For what services do general practitioners induce demand?
Economic incentives and professional norms
Lotte Bøgh Andersen & Søren Serritzlew
Paper presented at the Department of Economics, University of Copenhagen, 16. November 2007
Abstract
Whether general practitioners (GPs) induce demand for their own services, when they experience a
shortage of patients, is a point of disagreement in the literature. Likewise, the literature does not
agree upon the existence of rationing in case of too many patients. We argue that the GPs induce
demand and ration if no firm professional norm regulates the use of the specific service. GPs with
few patients give their patients more of services without professional norms than GP with many
patients, while there is no difference for services governed by professional norms. This indicates
that both economic incentives and professional norms are important. The conclusions are based on
register data concerning the GPs’ use of different services (n= 257 practices in the County of
Aarhus), six qualitative interviews and documentary material.
Introduction
The income of general practitioners (GPs) in most western countries depends on the number (and
type) of services supplied to the patients, because many payment systems include fee-for-service
elements (Gosden et al. 1999 & 2001; Groenewegen et al 1991; Hoffmeyer & McCarthy 1994). As
expected by standard agency theory, fee-for-service contracts lead to higher service production than
salary and capitation contracts (Sørensen & Grytten, 2003; Krasnik et al 1990). Letting the GPs
income depend on the number of services produced can, however, have unintended and undesirable
effects if the GPs in fee-for-service systems prioritize their own pecuniary interests higher than
public economy and patient welfare. The GPs might provide too many services per patient if they
have few patients on the GPs lists. ‘Supplier-induced demand’ happens when the suppliers (here the
GPs) try to produce more services than initially demanded by patients or ordered by society. If the
inconvenience of producing the marginal service exceed the fees (due to many patients), they might
on the other hand provide fewer services than desirable seen from the societal perspective (and
especially seen from the patient perspective). This is called ‘rationing’. There is, however,
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considerable controversy among health care researchers about the existence of supplier-induced
demand and rationing. Some researchers find evidence of induction/rationing (Evans 1974;
Richardson & Peacock 2006; Bech et al. 2007), and others find no relationship between the list size
and the number of services (Davis et al. 2000; Madden et al., 2005; Grytten & Sørensen, 2007). We
argue that this is because the existence of firm professional norms conditions the relationship. We
hypothesize that if a specific medical service is firmly regulated by professional norms, the number
of services per patient does not depend on the list size. Otherwise, we expect the number of services
per patient to be higher, the fewer patients the GPs have.
This proposition is tested on the very reliable Danish health insurance data which registers
the exact use of 70 different GP-services. The structure of the paper is as follows: We first discuss
the literature on supplier-induced demand followed by a brief introduction to the Danish GP
payment system. We then use six qualitative interviews and documentary material to map out the
relevant professional norms, which enables us to put forward the testable hypotheses. After a short
description of the data and the methods, we test the hypotheses, and the paper concludes with a
discussion about the interpretation of the results and the implications for further research.
The literature on supplier-induced demand
More than 35 years ago, Newhouse claimed that health care providers could create the demand for
their own services (Newhouse 1970). The validity of this claim soon became one of the most
controversial topics in health economics (Richardson & Peacock, 2006, 2). The central concepts in
the supplier-induced demand (SID) literature are supplier-induction and rationing, which are
physicians’ use of their market power to respectively increase and decrease the demand for their
services. The market power of GPs is based on the asymmetric distribution of information. GPs
have much more information about the service production than the patients. Patients seldom know
how many (and which) medical services they need. Part of the SID-literature expects the GPs to
take advantage of this information asymmetry and affect the demand if this is lower than the GPs
want. This implies that the number of potential patients affects the service production: The fewer
patients (that is, the lower demand for services), the more will the GP try to induce extra demand. In
a list system (where the each GP has a list of patients attending only this GP) the SID-logic implies
that GPs with short lists (low initial demand) try the hardest to increase demand and therefore tries
to provide more services per patient than GPs with long lists. Physicians with very long lists might,
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on the other hand, ration their services, because the disutility of providing the marginal service
exceeds the benefits (the fee).
Both supplier induction and rationing would lead to a negative relationship between the
number of services per patient and the number of listed patients per GP (the list size). Several
studies have identified this relationship empirically (e.g. Evans 1974; Richardson & Peacock 2006).
