a strictly
positive or negative preference, e.g., for the price attribute.

However, in practice the log-normal distribution may cause
problems for different reasons (see ‘‘Estimation results’’).
Therefore, applied researchers often keep the price attribute
fixed. The choice of adequate mixing distributions is
important and discussed in the ‘‘Estimation results’’ section.
The mixing distributions reflect unconditioned or pop-
ulation preferences. If no choices were observed, one
would only know that the coefficients follow f(b|h). In
contrast, observed choices allow conditioning the distri-
butions of b on the choices (y), permitting the derivation of
conditional or physician-specific distributions h(b|yn, xn, h)
of b (see Train [39], Chap. 11). By the Bayes theorem,
hðbjyn; xn; hÞ¼
Pðynjb; xnÞ � fðbjhÞR
Pðynjb; xnÞ � fðbjhÞdb
/ Pðynjb; xnÞ � fðbjhÞ; ð2Þ
where the denominator is the normalizing constant.
P(yn|b, xn) is the probability of physician’s observed choice
1
Principal GPs have ownerships in their practice, whereas
sessional

GPs are freelancers (mainly young females with childcare
responsi-
bilities) and employees of NHS boards (Scotland).
2
The RCM (or mixed logit) model is a generalization of the
standard
logit model. The RCM reduces to the standard model if density
f(b) = 1 for b = b and 0 for b = b. Further, the random-intercept
logit model (RIM, also called random-effects model) treats the
constant as normally distributed with all other coefficients kept
fixed.
3
This is irrelevant to this study, which is of the binary choice
type.
What do physicians dislike about managed care? 603
123
sequence yn given b and the attribute levels of the chosen
alternatives xn. Hence, all quantities are known to derive
h(b|yn, xn, h) and to calculate moments of physician-
specific coefficients. Means can be simulated as
weighted averages �b ¼
P

r w
rbr; with wr = P(yn|b
r
, xn)/P
r P(yn|b
r
, xn) where b
r
is a draw from f(b|h).
Study design
In this section, we present attributes related to physicians’
professional activity that distinguish MC from conven-
tional practice. Specifically, we analyze preferences for
different forms of treatment concepts, critical incident
reporting, quality circles, preferred provider lists, and
generic drug lists.
The attribute ‘treatment concepts’ has two levels. First,
shared decision making (SDM) requires that patients are
more strongly involved in the decision-making process
concerning the choice of treatment. SDM is widely applied
in practice (especially encouraged by MC networks) in

Switzerland, at least compared to other countries [6]. It is
recommended in the medical literature as a way to make
the physician a more perfect agent of the patient. An
additional benefit of SDM from the point of view of a risk-
averse physician is to shift the burden of proof in a
malpractice suit to the (now informed) patient; however,
liability suits against physicians are extremely rare in
Switzerland. The downside of SDM is a certain curtailment
of professional autonomy. Therefore, the valuation of SDM
can go either way (see Table 1). The second level is
adherence to treatment guidelines (GL), to be developed by
physicians and accepted by insurers. They define how to
proceed in the case of certain medical interventions.
Guidelines are typical of MC; they are little known in
Switzerland. They entail a strong limitation of professional
autonomy combined with extra administrative work. They
do shift the burden of proof in a malpractice suit to the
insurer or agency (health administration) issuing them.

In view of the very low likelihood of this event, GL is
expected to have a positive WTA (compensation required).
Critical incident reporting (CIR) obliges physicians to
anonymously report critical incidents that happened in their
practice. On the one hand, CIR calls for extra time and
effort, and may give rise to fears of being interpreted as a
confession of malpractice. On the other hand, CIR holds
the promise of quality improvement in the treatment pro-
vided. Hence, the valuation of CIR can go either way (see
Table 1).
The third attribute is the obligation to attend so-called
quality circles (QC), another feature of MC. In QC, phy-
sicians meet on a regular basis to discuss new treatments
and interventions as well as experiences made. This benefit
to participating physicians has to be balanced against the
sacrifice of time. Interviews with physician networks
indicated that many of their members like to participate
in QC provided they take place during lunches and are

accompanied by presentations by fellow members or spe-
cialists. On the whole, no clear prediction about the
expected sign of WTA can be made.
The fourth attribute is the preferred provider list (PPL),
which restricts referrals to specialists and hospitals to
providers selected by the MC organization. This restriction
is expected to be undesired by most physicians. However,
some of them may support PPL because they believe in the
ability of the MC organization to identify providers offer-
ing high quality and/or high cost-efficiency.
Fifth, mandatory prescription of generic drugs if avail-
able (GEN) is imposed by most MC organizations in
Switzerland. Physicians may perceive GEN as a good
instrument for tackling rising drug expenditures; on the
other hand, it does restrict their choice of pharmaceutical
treatment. Therefore, preferences could go either way.
The sixth attribute represents the price attribute in the
DCE. It is measured as a payment (PAY) over and above

current income per MC-insured person per month (IPM).
To be in line with microeconomic theory, all physicians
should positively value PAY.
Table 1 Attributes and attribute levels in the DCE
Attribute Attribute levels
No contractual obligation to adhere to any item below versus
Treatment concepts Shared decision making: yes/no (SDM, ±),
guidelines: yes/no (GL, ±)
Critical incident reporting Mandatory anonymous reporting:
yes/no (CIR, ±)
Quality circles
a
Mandatory meetings per year: 0/3/6/12 (QC, ±)
Preferred provider list Referrals only to listed providers: yes/no
(PPL, ±)
Generic drug list Restricted to prescribe generics if available:
yes/no (GEN, ±)
Payment Payment of CHF 0.00/0.50/1.00/1.50/2.00 per insured
and month (PAY, ?)
a
Quality circles are defined to the last 1.5 h per meeting. The
signs after the abbreviations in parentheses indicate our

expectations about
physician preferences
123
An example of a choice scenario is shown Table 2.
‘Independent without obligations’ defines the status quo of
conventional practice, an option available to all Swiss
physicians. In fact, only 13 % of respondents report to be
in MC practice (see ‘‘Data’’).
In Eq. (3) below, the attribute levels for treatment
concepts (SDM, GL), critical incident reporting (CIR),
preferred provider list (PPL), and generic drug list (GEN)
are coded as dummy variables. Because SDM and GL are
levels of one attribute, they never appear together in an
alternative. Quality circles (QC) have levels of 0, 3, 6, and
12 (meetings per year). Coding them as three categorical
variables (QC3, QC6, and QC12) has the advantage of not

imposing a specific functional form such as the linear or
quadratic. Finally, PAY denotes the payment a physician
receives in return for accepting MC-type obligations,
ranging from zero to CHF 2.00 per insured and month
(IPM). With an enrolment of 600 (say), this maximum
corresponds to about 8 % of the median monthly income
[17]. Therefore, the deterministic part of the random utility
can be written as
b0x ¼ b1SDM þ b2GL þ b3CIR þ b4QC3 þ b5QC6
þ b6QC12 þ b7PPL þ b8GEN þ b9PAY
þ b10CONST; ð3Þ
where the bs are the taste parameters of interest to be
estimated.
The total of six attributes and their levels combine to
form 480 possible combinations of alternative MC con-
tracts. Using JMP to optimize the experimental design, this
number was reduced to 40 D-optimal choice scenarios and
randomly split into four groups, resulting in 10 choice
situations per respondent. Each of the ten hypothetical MC
contracts had to be evaluated against the reference case

with no obligations imposed.
Data
The Swiss Medical Association (FMH) supported carrying
out the discrete choice experiment (DCE) by including a
link to the web-survey in a newsletter addressed to all
members in private practice. In July 2011, a pretest
involved a randomly selected sample of 1,000 FMH
members. Respondents had the opportunity to write com-
ments, which indicated a good understanding of the survey.
Respondents were randomly selected considering eco-
nomic and demographic characteristics to represent the
ambulatory care physician community in Switzerland. The
main survey was fielded in August 2011 with a return rate
of 11 % , resulting in 10,461 observed choices by 1,088
physicians. This rate of response coincided with our
expectations and previous experience with surveys
addressed to physicians. A high share of 87 % completed
all ten choice scenarios, with 9.6 the average number of

choices made per respondent. The share of respondents
always choosing no obligations was 29 %, while 1 % of
physicians agreed to sign up to all MC alternatives pre-
sented. In addition to the DCE, the survey included ques-
tions about general attitudes concerning experience with
MC, education, and other demographic variables.
The statistics compiled in Table 3 indicate that average
age is a high 54 years (the same as the national figure, see
Kraft [14]). With 26 years of experience, participants are
somewhat past their halftime in independent practice on
average. Accounting for 19 % of the sample, women are
underrepresented in the sample compared to their overall
share of 32 % in the medical profession [14]. The fact that
relatively fewer female physicians participated in the study
again coincides with previous survey experience by the
Swiss Medical Association, regardless of topic. About
77 % of sampled physicians are married (5 % are single, 9
divorced) and have on average 1.65 children under

18 years. Some 52 % have their practice in an urban
environment, while 25 % are located in suburban and 23 %
in rural areas, respectively. The majority of respondents are
from the German-speaking northern and eastern parts of
Switzerland (73 %), while 24 % are from the French-
speaking western and the remaining 3 % from the Italian-
speaking southern parts.
Approximately 45 % of sampled physicians are general
practitioners (including gynecologists and pediatricians),
while 13 % are specialists without surgical and 13 % with
surgical activity. Psychiatrists constitute 16 % of the
sample, while the remainder declared themselves to belong
Table 2 Example of choice scenario
Attribute Obligation
You are to base treatment decisions on shared
decision making
Yes
You obligate yourself to anonymously report

critical incidents
Yes
Number of quality circles you agree to attend per
year
6 (1.5 h each)
You accept a preferred provider list for referrals Yes
You prescribe exclusively generics if available No
You receive payment of CHF 1.50/IPM
a
I am willing to sign the MC contract with these
obligations
h
I would like to remain independent without
obligations
h
a
Payment is in CHF per insured per month (IPM). 1 CHF &
1.1 USD at 2011 exchange rates

123
to other groups or failed to state their specialty. Most
physicians work in single practice on their own account
(51 %), which means that their income or profit depends on
their own services. Approximately a third (30 %, not
shown in Table 3) work in shared or group practices but
bill their services individually. This is in contrast to shared
practices with a common account, which bill regardless of
who in the practice actually provided the services. These
shared practices with so-called common accounting are
rare (5 %). The MC setting is predominantly characterized
by networks where members continue to work on their own
account (12 % of respondents); common-account networks
are the exception (1 %). Among physicians in shared
practice, 61 % work in a team of two, 24 % in a team of
three, and 8 % in a team of four physicians. Maximum
team size reported is a low nine physicians.

