Managing waiting times in diagnostic
medical imaging
Sabina Nuti, Milena Vainieri
To cite: Nuti S, Vainieri M.
Managing waiting times in
diagnostic medical imaging.
BMJ Open 2012;2:e001255.
doi:10.1136/bmjopen-2012-
001255
▸ Prepublication history and
additional material for this
paper are available online. To
view these files please visit
the journal online
(http://dx.doi.org/10.1136/
bmjopen-2012-001255).
Received 4 April 2012
Revised 8 November 2012
Accepted 12 November 2012
This final article is available
for use under the terms of
the Creative Commons
Attribution Non-Commercial
2.0 Licence; see
http://bmjopen.bmj.com
Laboratorio Management
e Sanità, Institute of
Management, Scuola
Superiore Sant’Anna, Pisa,
Italy
Correspondence to
Dr Milena Vainieri;
m.vainieri@sssup.it
ABSTRACT
Objective: This paper aims to analyse the variation in
the delivery of diagnostic imaging services in order to
suggest possible solutions for the reduction of waiting
times, increase the quality of services and reduce
financial costs.
Design: This study provides a logic model to manage
waiting times in a regional context. Waiting times
measured per day were compared on the basis of the
variability in the use rates of CT and MRI examinations
in Tuscany for the population, as well as on the basis
of the capacity offered with respect to the number of
radiologists available. The analysis was performed at
the local health authority level to support the decision-
making process of local managers.
Setting: Diagnostic imaging services, in particular the
CT and MRI examinations. The study involved all the
12 local health authorities that provide services for 3.7
million inhabitants of the Italian Tuscany Region.
Primary and secondary outcome measures:
Participants: the study uses regional administrative
data on outpatients and survey data on inpatient
diagnostic examinations in order to measure
productivity.
Primary and secondary outcome measures: The
study uses the volumes per 1000 inhabitants, the days
of waiting times and the number of examinations per
radiologist. Variability was measured using the
traditional SD measures.
Results: A significant variation in areas considered
homogeneous in terms of age, gender or mortality
may indicate that the use of radiological services is not
optimal and underuse or overuse occurs and that there
is room for improvement in the service organisation.
Conclusions: Considering that there is a high level of
variability among district use rates and waiting times,
this study provides managers with a specific tool to
find the cause of the problem, identify a possible
solution, assess the financial impact and initiate the
eventual reduction of waste.
BACKGROUND
The epidemiological changes of the last
30 years have caused a reduction in acute
care for populations and an exponential
growth in the number of outpatient and
diagnostic services. Moreover, technological
innovations in the diagnostic sector have
made services more efficient, but at the same
time more expensive, thus increasing costs.
Citizens and physicians increasingly
request diagnostic services, often without
considering their possible negative effects,
ARTICLE SUMMARY
Article focus
▪ Which factors explain the variability in waiting
times and in the use of CT and MRI examination
rates? This article aims to analyse the relation-
ships among radiologists’ productivity, use rates
and waiting times in the Tuscan Region.
▪ Moreover, it proposes a logical tool to help man-
agers deal with this complex issue.
Key messages
▪ The results reveal a high level of variability
among the disability insurance use rates of local
health authorities.
▪ The factors that may affect these results do not
involve private providers or substitution effects
between similar procedures.
▪ Monitoring the appropriateness phase of the pre-
scription becomes critical when it is difficult to
ensure acceptable waiting times which could be
due to high volumes of the diagnostic imaging
services delivered or may depend on inefficiency
or a lack of professional resources (eg, the
number of radiologists).
▪ In Tuscany there is no correlation among scans/
radiologists, volumes and waiting times.
▪ This could lead to the hypothesis that the man-
agement of waiting times cannot be generalised
and depends on other factors that are strictly
related to the local organisational decisions.
▪ Finally, the paper proposes a logical framework
to help policy makers and managers cope with
waiting times and appropriateness.
Strengths and limitations of this study
▪ This study analyses the variation on diagnostic
imaging services throughout different perspec-
tives (volumes, waiting times and productivity).
▪ It provides policy makers with a logical model to
manage this variation.
▪ Limitations regard the generalisation of results,
as part of them may be referred only to the
organisational features of the region analysed (as
for the productivity).