One should not, however, leap from this association to the existence of supplier-induced demand.
At least three alternative explanations of the negative association between list sizes and services per
patient are mentioned in the literature (see Carlsen & Grytten 1998 for a more detailed discussion):
First, if the fees are not fixed, and if the patients pay the services themselves, it can be difficult to
distinguish a price effect from an inducement effect. Few patients per GP can drive the prices down,
which increases the demand for services. Second, the availability of doctors (e.g. how long before
the patients can get an appointment) can affect the demand. Patients might not want (or need) to go
to the doctor as frequently if they must wait many days or drive long distances. Third, the number
of patients per physician might be endogenous. If the GPs determine the list sizes themselves,
doctors with very care-demanding patients might choose to have a short list (which also implies a
negative relationship between the list size and the number of patients on the list, but with reversed
time order).
Even in literature considering these factors, the existence of supplier-induction is still a
much contested issue in the literature. On one hand, Richardson and Peacock (2006: 14) for
example argue that “SID [supplier induced demand] provides a satisfactory explanation of the
observed pattern and change in the demand for Australian medical services”, and Delattre and
Dormont concludes that ”Econometric results give a strong support for the existence of PID
[physician-induced demand]” (2003: 741). On the other hand, Grytten and Sørensen (2007; 2001)
for example find that long patient lists in Norway do no lead to rationing, and that short lists do not
increase service production (for other studies, which find similar results for other countries, see for
example Davis et al. 2000 and Madden et al. 2005).
We argue these results conflict, because the relationship between list size and services per
patient is conditioned by the existence of professional norms. Professional norms can be defined as
prescriptions for the acceptable actions under given conditions (e.g. specific patient symptoms)
applying to and sanctioned within a given occupation (Andersen, 2005: 25).Grytten, Skau, Sørensen
and Aasland argue that the professional and medical norms might control the behavior of GPs, and
that GP therefore do no allow their own “greed” to influence the production (2003: 66). They claim
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that “to reduce the desirable or actual treatment is not in accordance with medical ethics and
professional norms” (ibid: 52, our translation). This implies that profession norms govern the
provision of all the medical services of GPs. On the contrary, we argue that professional norms
govern the use of some medical services, whereas no firm professional norms apply for other
services. The medical occupation is usually seen as a strong profession (Freidson 1970; Dent 2003:
175-178; Saks 1995) with strong professional norms, but services without professional norms can
be identified also for occupations such as doctors and dentists (Serritzlew & Andersen, 2006;
Andersen & Blegvad, 2003). Further, two notions of the medically correct level of service provision
exist. The optimal level for the patient is when marginal medical benefit is higher than the price
paid by the patient (often zero), and the optimal level for society is when marginal medical benefit
is higher than marginal costs. The level prescribed by the professional norms (if any) is probably
between these two standards, and for some services it seems to be professional acceptable to
provide more services (and more time consuming care) to patients, when doctors have the capacity
to do so (Richardson & Peacock 2006, 13). Although sometimes pictured as an exact (and
omniscient) science, medical decision-making is often both complex and uncertain (Richardson &
Peacock, 2006: 8-9). Even if the professional norms of the medical occupation counteract some of
the potential drawbacks of the GP market power, strategic action is probably possible (for some
medical services) without going against the norms. Richardson and Peacock claim that for many
types of services no professionally defined level exists (ibid). This implies that the GPs have large
discretion in the provision of these services, and several studies have shown that the GPs vary much
with regard to the level of service provision (ibid; Vedsted et al. 2005). The general picture is that
health professionals primarily affect the number of services strategically when no professional
norms apply (Goodrick & Salancik 1996; Stano 1985; Dranovo & Wehner 1994; Grytten &
Sørensen 2001; Carlsen, Grytten & Skau 2003). This indicates that the general supplier-induction
hypothesis must take the service type into account. The theoretical proposition of this paper thus is:
If a specific medical service is firmly regulated by professional norms, the number of services
per patient does not depend on the list size. Otherwise, the number of services per patient is
higher, the fewer patients the GPs have on their lists.