In the attitudinal part of the survey, participants were
asked about their experiences with MC. This information is
used in the ‘‘Effects of prior experience’’ section to explore
experience-related differences in WTA values with respect
to MC attributes. Concerning treatment concepts, 57 %
have experience with shared decision making and 51 %
with treatment guidelines. About 27 % of sampled physi-
cians collected experience with critical incident reporting.
Quality circles are the most prominent MC feature, with 60
physicians having attended meetings at least once. As to
the most restrictive MC features, only 14 % stated expe-
rience with preferred provider lists and 27 % with generic
drug lists.
Estimation results
Table 4 shows the estimated distribution parameters for
two different model specifications. Both are estimated by
simulated maximum likelihood using 500 Halton draws
[12]. The left panel of Table 4 pertains to the random-

intercept model (RIM) specification, where all coefficients
are kept fixed with the exception of the constant, for which
a normal distribution is assumed. The constant captures
unobserved physician-specific effects. The right panel
displays the parameters pertaining to the random-coeffi-
cient model (RCM), where all coefficients are assumed to
be normally distributed (reflecting the theoretical expec-
tations listed in Table 1), with the exception of a fixed
coefficient for PAY. Revelt and Train [28] give three
reasons for keeping the price attribute fixed. First, it
facilitates the calculation of population WTA values.
Second, RCM estimates tend to be unstable when all
coefficients are random [31]. Third, the appropriate choice
of mixing distribution for the price attribute is not
straightforward. The most frequently applied log-normal
distribution does often not converge in practice. Further, it
renders estimates of the price coefficient that are very close
to zero, causing implausibly high WTA values [37].

Therefore, the WTA values (see Fig. 1 of the ‘‘Willingness
to accept MC-type obligations’’ section) capture only
preference heterogeneity from the MC attributes but no
heterogeneity with respect to PAY, and hence marginal
utility of income (which may be substantial in view of the
dispersion of medical income documented by Künzi et al.
[17]).
The simulated log-likelihood (SLL) values at conver-
gence are -4,549.7 (RIM) and -4,261.0 (RCM), while the
AICs are 9,121.3 (RIM) and 8,559.9 (RCM), respectively.
Therefore, goodness of fit speaks in favor of RCM esti-
mates, which are emphasized in the discussion below.
Table 4 shows estimated mean and standard deviation
parameters along with their standard errors (SE). The mean
parameters are insignificant for CIR (RIM) and six meet-
ings per year for both specifications. All remaining
parameters are highly significant with a p value below 0.01.
Table 3 Respondent

descriptives, Swiss ambulatory
care physicians (2011)
General practitioners include
gynecologists and pediatricians.
Statistics are mean (MN),
standard deviation (SD), and
median (MD)
Variable MN SD
Percentiles
5th MD 95th
Age of physician 53.73 8.25 40.00 54.00 66.00
Job experience (in years) 26.00 9.74 11.00 27.00 39.00
Male respondents 0.81 – – – –
Married 0.77 – – – –
Number of children under 18 1.65 1.70 0.00 2.00 4.00
Urban practices 0.52 – – – –
Suburban practices 0.24 – – – –
Rural practices 0.23 – – – –

General practitioners 0.45 – – – –
Specialists without surgery 0.13 – – – –
Specialists with surgery 0.13 – – – –
Psychiatrists 0.16 – – – –
123
Share of physicians who dislike MC
The estimated parameters for the population distributions
can be used to calculate population shares of physicians
with negative preferences for MC. For attribute k, this
share is given by Pðbk0Þ¼ Uð�MNk=SDkÞ; where U is
the cumulative normal distribution, and MNk and SDk are
the estimated mean and standard deviation, as given in
Table 4. An alternative approach is to calculate the share of
negative physician-specific coefficients using Eq. (2),
which has the advantage of conditioning on individual
choices observed. Therefore, the conditioned shares are
discussed below, while the unconditioned shares are shown

in parentheses.
Regarding MC-type treatment concepts, only 9 (31) %
of physicians have a distaste for shared decision making,
while no less than 86 (73) % dislike guidelines. Similarly,
the share of physicians opposing critical incident reporting
attains 93 (70) %. Almost all physicians (1 % rejecting)
like to attend three quality circles per year. However,
acceptance already decreases for six meetings per year,
with 38 (45) % against. Finally, a full 92 (79) % dislike to
be obliged to participate in 12 meetings per year. In sum,
about one-half of sampled physicians are willing to
participate in up to six quality circles without being com-
pensated. The MC attribute with the highest share of
opposing physicians is the preferred provider list with 94
(88) %. Restricting drug prescriptions to generics if
available is still refused by 88 (79) %. These findings
suggest that with the exception of shared decision making
and up to six quality circle meetings per year, all MC-type

attributes have to be compensated if a majority of Swiss
physicians are to be won over to MC.
Willingness to accept MC-type obligations
Next, we focus on the physician-specific willingness-to-
accept (WTA) values for MC attributes, shown in Table 5.
The discussion concentrates on the median values from the
RCM because they are more robust to outliers than the
mean values.
The negative WTA value for SDM indicates that the
median Swiss physician need not to be compensated for
involving patients in the decision making about choice of
treatment. In contrast, following guidelines has to be
compensated with about 3.57 CHF per MC-insured per
month (CHF/IPM). Critical incident reporting was shown
to have a small, insignificant effect on the choice prob-
abilities (Table 4). This is reflected by a WTA value of
only 0.34 CHF/IPM; this low value likely reflects physi-
cians’ belief that CIR contributes to an increase in treat-

ment quality. Quality circles are positively valued up to
six meetings per year by the median respondent. Hence,
including up to six quality circles in a MC contract allows
Table 4 Preferences for
managed care attributes—
regression results
a
Number of physicians: 1,088;
number of choices observed:
10,461. Coefficients for RCM
are all assumed to be normally
distributed, with the exception
of a fixed coefficient for PAY
Attribute Parameter
Random- intercept model
(RIM)
Random-coefficient model
(RCM)
Value SE Value S.E.

Shared decision making (SDM) Mean 0.38 (0.07) 0.48 (0.09)
SD 0.95 (0.16)
Guidelines (GL) Mean -0.66 (0.09) -1.49 (0.19)
SD 2.43 (0.26)
Critical incident reporting (CIR) Mean -0.06 (0.06) -0.16 (0.09)
SD 0.31 (0.16)
Three quality circles (QC3) Mean 0.33 (0.09) 0.30 (0.11)
SD 0.10 (0.22)
Six quality circles (QC6) Mean 0.04 (0.09) 0.10 (0.11)
SD 0.79 (0.18)
Twelve quality circles (QC12) Mean -0.91 (0.10) -1.66 (0.18)
SD 2.09 (0.20)
Preferred provider list (PPL) Mean -1.42 (0.07) -2.28 (0.13)
SD 1.95 (0.14)
Generic drug list (GEN) Mean -0.89 (0.07) -1.66 (0.12)
SD 2.09 (0.15)
Payment (PAY)
a

Mean 0.37 (0.05) 0.49 (0.06)
SD
Constant (CONST) Mean -0.73 (0.13) -0.64 (0.16)
SD 1.79 (0.07) 1.86 (0.11)
123
reducing the overall compensation required. Nevertheless,
this reduction is too low to play a crucial role in attracting
physicians to participate in MC. In addition, 12 meetings
already have to be compensated at the tune of 3.71 CHF/
IPM. Restricting referrals to providers listed by insurers
is strongly opposed and requires the highest compensa-
tion of all MC-type attributes. Its median WTA is 5.27
CHF/IPM. The next-highest WTA value pertains to the
restriction to prescribe only generics if available (GEN),
with 4.06 CHF/IPM. A likely reason for this high figure is
the fact that about one-half of Swiss physicians live in

jurisdictions permitting them to dispense drugs on their
own account [30]. Therefore, the GEN attribute entails the
loss of an option to generate extra income for many
respondents.
In view of the entries of Table 5, the question arises of
whether current extra payments by insurers suffice to win
physicians over to MC. A typical value is 1.50 CHF/IPM
for participating in a health maintenance organization
(HMO), the most restrictive MC variant (preferred provider
organizations and gatekeeping networks also exist in
Switzerland). Clearly, this extra payment falls far short of
what it takes to make the median Swiss physician join an
HMO. To the extent that it reflects achievable cost savings
due to MC, these savings could easily be insufficient for
MC to increase its current market share.
Because the coefficient of PAY is kept fixed, the WTA
values have the same distributions as the random coeffi-
cients for the MC attributes. The histograms of Fig. 1 point

to substantial heterogeneity of preferences, especially with
0
.2
.4
.6
.8
−4 −3 −2 −1 0 1
SDM
0
.1
.2
.3
.4
−5 0 5 10
GL
0
1
2
3
−.5 0 .5 1
CIR
0