Nuti S, Vainieri M. BMJ Open 2012;2:e001255. doi:10.1136/bmjopen-2012-001255 1
Open Access Research
 group.bmj.com on December 17, 2012 - Published by bmjopen.bmj.comDownloaded from 
such as radiation caused by CT examinations. It appears
that the offer of diagnostic services does not cover the
demand from patients, thus causing waiting lists to be
increasingly longer and patients to be highly unsatisfied.
Policy makers must therefore face a complex situation
resulting from longer waiting times, the increasing
demand of diagnostic services from patients and increas-
ing costs.
Which mechanisms may be adopted in order to face
such situations and assist managers of public health
systems to identify the causes of the problem and pos-
sible solutions?
On the one hand, the regional health system, with a
universal coverage mission, should provide an adequate
number of diagnostic imaging (DI) services to grant
equity of access to all citizens and should deliver such
services in a timely manner and according to patients’
needs. On the other hand, it should reduce the patients’
radiation exposure and the inappropriate duplication of
examinations as much as possible.
The WHO argues that the use rates of DI services
should be adapted to the local needs and should
be determined by (1) the type and size of the hospital;
(2) the number and type of patients: disease burden,
inpatients and outpatients and (3) the therapeutic
capabilities. Guyatt et al1 underlined the importance of
accounting for the reassuring effect of an investigation
on the wellness of a worried patient, while Hendel2 sug-
gested that intangible factors, local practice and clinical
judgement must be carefully considered in the DI pro-
cedure appropriateness assessment.
Certainly, the huge growth of DI procedures
observed in the last few years raises concern about a
possible overuse of these services.2–5 According to the
European referral guidelines for imaging, the causes
of DI service overuse are multiple: repeated investiga-
tions, investigations performed although unlikely to
affect patient management and premature or incorrect
investigations.
Miller3 and Lysdahl and Børretzen6 observed that
the number of supplied DI services is very different
among geographical areas and that, often, geograph-
ical areas that supply a larger number of DI services
do not present better health outcomes for the inhabi-
tants. Song et al7 reported that there was no evidence
of a survival benefit among people who moved to
regions with a higher-intensity level of procedures
compared to those who moved to lower-intensity
regions.
These results suggest that the analysis of the DI ser-
vices variability across geographical regions could be an
important step towards the understanding of the
primary determinants of DI procedure growth rates and
towards a definition of appropriate use standards. A sig-
nificant variation in areas considered homogeneous in
terms of age, gender or mortality may indicate that the
use of radiological services is not optimal and underuse
or overuse occurs.
This paper presents the results of a research project
aiming at exploring the factors that explain the variabil-
ity in waiting times and the use of CT and MRI examin-
ation rates in Tuscany. The final purpose of the project
is to support the regional administration planning
process regarding resource allocation and to set stan-
dards and gals for local health authorities (LHAs).
TUSCAN CONTEXT AND THE RESEARCH QUESTIONS
The Italian National Health System is based on the
principle of universal coverage and it is financed
by general taxation. Following the decentralisation
process that started in the 1990s, regions are respon-
sible for organising and providing healthcare services,
while the national level has to ensure universal cover-
age for the whole population. In total, 82% of health-
care expenditure is public (Source: OECD data 2009).
The remaining 18% of private expenses for healthcare
mainly concerns dental care and some other few
specialist visits, copayment and drugs. Healthcare ser-
vices can be provided by both private and public insti-
tutions, their mix varies within Italian regions. In
Tuscany over 90% of services are provided by public
institutions.
In particular, CT and MRI examinations are mainly
covered by public expenditure and the few private provi-
ders of diagnostic imaging services work under contract
with the public health authorities. The last Italian survey
on citizens’ behaviour and consumptions, carried out
on 2005 by the Italian National Institute of Statistics,
demonstrated that more than 80% of diagnostic services
are covered by public expenditure and copayment. The
percentage of DI covered by out of pocket varies across
Italian regions. In Tuscany the percentage is about 16%
(vs the Italian average of 20%), thus only a minor
number of DI services is not registered into the regional
administrative data.8
The Tuscan region reallocates resources among LHAs
using the regional capitation formula. Hence, LHAs are
responsible for the resource allocation process of all the
healthcare services. To achieve this task, LHAs are in
charge of organising the supply structure and conse-
quently they define the number of specialists and the
equipment to be dedicated to diagnostic imaging.