Despite the increasing attention to the importance of professional norms within the SID literature,
few have tested this proposition. Iversen and Lurås show (with data from Norway) that when
”professional opinions differ”, doctors with few patients make ”longer and more frequent
consultations and more laboratory tests per listed person” (2000: 447), and Davis et al. (2000: 407)
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argues that clinical factors rather than economic incentives explain the variation in clinical practice
in New Zealand. We do not, however, know of any studies, which systematically examine the
professional norms beforehand and test whether the level of supplier induction is higher for services
without professional norms compared to services with professional norms. This paper provides such
a test. After a short description of the Danish system, we analyze the norms.
Primary physician services in Denmark
In Denmark, the regions (before 2007: the counties) have responsibility for planning, organizing
and running primary health services, including GP services. GPs are all self-employed specialists in
general medicine with a contract with the National Health Insurance (Sygesikringens
Forhandlingsudvalg & Praktiserende Lægers Organisation 2006). The pay system includes fee-peritem, fee-per-patient and a fixed amount per doctor. The fees are fixed in the agreement between the
GPs’ organizations and the National Health Insurance. Fee-per-item comprises about 75 % of the
GPs’ gross income. Only very few services involve user payment (e.g. medical certificates). The
agreement between the GPs’ organization and the National Health Insurance specifies the
availability conditions. A patient with an acute need must be given an appointment the same day,
while non-acute treatments must be within 5 days (Sygesikringens Forhandlingsudvalg &
Praktiserende Lægers Organisation 2006, § 39.1.c+d). Further, central planning has ensured that the
nearest GP is very seldom far away. This makes availability as an alternative explanation of the
association between the number of patients and the number of services per patient less relevant. As
the bulk of services are totally fee of charge, and the fees are fixed, another alternative explanation
can be eliminated (that the association is due to fee reductions when patients are sparse). The last
alternative explanation cannot, however, be eliminated that easy. We cannot conclude that list sizes
are not endogenous in Denmark. If the number of patients per GP exceeds 1600, they can choose to
close their list for more patients. In case of more than 2411 patients the list is closed automatically.
In the investigated county, 133 practices are open, 130 are closed voluntarily, while only one
practice exceed the limit for automatic closure. Due to the voluntary closure of half of the GP
practices, the last alternative explanation of the association between list size and services per patient
(that patient morbidity affects both services per patient which then influence the GPs decisions
regarding list size) thus remains a methodological problem and is discussed in the section on data
and methods.
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The mentioned agreement between the GPs’ organization and the National Health Insurance
differentiates between base services and supplementary services. Base services are the different
types of consultations while the supplementary services consist of add-ons to the consultations (e.g.
puncture of the ear drum) and laboratory tests (e.g. urine test). The most used service is the ordinary
consultation. Telephone and email consultations are faster alternatives (with lower fees) to the
ordinary consultation, while visits, cognitive therapy and preventive consultations1 are more timeconsuming alternatives with higher fees. The Danish remuneration system thus opens for two types
of supplier induction/rationing. First, the GP can provide more or less supplementary services, and
second, they can choose strategically between three types of base services: The short ones with low
few (telephone and email consultations), the normal one (ordinary consultation) and the more time
consuming ones with higher fees (primarily visits and cognitive therapy). This raises another
question: What services are lucrative (in terms of the trade off between fee and work load/time
used)? This will be dealt with in the next section together with the professional norms.
Different services: Professional norms and lucrativeness
To put forward precise hypotheses on the relationship between list size and services per patient, we
need two types of information about the services: The professional norms governing them (if any)
and whether they are perceived to be lucrative compared to the alternative. We therefore conducted
six qualitative interviews with general practitioners in the county of Aarhus. We stratified on gender
and practice size (single or partnership) and sampled randomly within these strata. Further, we
gathered clinical guidelines from the home page of the Danish Medical Association. The interviews
and documents were coded in NVivo 7 for find evidence on the existence of professional norms
(see http://www.ps.au.dk/lotte/praksis_kvali.doc for the interview guide and the categories from the
coding).
The analysis showed unambiguously that the GPs’ perceive visits to be unprofitable and
cognitive therapy to be lucrative compared to the ordinary consultation. Both (are supposed to) take
more time than the ordinary consultation. The findings on the supplementary services (add-ons and
lab tests) did not indicate any substantial differences in lucrativeness: The GPs generally considered
the fee for these services to be in line with the ordinary consultation, considering the used time. We
1
Before April 2006, a preventive consultation gave the same fee as the normal consultation, excluding prevention of
ischemic heart disease which was better paid. In April 2006 a pre-arranged prevention consultation was introduced, but
it was not fully implemented at the time of the data collection.