.2
.4
.6
.8
−2 −1 0 1 2
QC=6
0
.1
.2
.3
−5 0 5 10
PPL
0
.0
5
.1
.1
5
.2
−5 0 5 10
GEN

Fig. 1 Histograms of
physician-specific WTA values
Table 5 Willingness to accept
MC-type obligations
WTA values (mean denoted by
MN and median by MD) are
shown in CHF per insured and
month (CHF/IPM) using
physician-specific coefficients;
1 CHF & 1.1 USD at 2011
exchange rates
Attribute Abbrev. RIM RCM
MN MD Percentiles
MN 5th 95th
Shared decision making SDM -1.03 -1.00 -0.86 -2.56 0.31
Guidelines GL 1.80 3.05 3.57 -2.31 6.53
Critical incident reporting CIR 0.17 0.33 0.34 -0.10 0.66
Three quality circles QC3 -0.89 -0.61 -0.61 -0.73 -0.50
Six quality circles QC6 -0.11 -0.21 -0.17 -1.53 0.90
Twelve quality circles QC12 2.46 3.46 3.71 -0.83 6.67

Preferred provider list PPL 3.87 4.70 5.27 -0.18 8.15
Generic drug list GEN 2.43 3.53 4.06 -1.54 7.40
123
regard to GL, PPL, and GEN. Opinions appear to be
strongly divided concerning GL and GEN in particular,
where bi-modality is evident. In the case of GEN, this
likely reflects the divide between physicians who dispense
drugs on their own account and those who do not.
Effects of prior experience
The preference patterns and WTA values found in the
previous section do not distinguish between different
groups of physicians. This section is devoted to the ques-
tion of whether prior experience with a MC setting makes a
difference; differences between general practitioners and
specialists are discussed in the next section.
To test for differences between physicians with and

without MC experience, all attributes are interacted with a
dummy indicating whether respondents declared have
made experience with this specific MC attribute. Table 8 of
the ‘‘Appendix’’ (left-hand side) shows the estimated dis-
tribution parameters for the RCM containing this type of
interaction. The physician-specific WTA values estimated
for physicians with and without experience with the per-
tinent MC attribute are displayed in Table 6. In general,
physicians with experience have lower WTA values,
indicating less resistance against or even a preference for
the MC feature. There are two reasons for this effect. First,
physicians may like MC because of their favorable expe-
rience. Second, however, self-selection may be at work.
Physicians with a preference for MC are likely to have
selected this setting, causing them to have prior MC
experience. As will be argued below, disentangling the two
directions of causality is not worthwhile in the present
policy context.

The discussion is limited to the most salient differences.
They concern SDM, PPL, and GEN. First, physicians
stating that they have never had experience with SDM
dislike involving patients in the decision-making process.
They ask for a median compensation of 0.72 CHF/IPM for
SDM. In contrast, physicians with experience in SDM have
a positive preference for it and do not have to be com-
pensated. Second, physicians who have worked with a
preferred provider list (PPL) exhibit a median WTA value
of 2.98 CHF/IPM, less than one-half of that characterizing
their colleagues without that experience (6.30 CHF/IPM).
Third, restricting drug prescription to generics has a med-
ian WTA of 3.72 CHF/IPM among physicians who have
applied such a list, compared to 5.52 CHF/IPM for those
who have not.
While it would be of scientific interest to distinguish the
effect of prior experience from a possible self-selection
effect, for policy makers attempting to increase the market

share of MC, this is a moot point. They need to win over
physicians without prior MC experience. This means that
the achievable cost savings must suffice to finance the
higher compensations requested by this group—letting
alone the compensation asked by Swiss consumers as
estimated by another DCE [44].
Differences between GPs and specialists
In the survey, physicians were asked to state if they are
general practitioners (GPs, including gynecologists and
pediatricians), specialists with and without surgical activ-
ities, or psychiatrists. Because GPs play a crucial role in
MC as gatekeepers for their patients, this section compares
their preferences with those of their specialized colleagues
who are grouped together as ‘specialists.’ The same RCM
is estimated as in the ‘‘Estimation results’’ section, but this
time with MC attributes that interacted with a dummy
variable, indicating whether the respondent is a specialist
Table 6 Willingness-to-accept values by experience

Attribute Physicians without experience Physicians with
experience
MN MD Percentiles MN MD Percentiles
5th 95th 5th 95th
Shared decision making 0.52 0.72 -1.40 2.25 -2.28 -2.09 -4.44 -
0.40
Guidelines 3.80 3.85 1.46 5.55 0.89 1.22 -2.55 3.19
Critical incident reporting 0.72 0.75 0.13 1.20 -0.35 -0.34 -1.54
0.90
Three quality circles 0.37 0.37 0.30 0.44 -1.32 -1.32 -1.59 -1.04
Six quality circles 1.59 1.59 1.54 1.62 -1.39 -1.39 -1.44 -1.33
Twelve quality circles 5.08 5.18 1.95 7.45 2.95 3.05 -1.41 6.57
Preferred provider list 5.61 6.30 -0.37 9.38 2.78 2.98 -2.94 7.46
Generic drug list 4.64 5.52 -2.18 9.15 3.48 3.72 -2.24 8.69
WTA values are shown in CHF per insured per month, IPM
using physician-specific WTA values from RCM, containing
interactions.
‘Experience’ refers to the particular MC attributes listed
123

or not. In analogy to the previous section, estimated dis-
tribution parameters are relegated to Table 8 of the
‘‘Appendix’’ (right-hand side). Table 7 displays the calcu-
lated physician-specific WTA values.
With regard to most MC-type attributes, WTA values do
not markedly differ between GPs and specialists. There are
two exceptions. One is the preferred provider list (PPL), for
which the median GP would have to be compensated at the
tune of 3.64 CHF/IPM, compared to 6.53 CHF/IPM for the
median specialist, the overall maximum found in this
study. This discrepancy is intuitive for three reasons. First,
a specialist who joins a MC network depends on referrals
from GPs (potentially governed by a PPL) in an even more
decisive way than in conventional practice, whereas
referrals play a minor role in either setting for a GP. Sec-
ond, many specialists serve more than one MC network, in
which case a PPL imposed by one of the networks can hurt

them. By way of contrast, GPs typically work for a single
MC organization; there is no need for them to rely on
demand emanating from other MC organizations. Finally,
specialized physicians may feel that they know better than
GPs which providers to choose for their patients or net-
works. The second discrepancy concerns the generic drug
list (GEN), where GPs have to be compensated with a
median of 3.06 CHF/IPM, but specialists with 4.44 CHF/
IPM. A likely explanation is that specialists are more likely
than GPs to treat rare diseases that might call for a brand-
name drug, which is not listed.
On the whole, general practitioners are found to be
less strongly opposed to attributes of MC. Thus, winning
them over to MC is less costly than estimated in ‘‘Esti-
mation results’’ section based on the whole sample. Still,
a payment of 1.50 CHF/IPM remains insufficient for
attracting a majority of GPs to an MC organization that
imposes guidelines requiring more than six quality circle

meetings per year, a preferred provider list, or a generic
drug list.
Conclusions
Policy makers try to limit increasing health care expendi-
ture by mandating or encouraging Managed Care (MC).
However, attempts to increase the market share of MC
often fail because of a lack of participating physicians. As
long as conventional practice remains an alternative, health
service providers must be won over to MC because they
have to accept limitations of their professional autonomy.
The objective of this contribution is to investigate physi-
cians’ preferences for MC attributes measured as willing-
ness-to-accept (WTA) values. The data come from a
sample of 1,088 Swiss private-practice physicians working
in ambulatory care participating in a discrete choice
experiment (DCE) in 2011.
The MC attributes studied are shared decision-making
and guidelines; reflecting treatment concepts; critical

incident reporting; attending 0, 3, 6, or 12 quality circle
meetings per year, accepting a preferred provider list, and
having drug prescription restricted to generics if available.
To determine the money valuation of MC attributes
expressed as WTA values, a price attribute is included,
defined as a payment per MC-insured per month (IPM) to
compensate the physician for additional cost and effort.
Estimated distribution parameters for the random-coef-
ficient model show that the median Swiss physician likes
shared decision making, three quality circles, and payment;
is indifferent with regard to six quality circles per year; and
dislikes all other MC attributes. The highest share of
opposing physicians is found for the preferred provider list.
All respondents like three quality circles per year. With
respect to strength of opposition, estimated WTA values
reveal that preferred provider and generic drug lists have to
be compensated most, with median WTA ranging from
3.60 CHF/IPM to 5.30 CHF/IPM (1 CHF & 1.1 USD in
2011). These figures exceed the current level of 1.50 CHF/

IPM, which already amounts to 8 % of median physician
Table 7 Willingness-to-accept values, general practitioners vs.
specialists
Attribute General practitioners Specialists
MN MD Percentiles
MN MD
Percentiles
5th 95th 5th 95th
Shared decision making -0.58 -0.55 -1.62 0.46 -1.03 -0.70 -4.37
1.07
Guidelines 3.38 4.26 -3.34 7.63 3.47 4.15 -3.15 7.15
Critical incident report. 0.23 0.24 -0.16 0.59 0.68 0.78 -0.59
1.46
Three quality circles -0.90 -0.90 -0.96 -0.85 -0.13 -0.12 -0.56
0.27
Six quality circles -0.98 -0.98 -1.32 -0.64 0.60 0.60 0.20 0.96
Twelve quality circles 2.50 2.49 -0.12 4.92 3.61 3.72 0.93 5.83
Preferred provider list 3.64 3.64 -0.11 6.91 5.94 6.53 -1.30
11.30
Generic drug list 2.93 3.06 -1.70 6.80 4.15 4.44 -0.84 7.55

WTA values are shown in CHF per insured per month, IPM
using physician-specific WTA values from interacted RCM
123
income. Shared decision making and up to six quality
circles are accepted without compensation.
Clear signs of preference heterogeneity motivate dis-
tinctions between physician groups. For an expansion of
MC, physicians without prior experience with MC-type
attributes need to be attracted. However, some of their WTA
values turn out to be twice as high as those of physicians with
prior experience. Another distinction of importance is
between general practitioners and specialists since some MC
organizations have difficulty offering the full range of spe-
cialties. Indeed, specialists are found to exhibit higher WTA
values than GPs almost without exception; their resistance
against a preferred provider list would have to be overcome
by a payment of 6.53 CHF/IPM, the overall maximum found

in this study. Considering that a current rate for participating
in an HMO is 1.50 CHF/IPM, these findings lead to the
prediction that MC plans designed to achieve cost savings
are unlikely to enlist the majority of Swiss physicians as long
as they retain the option of conventional practice with full
professional autonomy. Realistically, the implementation of
shared decision making, critical incident reporting, and up to
six quality circle meetings per year can be expected. It is
doubtful that future cost savings achievable through treat-
ment guidelines, a preferred provider list, and generic drug
lists are of a magnitude that would permit the current 1.50
CHF/IPM to be doubled or even tripled, reaching compen-
sation amounts that would render MC attractive to the
median physician. Prospects for a voluntary, market-driven
expansion of MC in Switzerland look rather bleak indeed;
quality circles as the one positively valued attribute do not
modify this conclusion.
Acknowledgements The authors would like to express their

thanks
to Dr. med. Jacques de Haller and Dr. med. Ignazio Cassis from
the
Swiss Medical Association (FMH) for making the experiment
for the
present analysis possible. Support by Martina Hersperger and
Esther
Kraft is also gratefully acknowledged. Special thanks go to Dr.
Maria
Trottmann and Dr. Harry Telser for their very helpful
comments.
Appendix
See Table 8.
Table 8 Preferences for
managed care attributes (model
with interactions)
Attribute Parameter Experience Profession
Value SE Value SE
Shared decision making Mean -0.24 (0.14) 0.28 (0.11)
SD 1.14 (0.16) 0.71 (0.16)
SDM interacted Mean 1.32 (0.17) 0.18 (0.18)
SD 0.35 (0.96) 1.30 (0.27)

Guidelines Mean -1.71 (0.22) -1.55 (0.24)
SD 1.30 (0.32) 2.65 (0.26)
GL interacted Mean 1.25 (0.26) -0.07 (0.31)
SD 0.91 (0.38) 0.79 (0.34)
Critical incident reporting Mean -0.34 (0.10) -0.11 (0.11)
SD 0.37 (0.17) 0.34 (0.24)
CIR interacted Mean 0.54 (0.18) -0.22 (0.17)
SD 0.72 (0.28) 0.61 (0.26)
Preferred provider list Mean -2.50 (0.14) -1.62 (0.13)
SD 2.22 (0.18) 1.49 (0.20)
PPL interacted Mean 1.07 (0.31) -0.95 (0.21)
SD 0.36 (0.43) 2.09 (0.22)
Generic drug list Mean -2.10 (0.19) -1.31 (0.14)
SD 2.48 (0.24) 1.71 (0.14)
GEN interacted Mean 0.41 (0.23) -0.56 (0.20)
SD 0.41 (0.29) 0.65 (0.17)
Three quality circles Mean -0.17 (0.16) 0.42 (0.13)
SD 0.04 (0.29) 0.04 (0.20)

QC3 interacted Mean 0.78 (0.18) -0.37 (0.17)
SD 0.15 (0.19) 0.31 (0.29)
Six quality circles Mean -0.73 (0.17) 0.46 (0.14)
SD 0.03 (0.23) 0.23 (0.27)
123
References
1. Beardow, R., Cheung, K., Styles, W.: Factors influencing the
career choices of general practitioner trainees in North West
Thames Regional Health Authority. Br. J. General Pract. 143,
449–452 (1993)
2. Benarroch, M., Hugh, G.: The interprovincial migration of
Canadian physicians: does income matter?. Appl. Econ. 36(20),
2335–2345 (2004)
3. Buddeberg-Fischer, B., Klaghöfer, R.: Geschlecht oder Pers-
önlichkeit? Determinanten der Karrierepläne angehender Ärz-
tinnen und Ärzte. (Gender or personality? Determination of
career plans of future physicians). In: Abele A., Hoff E.-H.,

Hohner H.-U. (eds). Frauen und Männer in akademischen Pro-
fessionen. Berufsverläufe und Berufserfolg (2003)
4. Buddeberg-Fischer, B., Klaghöfer, R., Stamm, M., Marty, F.,
Dreiding, P., Zoller, M., Buddeberg, C.: Primary care in Swit-
zerland—no longer attractive for young physicians? Swiss Med.
Wkly. 136, 416–424 (2006)
5. De Becker-Grob, E.W., Ryan, M., Gerard, K.: Discrete choice
experiments in health economics: a review of the literature.
Health Econ. doi:10.1002/hec.1697 (2010)
6. Deveugele, M., Derese, A., van den Brink-Muinen, Bensing,
J.,
De Maeseneer, J.: Consultation length in general practice: cross
sectional study in six European countries. Bri. Med. J. 325,
472–478 (2002)
7. Dionne, G., Contandriopoulos, A.: Doctors and their
workshops:
a review article. J. Health Econ. 4, 21–33 (1985)
8. Enthoven, A.: Consumer-choice health plan. N. Engl. J. Med.
298(22), 1223–1238 (1978)
9. Feldstein, M.: The rising price of physicians’ services. Rev.
Econ.

Stat. 52, 121–133 (1970)
10. Gagne, R., Leger, P.: Determinants of physicians’ decision
to
specialize. Health Econ. 14(7), 721–735 (2005)
11. Glied, S.: Managed care. In: Culyer, A.J., Bewhouse, J.P.
(eds.)
Handbook of Health Economics, vol. 1, Chap. 13, pp. 707–753.
North-Holland Publishing Company, Amsterdam (2000)
12. Hole, A.: Fitting mixed logit models by using maximum
simu-
lated likelihood. Stata J. 7(3), 388–401 (2007)
13. Hole, A.: Modelling heterogeneity in patients’ preferences
for the
attributes of a general practitioner appointment. J. Health Econ.
27, 1078–1094 (2008)
14. Kraft, E.: 30’273 Ärztinnen und Ärzte für die Schweiz
(30,273
physicians for Switzerland). Schweizer Ärztezeitung 92(12),
440–444 (2010)
15. Kristiansen I.: What is in the doctor’s utility function? A
theo-
retical and empirical investigation into what influences doctors’
decision making. Dissertation, University of Tromso (1994)
16. Kristiansen, I.: Medical specialists’ choice of location: the

role of
geographical attachment in Norway. Soc. Sci. Med. 34(1), 57–
62
(1992)
17. Künzi, K., Strub, S., Stocker, D.: Erhebung der Einkommen-
verhältnisse der berufstätigen Ärtzeschaft (Census of earning
capacity of the working medical fraternity). Schweizerische
Ärztezeitung 92(36), 1361–1366 (2011)
18. Labelle, R., Stoddart, G., Rice, T.: A re-examination of the
meaning and importance of supplier-induced demand. J. Health
Econ. 13, 347–368 (1994)
19. Louviere, J.J., Hensher, D.A., Swait, J.D.: Stated Choice
Meth-
ods. Analysis and Applications. University Press, Cambridge
(2000)
20. Luce, D.: Individual Choice Behavior. Wiley, New York
(1959)
21. Manski, C.F.: The structure of random utility models.
Theor.
Decis. 52, 229–254 (1977)
22. Marinoso, B., Jelovac, I.: GPs’ payment contracts and their
referral practice. J. Health Econ. 22(4), 617–635 (2003)
23. McFadden, D.: Econometric models of probabilistic choice.