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have chosen to focus on the services listed in table 1. They are all rather frequently used. Among
these services, especially cognitive therapy is lucrative, especially for GPs with short lists (it is
rather time consuming.
Table 1: Professional norms and lucrativeness of the analyzed medical services
Medical service
Professional norm
Fee in relation to time used
Ordinary consultation
Strong
Medium
Cognitive therapy
Very weak
Time-consuming, high fee
Visit
Strong concerning elderly
Time-consuming, not high fee
Urine test
Medium
Quick, but low fee
INR test
Medium
Medium
Index of ordinary add-ons
Medium
Medium
Index of ordinary lab tests
Medium
Medium
Note: only add-ons and lab tests, which the GP used on average one time or more in a two month
period, are part of the indexes.
The interviews clearly show that professional norms require that the GP must see a patient if he/she
contacts the GP (unless the demand for a consultation is obviously unfounded). Thus, the norms
governing the ordinary consultation are rather strong. The more specialized base services are,
however, less firmly governed. Cognitive therapy was recently introduced, and we do not find any
firm norms concerning the use of this service. The norms require visits to fragile elderly patients,
but no norms regulate the use of visits to other groups of patients. The supplementary services all
seem to be regulated by norms of medium strength. The main result is that cognitive therapy stands
out as the service with the weakest norms. Being lucrative and without firm norms, it is the ‘most
likely’ service in term of the SID hypothesis. The ordinary consultation is, on the other hand, the
‘most unlikely’ service for SID to happen. The other services lie between these extremes. Based on
this analysis, the following hypotheses can be formulated:
1. The number of cognitive therapies per patient is higher for GP with relatively few listed
patients.
2. The number of ordinary consultations per patient does not depend on the list size per GP.
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3. The negative relationship between the number of cognitive therapies per patient and the number
of listed patients per GP is significantly stronger than for ordinary consultations, urine tests,
INR, visits, lab tests in general and add-ons in general.
After a short presentation of the data and methods, these hypotheses will be tested quantitatively.
Data and methods
Predominantly, the paper is based on statistical analysis of register data, but (as discussed above)
we included qualitative interviews with six general practitioners (GPs) and documentary material to
investigate the professional norms. The primary data source was the Danish Health Insurance
Register, which contains the number and type of services provided by GPs. Based on this register,
we investigated services provided in April and May 2006 in the County of Aarhus. The units of
analysis were 257 of the 264 practices in the county. Newly established and near-terminate
practices were excluded, but we included both single practices and group practices. For the group
practices, the number of patients was divided by the number of GPs working in the practice. We
investigated only day-time services, because the patients attend different doctors at night.
As suggested in the hypotheses, the dependent variable is the number of services per patient.
We used three versions of this variable: The simple number of services per patient, the number of
services per patient controlled for the socio-demographic characteristics of the patients (the county
did the standardization) and a version where we calculated the relative deviation from the average
use of the service. All three operationalizations gave roughly the same results. We present the lastmentioned and comments on the other measures if they gave other results.
The primary explanatory factor was the list size (the number of listed patients per GP). The
type of service is an important control variable (of course, the number of services provided per
patient differs from service to service), and the interaction term between the type of service and the
list size measures differences in supplier-induction between the services.
As mentioned in the literature review, the time order of the variables might be problematic.
With the available data, there was no simple way to determine whether list size affected service
production or the other way around. The SID-theory expects list size to affect service production,
but list size may also be determined by the service needs of the patients. That is, GPs with healthy
patients might try to increase the number of patients on their list. Like we mentioned above, we
made all the analyses using a version of the list size variable, which was standardized for the
specific composition of patients. Further, the GPs can essentially only affect their list size by
8
opening and closing their lists. Thus, the status of the practices (open or closed) was coded and
controlled for. We also controlled statistically for the following socio-demographic variables: The
proportion of inhabitants from third world countries, the proportion of social housing, the
population density, the unemployment, and the social index. These data come from the Ministry of
the Interior’s key figures. Another solution to the endogeneity problem could have been to use
instrument variables and a two-stage least square estimation, but no adequate instrument variable
was available.