In:
Manski, C., McFadden, D. (eds) Structural Analysis of Discrete
Data with Applications, pp. 198–272. The MIT Press,
Cambridge
(1981)
24. McFadden, D.: Economic choices. Am. Econ. Rev. 91, 351–
378
(2001)
25. McGuire, T.G.: Physician agency. In: Culyer, A.J.,
Newhouse, J.P.
(eds) Handbook of Health Economics, vol. 1, Chap. 9, pp. 461–
536.
North-Holland Publishing Company, Amsterdam (2000)
26. Nordt, C.: Strukturwandel der medizinischen
Grundversorgung.
Ursachen und Wirkungen der ärztlichen Arbeitszufriedenheit in
unterschiedlichen Praxismodellen. (Structural changes in
primary
medical practice. Determinants and consequences of physicians’
job satisfaction in different settings). Dissertation, submitted
for
the Factulty of Philosophy, University of Zurich (2003)

27. Pauly, M.: Editorial: A re-examination of the meaning and
importance of supplier-induced demand. J. Health Econ. 13,
369–372 (1994)
28. Revelt, D., Train, K.E.: Customer-specific taste parameters
and
mixed logit. Working paper (1999)
29. Richardson, J.: The inducement hypothesis: that doctors
generate
demand for their own service. In: Gaag, J., Perlman, M. (eds)
Health, Economics and Health Economics., pp. 189–214. North-
Holland Publishing Co., Amsterdam (1981)
Table 8 continued
Attribute Parameter Experience Profession
Value SE Value SE
SD 0.01 (0.24) 0.10 (0.24)
Twelve quality circles Mean -2.33 (0.25) -1.15 (0.18)
SD 1.45 (0.25) 1.26 (0.24)
SD 1.24 (0.34) 0.59 (0.25)

Constant Mean -0.56 (0.16) -0.42 (0.15)
SD 1.76 (0.11) 1.77 (0.09)
Payment Mean 0.46 (0.06) 0.47 (0.06)
123
http://dx.doi.org/10.1002/hec.1697
30. Rischatsch, M., Trottmann, M., Zweifel, P.: Generic
substitution,
financial interests, and imperfect agency. Working paper (2010)
31. Ruud, P.: Simulation of the multinomial probit model: An
anal-
ysis of covariance matrix estimation. working paper (1996)
32. Ryan, M.: A comparison of stated preference methods for
esti-
mating monetary values. Health Econ. 13(3), 291–296 (2004)
33. Ryan, M., Gerard, K.: Using discrete choice experiments to
value
health care programmes: current practice and future reflections.
Appl. Health Econ. Health Policy 2(1), 55–64 (2003)
34. Scanlon, D., Chernew, M., Lave, J.: Consumer health plan
choice. Annu. Rev. Public Health 18, 507–528 (1997)

35. Scott, A.: Eliciting GPs’ preferences for pecuniary and non-
pecuniary job characteristics. J. Health Econ. 20, 329–347
(2001)
36. Scott, A.: Giving things up to have more of others. the
implica-
tions of limited substitutability in eliciting preferences for
health
and health care. Discussion paper 01/98, Health Economics
Research Unit, University of Aberdeen (1998)
37. Sillano, M., Ortuzar, J.D.D.: Willingness-to-pay estimation
with
mixed logit models:some new evidence. Environ Plann. A 37,
525–550 (2005)
38. Sloan, F.: The demand for higher education: the case of
medical
school applicants. J. Human Resour. 6(4), 466–489 (1971)
39. Train, K.E.: Discrete Choice Methods with Simulation.
Univer-
sity Press, Cambridge (2003)
40. Trottmann, M., Zweifel, P., Beck, K.: Supply-side and
demand-
side cost sharing in deregulated social health insurance: which
is
more effective?. J. Health Econ. 31, 231–242 (2012)

41. Ubach, C., Scott, A., French, F., Awramenko, M., Needham,
G.:
What do hospital consultants value about their job? A discrete
choice experiment. Br. Med. J. 326, 1432–1438 (2003)
42. Wordsworth, S., Skatun, A., Scott, A., French, F.:
Preferences for
general practice jobs: a survey of principals and sessional GPs.
Br. J. Gen. Pract. 54(507), 740–746 (2004)
43. Zweifel, P.: Supplier-induced demand in a model of
physician
behavior. In: Gaag, J., Perlman, M. (eds) Health, Economics and
Health Economics, North-Holland Publishing Co., Amsterdam
(1981)
44. Zweifel, P., Telser, H., Vaterlaus, S.: Consumer resistance
against
regulation: the case of health care. J. Regul. Econ. 29(3), 319–
332
(2006)
123
Reproduced with permission of the copyright owner. Further
reproduction prohibited without

permission.
c.10198_2012_Article_405.pdfWhat do physicians dislike about
managed care? Evidence from a choice
experimentAbstractIntroductionLiterature reviewMethodsStudy
designDataEstimation resultsShare of physicians who dislike
MCWillingness to accept MC-type obligationsEffects of prior
experienceDifferences between GPs and
specialistsConclusionsAcknowledgementsAppendixReferences
van der Heijden et al. BMC Health Services Research 2014,
14:280
http://www.biomedcentral.com/1472-6963/14/280
RESEARCH ARTICLE Open Access
Resource use and costs of type 2 diabetes
patients receiving managed or protocolized
primary care: a controlled clinical trial
Amber AWA van der Heijden1,2*, Martine C de Bruijne1,3,
Talitha L Feenstra2,4, Jacqueline M Dekker1,
Caroline A Baan1,2, Judith E Bosmans5, Sandra DM Bot1, Gé
A Donker6 and Giel Nijpels1
Abstract
Background: The increasing prevalence of diabetes is associated
with increased health care use and costs.
Innovations to improve the quality of care, manage the
increasing demand for health care and control the growth
of health care costs are needed. The aim of this study is to
evaluate the care process and costs of managed,
protocolized and usual care for type 2 diabetes patients from a
societal perspective.
Methods: In two distinct regions of the Netherlands, both
managed and protocolized diabetes care were
implemented. Managed care was characterized by centralized

organization, coordination, responsibility and
centralized annual assessment. Protocolized care had a partly
centralized organizational structure. Usual care was
characterized by a decentralized organizational structure. Using
a quasi-experimental control group pretest-posttest
design, the care process (guideline adherence) and costs were
compared between managed (n = 253), protocolized
(n = 197), and usual care (n = 333). We made a distinction
between direct health care costs, direct non-health care
costs and indirect costs. Multivariate regression models were
used to estimate differences in costs adjusted for
confounding factors. Because of the skewed distribution of the
costs, bootstrapping methods (5000 replications)
with a bias-corrected and accelerated approach were used to
estimate 95% confidence intervals (CI) around the
differences in costs.
Results: Compared to usual and protocolized care, in managed
care more patients were treated according to
diabetes guidelines. Secondary health care use was higher in
patients under usual care compared to managed
and protocolized care. Compared to usual care, direct costs were
significantly lower in managed care (€-1.181
(95% CI: -2.597 to -334)) while indirect costs were higher
(€758 (95% CI: -353 to 2.701), although not significant.
Direct, indirect and total costs were lower in protocolized care
compared to usual care (though not significantly).
Conclusions: Compared to usual care, managed care was
significantly associated with better process in terms of
diabetes care, fewer secondary care consultations and lower
health care costs. The same trends were seen for
protocolized care, however they were not statistically
significant.
Trial registration: Current Controlled trials: ISRCTN66124817.

Keywords: Type 2 diabetes mellitus, Controlled clinical trial,
Quality of health care, Health economy
* Correspondence: [email protected]
1Department of General Practice, The EMGO Institute for
Health and Care
Research, VU University Medical Center, van der
Boechorststraat 7, 1081BT
Amsterdam, The Netherlands
2National Institute for Public Health and the Environment
(RIVM), Bilthoven,
The Netherlands
Full list of author information is available at the end of the
article
© 2014 van der Heijden et al.; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the
Creative Commons Attribution License
(http://creativecommons.org/licenses/by/2.0), which permits
unrestricted use,
distribution, and reproduction in any medium, provided the
original work is properly credited. The Creative Commons
Public
Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to
the data made available in this
article, unless otherwise stated.
http://www.controlled-trials.com/ISRCTN66124817
mailto:[email protected]
http://creativecommons.org/licenses/by/2.0
http://creativecommons.org/publicdomain/zero/1.0/
14:280 Page 2 of 8

Background
The increasing prevalence of diabetes is associated with
an increase in health care use and costs [1]. Innovation
to improve quality of care, manage the increasing de-
mand for health care and control the growth of health
care costs is needed [1,2]. There is increasing awareness
that tackling the growing societal and economic burden
brought about by diabetes will require nothing less than
a transformation of health care, from a system that reacts
to acute episodes of illness to one that seeks to pro-
actively maintain health [3-5]. Several deficiencies exist in
the current management of diabetes, including a lack of
care coordination, limited follow-up of patients over time,
inadequate training in self-management skills and insuffi-
cient adherence to evidence-based guidelines by care pro-
viders. As a result, discrepancies exist between care as
recommended and care as received by patients [6-8].
In recent years, targeted programs have become an im-
portant means of improving the quality of diabetes care
and overcoming existing deficiencies [7-9]. A wide array
of approaches exists including the Chronic Care Model
[10,11] and managed care [12]. A common characteristic
of chronic care programs is their underlying assumption
that increasing the quality of care will result in improved
health outcomes. Studies evaluating the effects and costs
of diabetes care, including elements of the Chronic Care
Model, have shown inconsistent results [4,9,13-20]. In
general, these studies did not include a control group or
information on costs from a societal perspective.
In two distinct regions of the Netherlands, diabetes
care was implemented at the primary care level with a
different degree of organization in each region. In the
first region, managed diabetes care based on the Chronic

Care Model was implemented, characterized by central-
ized organization, coordination, responsibility and central-
ized annual assessment. In the second region, protocolized
care was implemented at the primary care level, with cen-
tralized organisation and coordination and decentralized
responsibility and annual assessment. We hypothesized
that managed and protocolized care are associated with a
better process of care (adherence to diabetes guidelines)
and lower costs compared to usual care, which is charac-
terized by a decentralized organizational structure.
The aim of this study was to evaluate the process and
costs of managed diabetes care and protocolized diabetes
care as compared to usual diabetes care.
Methods
In this pragmatic controlled trial, the processes and
costs of diabetes care were compared between patients
receiving managed care, patients receiving protocolized
care and patients receiving usual diabetes care. Measure-
ments were performed before and after the implementa-
tion of protocolized care and compared between the
three groups using a quasi-experimental control group
pretest-posttest design.
The care groups were compared and evaluated accord-
ing to the Dutch guidelines for type 2 diabetes [21]. Ac-
cording to these guidelines, patients should visit their
general practitioners’ (GP) practice four times a year for
a diabetes assessment in which weight and fasting blood
glucose are measured. Blood pressure is recommended
to be measured when antihypertensive medication is
used. Foot screening is recommended to be performed
in patients at risk for developing ulceration. Patients’
well-being, lifestyle and medication use should be dis-
cussed. Once a year, the assessment must be expanded

to include measurement of blood pressure, lipids and
HbA1c and screening for complications, among other
things. To perform screening for retinopathy, the patient
is referred to a specialist in ophthalmology.
Usual care
Usual diabetes care has a decentralized organizational
structure and the patient’s own GP is responsible for dia-
betes care. Patients of all GPs should receive diabetes
care according to the Dutch guidelines for type 2 diabetes
[21]. In the usual care group, 17 GP’s throughout the
Netherlands were included and their diabetes patients
were invited to participate in our study. The GPs in the
usual care group are affiliated with the Continuous Mor-
bidity Registration sentinel stations of The Netherlands
Institute for Health and Services Research [22]. This net-
work of general practices represents 0.8% of the Dutch
population and is representative at a national level for age,
sex, geographic distribution and population density. The
possibility exists that GPs in the usual care group partici-
pate in some form of disease management for type 2 dia-
betes patients.
Managed diabetes care
According to the Chronic Care Model, improvement of
care can be achieved by separating acute care from the
planned management of chronic diseases, offering the pa-
tient education about the disease and enabling supporting
self-management. A computerized information system is
used to provide a reminder to comply with evidence-based
guidelines in planning individual patient care and in giving
feedback to caregivers about their performance [3,4].
In 1996, managed care was implemented in the Dia-
betes Care System (DCS) in the West-Friesland region
of the Netherlands, based on the Chronic Care Model.

In contrast with usual care, in which the GP is respon-
sible for the diabetes care, the DCS is responsible for the
execution and quality of diabetes care and organizes dia-
betes care centrally and coordinates the care across all
care providers. Using a centrally organized database, pa-
tients’ clinical information is accessible to the health care
14:280 Page 3 of 8
providers involved. Starting at diabetes diagnosis, patients
treated by the DCS receive an annual extended diabetes
assessment at the specialized Diabetes Care Centre in
addition to the diabetes care offered by the patients’ GPs.
During this assessment BMI, blood pressure, HbA1c, lipid
levels, fasting glucose level and kidney function are
measured. Screening for cardiovascular diseases, retinop-
athy and complications of the foot is performed at the
centre. Patients have a central role in their care and self-
management is stimulated by providing education and in-
formation programs. Moreover, individual care plans are
discussed with the patient and patients are encouraged to
make their own choices with respect to treatment options
and lifestyle behaviour. Diabetes nurses visit participating
GPs twice a year to provide feedback on their perform-
ance. Individual patients are evaluated and mean values
of risk factors of the GP’s diabetes population are com-
pared to those of the diabetes populations of other par-
ticipating GPs.
Protocolized diabetes care
In 2007, protocolized care was implemented in 12 general
practices in the Amstelland region of the Netherlands. This
form of care focuses mainly on the adherence to guidelines

for type 2 diabetes. In addition to usual care, a web-based
database for the registration of diabetes-related data is used
and is also applied to monitor mean values of risk factors
and whether or not patients received diabetes care in line
with the Dutch guidelines for type 2 diabetes. Education is
offered to all health care professionals involved to increase
their expertise in the field of type 2 diabetes. In contrast
to managed diabetes care, all assessments are performed
in a patient’s own GP’s office and there is no centrally
organized assessment.
The presence of specific elements by type of diabetes
care are presented in more detail in the online Additional
file 1: Table S1.
Patient selection
Type 2 diabetes patients, between 40 and 75 years of age
and capable of understanding the Dutch language were
eligible for this study. From July 2007 to May 2009, dia-
betes patients that fit these criteria were invited to par-
ticipate in the study.
The study population consisted of three subpopula-
tions. For the managed care group, a random sample of
643 patients received an invitation to participate in this
study and 313 (49%) patients participated. For the proto-
colized care group, a random sample of 802 patients re-
ceived an invitation to participate of which 293 (37%)
patients were included. For the usual care group, a ran-
dom sample of 1098 patients was invited and 485 (44%)
patients participated. Patients with type 1 diabetes, defined
as diabetes with onset before the age of 40 in combination
with insulin treatment, were excluded (managed care: n =
3; protocolized care: n = 4; usual care: n = 13). After exclu-
sion of patients without a completed cost diary both at
baseline and one year after baseline, 215 patients receiving

managed care, 197 patients receiving protocolized care
and 333 patients under usual care were eligible for the
analyses. Patients who did not complete two cost diaries
were younger (64 vs. 65, p = 0.01) and were less likely to
be married or living together (73 vs. 80, p = 0.02) com-
pared to patients who completed two cost diaries. Other
characteristics of the participants included were similar to
those who had not completed two cost diaries.
All participants provided written informed consent.
Ethical approval for the study was obtained from the
Ethical Review Committee of the VU University Medical
Center Amsterdam.
Measurements
Information on marital status, educational level, work sta-
tus, smoking habits, diabetes duration, type of treatment
(dietary advice or medication) and performance of assess-
ments and screenings was obtained by self-administered
questionnaires.
Costs
All participants were asked to complete a prospective
cost diary over the course of three months at baseline
and over the course of three months one year later. The
cost diary is considered a valid method of obtaining infor-
mation on costs [23]. If we did not receive a completed
cost diary and the patient did not respond to a reminder,
or in the event of an incomplete diary, we attempted to
collect this missing data in a telephone interview.
Information on costs from a societal perspective was
obtained and included direct health care costs, direct
non-health care costs and indirect costs attributable to
type 2 diabetes. The cost diary included questions re-
garding visits to health care providers related to diabetes

care. Patients also reported visits, if any, to the GP, men-
tal health care providers and complementary health pro-
fessionals. Patients were asked to specify visits to other
medical specialists and therapists. Laboratory tests, use
of home care and hospitalization were also reported. Fi-
nally, indirect costs were measured by asking the patient
about loss of productivity (absenteeism from paid and un-
paid work). Dutch unit prices were used to calculate costs
of resource use (online Additional file 1: Table S2) [24].
Statistical analysis
Characteristics of the population are presented as means
(SD), median (interquartile range) or proportions accord-
ing to diabetes care group. To investigate the process of
diabetes care, the proportion of patients that received the
14:280 Page 4 of 8
assessments or screenings as recommended by the Dutch
guidelines for type 2 diabetes was calculated.
The cost diary at baseline and one year after baseline
was used to calculate health care use and costs over the
course of one year, using linear interpolation between
the two time measurements.
The proportion of patients visiting each health care pro-
vider (Chi2 tests) and mean number of visits per patient
for that specific health care provider (Mann-Whitney test)
were calculated. Despite the skewed distribution of health
care use and costs in our population, mean number of
visits and mean costs were reported because this is the
most informative measure from an economic perspective.

We differentiated between direct health care costs,
direct non-health care costs and indirect costs. Direct
health care costs consisted of costs related to visits to
health care providers, laboratory tests, use of home care
and hospitalizations. Direct non-health care costs in-
cluded the cost of visits to health care providers not paid
by patients’ health insurance. Indirect costs were costs
related to loss of productivity (paid and unpaid work).
Regression analysis was performed with direct health
care and non-health care costs, indirect costs, total direct
and total costs as dependent variables and type of care as
the independent variables, estimating differences in costs
between managed and usual care and between protoco-
lized and usual care. Multivariate regression models were
used to estimate differences in costs adjusted for con-
founding factors. Because of the skewed distribution of
the costs, bootstrapping methods (5000 replications) with
a bias-corrected and accelerated approach were used to
estimate 95% confidence intervals (CI) around the differ-
ences in costs [25].
In a sensitivity analysis, differences in costs were ana-
lyzed using linear multilevel regression analyses to ac-
count for clustering at the general practice level [26].
95% CI’s around cost differences were estimated using
bias-corrected bootstrapping with 5000 replications,
stratified for general practice to account for the cluster-
ing of data. Multilevel analysis was not possible for the
managed care group, due to the low number of patients
within each general practice included in our study.
Results
The mean age of diabetes patients was 65 years. Com-
pared to patients under usual care, a lower proportion of