As an additional control variable, we included the average age of the GP, because the
qualitative interviews indicated that young and old GPs differ. This can be due to dept from the
practice purchase or different priorities. The average age of the GPs is highly correlated with the
seniority of the GPs. The results gave similar results when we included the seniority instead of the
age.
Grytten and Sørensen (2007: 724) suggest that the relationship should be non-linear and that
the total number of consultations should be constant for varying list sizes (namely around the
optimal total number of consultations for the GP). We find it more plausible that the GPs all the
time adjust at the margin: That the elasticity is lower than one for the whole spectrum (a reduction
of 1 % in the number of patients leading, for example, only to a reduction in the total number of
services of 0.95 %). As nothing in our data indicates that the relationship is non-linear, we use
linear models in the analyses of the number of services per patient and list size.
We use three types of OLS regressions. The first type includes only one service, and the
practices are the units of analyses. But to compare the use of different services systematically, the
second type of regression analyzes the practices’ use of different services. In these analyses, the
number of service1 per patient for GP1 is one observation, the number of service2 per patient is a
second observation, the number of service2 per patient for GP2 is a third observation etc. The third
type of analysis is a fixed effect model with dummies for the practices, in which the units of
analysis are the same as in the second type of analysis.
Results
The structure of this section follows the three hypotheses. Firstly, we analyze the relationship
between the number of cognitive therapies per patient and the number of listed patients per GP.
Secondly, the same test is performed for ordinary consultations. Finally, we investigate whether the
9
number of services per patient depends more on the list size for cognitive therapy than for other
services.
Hypothesis 1 expects that number of cognitive therapy services per patient is higher, the
fewer listed patients the GPs have. This is tested in model 1, 2 and 3 in table 2. All these analyses
indicate that the list size affects the number of cognitive therapies per patient negatively. The
inclusion of controls in model 2 and 3 reduce the size of the effect a little, but it is still highly
significant. This confirms hypothesis 1.
(Table 2 around here)
According to hypothesis 2, the list size should not affect the number of ordinary consultations per
patient. This is tested in model 4 in table 2. As expected, the effect is not significant, and this does
not change if more controls are introduced (not shown). This supports hypothesis 2.
Hypothesis 3 is about the interaction between the type of service and the list size. It expects
the relationship between the number of services per patient and the list size to be more strongly
negative for cognitive therapy compared to urine tests, INR, ordinary consultations, visits, lab tests
and add-ons. It does, in other word, expect that the interaction term between these services and the
list size is positive in the regressions with cognitive therapy as the reference service (a high negative
association means a lot of SID). In accordance with the tests of hypothesis 1 and 2, the results show
that the list size does affects cognitive therapy services more than normal consultations. The
positive interaction term in model 6 in table 2 between the dummy for ordinary consultation and the
list size shows this. The comparisons between cognitive therapy and the other services consistently
show a positive interaction term between the list size and the dummies for the services other than
cognitive therapy (urine tests and INR in model 5 and all the analyzed groups of services in model
7, 8 and 9). Except for visits, the regression coefficients of the interaction variables are significantly
higher than zero. The professional norms for visits do, as mentioned, demand this service for fragile
elderly patients, but otherwise the norms concerning the service is weak. This might explain why
the association between list size and services per patient is not significantly weaker for visits
compared to cognitive therapies.
The results are generally in accordance with the expectations, except for one thing: When
the practice dummies are introduced in model 9, the effect of the list size on the number of
cognitive therapy services per patient is no longer significant and changes the sign (cognitive
10
therapy is the reference service in model 9, so the regression coefficient for list size is the effect to
this specific service). There is much multicollinearity in this model (the tolerance for the list size is,
for example, 0.0184), and this analysis might not be meaningful with the available data. We would
be grateful for comments on this issue.