patients receiving managed care were highly educated
(7.6 vs. 18.6%) and a lower proportion of patients receiv-
ing protocolized care was less educated (48.2 vs. 59.5%).
The use of glucose lowering medication was highest in
patients receiving managed care (88.2%) compared to
patients receiving protocolized (76%) care or usual care
(79.9%) Patients receiving protocolized care (5.6%) or
usual care (13.3%) were more likely to consult a specialist
in internal medicine for diabetes care as compared to pa-
tients receiving managed care (1.0%, Table 1).
A significantly higher proportion of patients receiving
managed care reported that they received information
about self-control of feet, screening of the feet and meas-
urement of weight compared to protocolized and usual
care patients. Compared to usual care, more patients in
the managed care group were screened for retinopathy
and a higher proportion of patients in the protocolized
care group reported screening for nephropathy (Figure 1).
Patients receiving protocolized care had more consul-
tations with the diabetes nurse than patients receiving
managed care or usual care. Patients in the managed
care group visited the dietician more frequently than pa-
tients in the protocolized or usual care groups. Fewer
patients in the managed care group visited specialists in
internal medicine and ophthalmology and the mean num-
ber of these consultations was lower in this group than in
the protocolized and usual care groups (Table 2).
Direct and total direct health care costs were signifi-
cantly lower in the managed and protocolized care groups
compared to the usual care group. After adjustment for
confounding factors, differences in direct costs decreased,
but direct costs remained statistically significantly lower in
managed care than in usual care. Costs associated with

productivity loss (indirect costs) were comparable in the
protocolized and usual care groups, but was higher in pa-
tients receiving managed care as compared to protoco-
lized and usual care, although this relationship was not
statistically significant. Differences in indirect costs in-
creased after adjustment for diabetes duration, marital sta-
tus, educational level and retirement. Total costs were
lower in managed care and protocolized care compared to
usual care, although this relationship was not statistically
significant (Table 3).
Adjustment for clustering at the general practice levels
did not change the difference in costs between protoco-
lized and usual care and slightly increased the statistical
uncertainty (direct health care costs: -1057 (95% CI: -2114
to -166); total costs: -1228 (95% CI: -2443 to 67).
Discussion
Overall, managed care was associated with a better process
of diabetes care, higher use of primary health care, fewer
secondary care consultations and lower health care costs
compared to usual care. The same trends were seen for
protocolized care, however differences in costs were not
statistically significant after adjustment for differences in
patient characteristics between the care groups.
The results of our study are in line with previous stud-
ies showing that an increased focus on the adherence of
guidelines leads to an improved process of the diabetes
care [27]. More specifically, patients receiving structured
or specialized diabetes care were more frequently treated
Table 1 Baseline characteristics of the population stratified by
diabetes care group

Managed
care
Protocolized
care
Usual
care
P value usual care vs
Managed
care
Protocolized
care(n = 215) (n = 197) (n = 333)
Men (%) 52.1 53.8 51.1 0.81 0.54
Age (years) 64.6 (7.4) 65.5 (7.5) 64.4 (7.0) 0.66 0.07
Diabetes duration (years) 6 (2-11) 5 (3-10) 6 (3-10) 0.85 0.74
Married/living together (%) 81.1 78.2 80.4 0.84 0.54
Educational level (%) <0.01 0.04
- low 52.8 48.2 59.5
- medium 39.6 28.4 21.9
- high 7.6 23.4 18.6
Paid job (%) 17.9 26.4 18.6 0.84 0.04

Retired (%) 47.2 45.7 44.4 0.53 0.78
Disabled (%) 9.4 3.6 6.0 0.14 0.22
Smoking status (%) 0.48 0.25
- current 16.8 12.3 16.2
- former 55.1 55.2 52.0
- never 28.1 32.5 31.8
Treatment (%) 0.05 <0.01
- diet only 11.8 24.0 20.1
- oral medication 67.3 64.1 56.8
- insulin 8.1 1.6 10.0
- insulin and oral medication 12.8 10.4 13.1
Treated in secondary care (specialist in internal medicine) 1.0
5.6 13.3 <0.01 0.01
Values are presented as mean (SD), median (interquartile range)
or proportions.
0
10
20
30

40
50
60
70
80
90
100
in
fo
rm
at
io
n
fe
et
fe
et
ey
es
ki
dn
ey

s
bl
oo
d
pr
es
su
re
w
ei
gh
t
Managed care
Protocolized care
Usual care
P
a
ti
e
n
ts
(
%

)
* *
*
*
†
§
§
§
Figure 1 Proportion of patients reporting that they received a
specific medical examination during the last year. *Indicates a
significant difference (P < 0.05) between managed and usual
diabetes care. †Indicates a significant difference between
protocolized and usual care. §Indicates a significant difference
between managed and protocolized care.
14:280 Page 5 of 8
according to the guidelines for type 2 diabetes [14,28,29].
The lower costs associated with managed care compared
to usual care is comparable with recent studies showing
that an increasing level of structured care was associated
with decreased costs [15,30]. In these studies, information
on costs was obtained by claims paid for covered health
care use. Detailed information on health care use or costs
from a societal perspective was unavailable. To obtain
information on health care use to calculate costs of care,
self-administered three-month cost diaries were used.
Self-reporting of information might have led to an under-
reporting of health care use due to recall bias [31]. How-

ever, because of the prospective design of the cost diary,
recall bias and underreporting of data is unlikely. Previous
research comparing data obtained by cost diaries with data
retrieved from insurance companies showed that cost
diaries are a feasible and valid tool with which to measure
costs [23]. Furthermore, the use of the cost diary at base-
line and at one year after baseline to calculate health care
use and costs leads to more reliable estimates than a single
measurement. Using this method, we were also able to ob-
tain indirect costs and costs not covered by health insur-
ance companies. Patients who did not complete two cost
Table 2 Resource use and productivity loss stratified by
diabetes care over one year
Managed care Protocolized care‡ Usual care‡
N = 215 N = 197 N =333
Consultation of .. % ≥1 visits Mean (SD) # of visits % ≥1 visits
Mean (SD) # of visits % ≥1 visits Mean (SD) # of visits
General practitioner 77.8 7.6 (8.6) 80.0 5.7 (6.9) 78.4 6.1 (6.9)
Diabetes nurse 74.5ab 3.8 (3.8)b 82.6 4.3 (3.5)a 84.5 3.7 (2.8)
Dietician 38.4ab 1.4 (2.4)ab 20.5 0.9 (2.2) 21.9 0.9 (2.4)
Podiatrist 19.4b 0.7 (1.8)b 9.2a 0.4 (1.6)a 24.3 1.2 (2.9)
Physical therapist 25.9 5.3 (13.8) 30.3a 7.3 (18.1)a 21.0 3.9
(11.9)
Specialist in

- Internal medicine 6.9ab 0.4 (2.0)ab 15.4a 0.6 (1.7)a 28.9 1.5
(2.8)
- Ophthalmology 17.6ab 0.8 (2.5)ab 47.7 1.5 (2.5) 52.0 1.8 (2.6)
- Cardiology 15.7 0.6 (2.5) 15.4 0.7 (1.9) 15.2 0.7 (2.3)
- Neurology 5.1 0.2 (0.9) 6.2 1.6 (0.7) 6.4 0.6 (4.0)
- Nephrology 1.4 0.0 (0.4) 3.6 0.2 (1.3) 1.8 0.1 (0.4)
Other specialism 25.9 1.4 (3.1) 27.7 1.4 (2.9) 32.8 1.6 (4.0)
Hospitalization 9.7 0.7 (3.0) 10.3 1.1 (4.7) 12.5 2.7 (14.8)
Absenteeism paid work 8.8 4.9 (27.8) 10.8 2.8 (14.1) 10.0 3.1
(15.2)
Absenteeism unpaid work 13.9 12.3 (50.0) 9.7 18.2 (127.4)a
18.2 20.7 (70.6)
Data are expressed as proportions of patients who used the
specific resource and mean (SD) resource use per patient over
one year. aSignificantly different
(P < 0.05) from usual care. bSignificantly different (P < 0.05)
from protocolized care.
14:280 Page 6 of 8
diaries were excluded from this study, which may have re-
sulted in the selection of healthier diabetes patients. How-
ever, with the exception of age and marital status, patient
characteristics did not differ statistically significantly be-
tween those included in and excluded from the study.

In the managed care group, many of the patients were
disabled and more patients reported sick leave compared
to patients in the protocolized and usual care groups. It
has been shown that individuals with less education are at
increased risk for sick leave [32], which may provide an
explanation of the high indirect costs seen in this group,
Table 3 Mean (SD) costs (€) over one year and mean differen
Managed
care
Protocolized
care
Usual care Mean diffe
managed
(n = 215) (n = 197) (n = 333) Model 1
Direct health
care costs
1259 (2712) 1568 (3288) 2607 (8678) -1348
(-2593 to-5
Direct
non-health
care costs
17 (102) 19 (127) 13 (92) 4 (-10 to 2
Total direct
costs
1276 (2715) 1587 (3293) 2620 (8680) -1344

(-2606 to -5
Indirect
costs
1727 (8808) 1125 (4548) 1328 (4840) 461 (-524 to
Total costs 3003 (9457) 2711 (5690) 3949 (10328) -882 (-2415
to
Usual care is the reference category.
Model 1: adjusted for age and sex.
Model 2: further adjusted for diabetes duration, marital status,
educational level and
aSignificantly different (P < 0.05) from usual care.
in which only 7.6% of the patients were highly educated.
Managed and protocolized care was implemented at the
general practice level and organized regionally. Random
allocation of patients to the intervention or control group
was therefore not feasible. Despite adjustment for differ-
ences in patient characteristics between the care groups,
uncontrolled biases might have affected the results. Of the
characteristics that differed between patients of the three
groups, age, educational level and work status significantly
affected the results. Differences between groups in treat-
ment did not influence the results.
ces in costs (€) between groups
rences in costs between
and usual care (95% CI)
Mean differences in costs between
protocolized and usual care (95% CI)
Model 2 Model 1 Model 2

31)a
-1188
(-2559 to -339)a
-1057
(-2333 to -201)a
-794 (-2082 to 52)
5) 8 (-6 to 29) 7 (-10 to 33) 6 (-13 to 33)
41)a
-1181
(-2597 to -334)a
-1050
(-2336 to -191)a
-788 (-2042 to 47)
2277) 758 (-353 to 2701) -78 (-782 to 836) -96 (-844 to 823)
932) -423 (-2146 to 1566) -1128 (-2682 to 86) -884 (-2281 to
323)
retirement.
14:280 Page 7 of 8
We acknowledge that managed as well as protocolized
care were implemented in a well functioning Dutch pri-
mary care system which may have resulted in smaller
differences between patients receiving managed or pro-

tocolized care and patients treated in accordance with
usual care. However, we do believe that the results of
our study can be extrapolated to other countries and
other health care systems with high referral rates to sec-
ondary care [33].
Our results indicate that a part of health care use can
be substituted by the implementation of managed care
in particular, resulting in fewer consultations with spe-
cialists at the secondary care level. This substitution of
secondary care for primary care was not associated with
a lower quality of care compared to usual care. Instead,
managed care performed better in terms of the process
of care. More patients in the managed care group re-
ceived assessments and screenings according to diabetes
guidelines, which might have resulted in the detection of
complications at an early stage and early initiation of
appropriate treatment, which may, consequently, reduce
the number of complications in the long run.
Conclusions
The implementation of managed diabetes care, with a
high level of centralization, embedded in primary care re-
sulted not only in a better process of diabetes care, but
also in lower health care costs. The combination of better
process of care and reduced costs is of great importance
particularly for a highly prevalent, chronic disease such as
type 2 diabetes, which makes this form of managed care a
promising strategy for treating the growing population of
type 2 diabetes patients.
Additional file
Additional file 1: Table S1. Characteristics of care by diabetes
care
group. Table S2. Costs prices used for valuing resources or

absenteeism
(2008).
Competing interests
The authors declare that they have no financial or non-financial
competing
interests relevant to this article. No author had a relationship
with any
company that might have an interest in the submitted work; and
no author
has specified non-financial interests that may be relevant to the
submitted
work. There are no financial conflicts of interest among any of
the authors
including but not limited to funding.
Authors' contributions
AVDH collected and researched data and wrote the manuscript.
GN and
JMD conceived and designed the study and reviewed and edited
the
manuscript. TF and MdB research data, critically revised the
manuscript, SB
and GD contributed to acquisition of data, reviewed and edited
the
manuscript. All authors read and approved the final manuscript.
Acknowledgements
This study was funded by ZonMw, the Netherlands Organization
for Health
Research and Development, and by Univé Insurance. We thank
the general
practitioners of NIVEL’s CMR sentinel stations and the DCS
for their help with
patient recruitment and data collection.
The researchers were independent of the studies funders and had

full access
to all required data. The funders had no role in the design and
conduct of
the study; collection, management, analysis, and interpretation
of the data;
and preparation, review, or approval of the manuscript. A.v.d.H
had full
access to all of the data in the study and takes responsibility for
the integrity
of the data and the accuracy of the data analysis.
Author details
1Department of General Practice, The EMGO Institute for
Health and Care
Research, VU University Medical Center, van der
Boechorststraat 7, 1081BT
Amsterdam, The Netherlands. 2National Institute for Public
Health and the
Environment (RIVM), Bilthoven, The Netherlands. 3Department
of Public and
Occupational Health, The EMGO Institute for Health and Care
Research, VU
University Medical Center, van der Boechorststraat 7, 1081BT
Amsterdam,
The Netherlands. 4Department of Epidemiology, University
Medical Center
Groningen, Groningen, The Netherlands. 5Faculty of Earth and
Life Sciences,
VU University Amsterdam, Amsterdam, The Netherlands.
6NIVEL, Netherlands
Institute for Health Services Research, Utrecht, The
Netherlands.
Received: 11 September 2013 Accepted: 9 June 2014
Published: 25 June 2014

References
1. American Diabetes Association: Economic costs of diabetes
in the U.S. in
2012. Diabetes Care 2013, 36:1033–1046.
2. Gruber J: The cost implications of health care reform. N Engl
J Med 2010,
362:2050–2051.
3. Bodenheimer T, Wagner EH, Grumbach K: Improving
primary care for
patients with chronic illness. JAMA 2002, 288:1775–1779.
4. Bodenheimer T, Wagner EH, Grumbach K: Improving
primary care for
patients with chronic illness: the chronic care model, Part 2.
JAMA 2002,
288:1909–1914.
5. Bodenheimer T: The future of primary care: transforming
practice. N Engl
J Med 2008, 359:2086–2089.
6. Glasgow RE, Strycker LA: Preventive care practices for
diabetes
management in two primary care samples. Am J Prev Med 2000,
19:9–14.
7. Kirkman MS, Williams SR, Caffrey HH, Marrero DG: Impact
of a program to
improve adherence to diabetes guidelines by primary care
physicians.
Diabetes Care 2002, 25:1946–1951.
8. McGlynn EA, Asch SM, Adams J, Keesey J, Hicks JH,

DeCristofaro A, Kerr EA:
The quality of health care delivered to adults in the United
States. N Engl
J Med 2003, 348:2635–2645.
9. Knight K, Badamgarav E, Henning JM, Hasselblad V, Gano
AD, Ofman JJ,
Weingarten SR: A systematic review of diabetes disease
management
programs. Am J Manag Care 2005, 11:242–250.
10. Wagner EH, Austin BT, Davis C, Hindmarsh M, Schaefer J,
Bonomi A:
Improving chronic illness care: translating evidence into action.
Health Aff 2001, 20:64–78.
11. Wagner EH, Bennett SM, Austin BT, Greene SM, Schaefer
JK, Vonkorff M:
Finding common ground: patient-centeredness and evidence-
based
chronic illness care. J Altern Complement Med 2005, 11(Suppl
1):S7–S15.
12. Ouwens M, Wollersheim H, Hermens R, Hulscher M, Grol
R: Integrated care
programmes for chronically ill patients: a review of systematic
reviews.
Int J Qual Health Care 2005, 17:141–146.
13. Herrin J, Cangialose CB, Nicewander D, Ballard DJ: Cost
and effects of
performance feedback and nurse case management for medicare
beneficiaries with diabetes: a randomized controlled trial. Dis
Manag
2007, 10:328–336.

14. Lairson DR, Yoon SJ, Carter PM, Greisinger AJ, Talluri
KC, Aggarwal M,
Wehmanen O: Economic evaluation of an intensified disease
management
system for patients with type 2 diabetes. Dis Manag 2008,
11:79–94.
15. Littenberg B, MacLean CD, Zygarowski K, Drapola BH,
Duncan JA, Frank CR:
The Vermedx Diabetes Information System reduces healthcare
utilization. Am J Manag Care 2009, 15:166–170.
16. Mattke S, Seid M, Ma S: Evidence for the effect of disease
management: is
$1 billion a year a good investment? Am J Manag Care 2007,
13:670–676.
17. McEwen LN, Hsiao VC, Nota-Kirby EM, Kulpa GJ, Schmidt
KG, Herman WH:
Effect of a managed care disease management program on
diabetes
care. Am J Manag Care 2009, 15:575–580.
14:280 Page 8 of 8
18. McRae IS, Butler JR, Sibthorpe BM, Ruscoe W, Snow J,
Rubiano D,
Gardner KL: A cost effectiveness study of integrated care in
health
services delivery: a diabetes program in Australia. BMC Health
Serv Res
2008, 8:205.

19. Wagner EH, Sandhu N, Newton KM, McCulloch DK,
Ramsey SD, Grothaus LC:
Effect of improved glycemic control on health care costs and
utilization.
JAMA 2001, 85:182–189.
20. Wagner EH, Grothaus LC, Sandhu N, Galvin MS, McGregor
M, Artz K,
Coleman EA: Chronic care clinics for diabetes in primary care:
a
system-wide randomized trial. Diabetes Care 2001, 24:695–700.
21. Rutten GEHM, de Grauw WJC, Nijpels G, Goudswaard AN,
Uitewaal PJM,
van der Does FEE, Heine RJ, van Ballegooie E, Veduijn MM,
Bouma M:
NHG-Guidelines type 2 Diabetes Mellitus (second version).
Huisarts Wet
2006, 49:137–152.
22. Donker GA: Continuous morbidity registration sentinels
Netherlands 2006 NIVEL,
annual report; 2007. http://www.nivel.nl/pdf/CMR-Peilstations-
2006.pdf.
23. Goossens ME, Rutten-van Molken MP, Vlaeyen JW, van der
Linden SM: The
cost diary: a method to measure direct and indirect costs in
cost-effectiveness research. J Clin Epidemiol 2000, 53:688–695.
24. Oostenbrink JB, Bouwmans CAM, Koopmanschap MA,
Rutten FFH:
Handleiding voor kostenonderzoek: Methoden en standaard
kostprijzen voor
economische evaluaties in de gezondheidszorg. Geactualiseerde
versie 2004

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