Discussion and conclusion
The results generally confirm the hypotheses (expect for the fixed effect model which was
discussed above). The use of cognitive therapy, which is a well-paid, time-consuming alternative to
the ordinary consultation unregulated by professional norms, depends on the number of listed
patients per GP, whereas the number of ordinary consultations per patient does not depend on the
list size. Further, the use of cognitive therapy depends significantly more on the number of listed
patients than it is the case for ordinary consultations, urine tests, INR, ordinary lab tests and
ordinary add-ons. The visit service, which is another time-consuming alternative to the ordinary
consultation, is only regulated by professional norm for elderly patients, and its use depends less on
the list size than the use of cognitive therapy do, but the interaction is not statistically significant.
This fits with the results from the qualitative interviews, which show that the norms governing
visits are the second-weakest (next to cognitive therapy). The result thus indicates that the degree of
supplier induction decreases with increasing firmness of the norms governing the specific service.
The analysis generally supports the theoretical proposition. If a specific medical service is
firmly regulated by professional norms, the number of services per patient does not depend on the
list size, and if no firm norm applies the number of services per patient decreases with the number
of patients on the GPs list. The answer to the question posed in the title is thus: General
practitioners induce demand for services which are not regulated by professional norms.
The paper proposes a possible synthesis conflicting results in the SID literature: The degree
of supplier induced demand depends on firmness of the professional norms governing the specific
services. As the results come from only one county in one country, more evidence is needed, before
the results can be generalized. We therefore call on studies where professional norms are included
in the analyses and not only used as ex-post explanations. This paper indicates that economic theory
can fruitfully be combined with the sociology of professions, as both economic incentives and
professional norms seem affect the behavior of GPs.
11
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Table 1: Regression of services per 100 patients. April and May 2006. Unstandardized regression coefficients. Relative difference from mean.
Model 1 :
Only
cognitive
therapy)
Intercept
List size
Open practice
Average age of the GPs
0.0210
-0.0198 **
Model 2:
Cognitive
therapy with
practice
controls
0.118
-0.0167 **
-0.00268
-0.00189 ***
Model 3:
Cognitive
therapy with
all controls
0.143
-0.0152**
-0.00465
-0.00187***
Model 4:
Ordinary
consultation
with practice
controls
0.0526
0.0050
-0.00215
-0.00129 ***
Urine test
Urine test * List size
INR
INR * List size
Ordinary consultation
Ordinary consultation * List size
Visits
Visits * List size
Ordinary laboratory services
Ordinary lab. services * List size
Ordinary add-on services
Ord. add-ons * List size
Population density
Proportion social housing
Proportion unemployed
Proportion third world immigrants
Social index
R2
N
Model 5:
Model 6:
Cognitive therapy Cognitive
therapy and
compared to urine ordinary
test and INR
consultations
0.108
0.125
-0.0181**
-0.0157***
-0.00146
-0.00392
-0.00137***
-0.00157***
Model 7:
Cognitive
therapy and
all groups of
services
0.0827
-0.0177***
-0.0000296
-0.00122***
Model 8:
Model 7
with
controls
0.0985
-0.0175***
-0.00241
-0.00127***
Model 9:
Model 8
with
dummies for
all practices
0.0936
0.0210
0.0392***
-0.00196***
-0.0347***
0.0228***
-0.0158
0.0105
-0.0424***
0.0271***
-0.0217*
0.0152*
-0.0347***
0.0228***
0.0158
0.0105
-0.0424***
0.0271***
-0.0217*
0.0152*
-0.0347***
0.0228***
-0.0158
0.0105
-0.0424***
0.0271***
-0.0217**
0.0152**
0.065
1285
0.00000151
0.0000870**
0.00622***
0.00000296
0.0700***
0.093
1285
0.0000213***
0.0000994
-0.0104***
0.0000349***
Dropped
0.57
1285
-0.0390**
0.0254**
-0.0580***
0.0360**
-0.0347***
0.0228***
0.025
257
0.065
257
0.00000420
0.00109
0.00440
-0.000000649
-0.0784
0.092
0.12
257
257
0.000000268
0.0000543
0.00702**
0.00000452
-0.0705**
0.083
771
0.00000173
0.00114
0.00403
0.00000393
-0.0781**
0.13
514
Note: * p<0.10 ** p<0.05 *** p<0.01. All numbers are measured in 2006, except the unemployment (2003). Tolerance for list size in model 8 is 0.0184. Results for un-standardized
data and data controlled for the gender and age of the patients are approximately the same, expect that the list size do not significantly affect the number of services in the last models.
15