The overall economic value of services such as CT and
MRI in Tuscany is around 65 million Euros (about 1%
of the regional budget). The volume of these services
for the 3.7 million of residents is one of the highest in
Italy (Italian Ministry of Health www.salute.gov.it).
Despite high volumes, the actual offerings in Tuscany
seem to not be enough.
In 2010, waiting times in Tuscany were more than
60 days on average; however, for some LHAs, waiting
times reached up to 90 days; variability appeared to be
very great: waiting times went from 14 to 260 days.
Moreover, citizens claim that waiting times are quite
long; this is the main reason why some of them choose
2 Nuti S, Vainieri M. BMJ Open 2012;2:e001255. doi:10.1136/bmjopen-2012-001255
Managing variation in diagnostic medical imaging
 group.bmj.com on December 17, 2012 - Published by bmjopen.bmj.comDownloaded from 
the private supplier (see, for instance, the results of
2005 survey directed to population9).
Tuscan policy makers, therefore, consider waiting
times to be one of the most important challenges to
achieve, also because the National Government
requested these diagnostic services to be delivered to
patients within 30 days (Piano Nazionale di Governo
delle Liste di Attesa—PNGLA 2010–2012).
As regards the use rates per inhabitant standardised
by age and sex, great variability (for MRI and CT) was
registered in 2009 and 2010: CT use rates went from 45
to 88 per 1000 inhabitants, while the MRI use rates went
from 56 to 83 per 1000 inhabitants.
The results indicate that, although Tuscany is a homo-
geneous territory from a sociodemographic perspective
and represents excellence within the Italian regions in
terms of quality of healthcare and governance,10 there is
great variability across the region in terms of both
waiting times and DI use rates.
Geographical variation in the DI use rates may consti-
tute overuse or underuse with a consequent risk of
inappropriateness of the service.6 Bhargavan and
Sunshine11 highlighted great variability in the provision
of DI services across areas and suggested that the use of
appropriateness criteria, such as those defined by the
American College of Radiology (ACR), may minimise
these differences. Moreover, they explored this variability
to highlight the state-level variables that affect it most,
and they observed that the greater the number of
Medicare providers, the greater the DI investigation rate
per 1000 inhabitants. However, it is worth highlighting
that the variability across regions could be affected by
other factors like the availability of diagnostic technolo-
gies, socioeconomic factors (ie, education and income)
or the number of radiologists in the region.6 12
Moreover, Lysdahl and Børretzen6 observed that each
area may present a propensity towards a particular pro-
cedure considered a reasonable approach for a specific
indication, which may lead each area to present a substi-
tution effect among procedures types. On the contrary,
it is found that the high use of one modality does not
correspond with low use of an alternative modality for
specific organs (locations), supporting the assumption
that overuse really exists in high-use areas, thus leading
to potentially inappropriate resource allocation.
In 2010, the Tuscan region evaluated as inappropriate
district areas that registered use rates of the diagnostic
imaging services far from the regional median use rate.
Those districts may face the risk of an excessive
number of radiation exposures (in the case that they
are above the median) or a lack of services (in the case
that they are lower than the median).13 This assump-
tion yields some disadvantages because it does not take
into account differences in medical procedures, where
any decision involves a certain degree of discretion,
the disease burden of particular areas or patient
preferences and outcomes.14 However, Lysdahl and
Børretzen6 observed that the number of supplied DI
services did not correspond to better health outcomes
for inhabitants. Moreover, Song et al7 found that the
mortality rate was not lower in areas presenting a
high intensity of practices, as one could expect.
Low-intensity and high-intensity areas present similar
outcomes and, after 3 years, there is no evidence of a
survival benefit among people who moved to higher-
intensity regions compared to those who moved to
lower intensity regions.
Considering these premises, the present research
investigated the relationship between volumes and
waiting times to find out if long waiting times are deter-
mined by high volumes of diagnostic services delivered
to residents, as well as to assess the impact of factors
such as the presence of private suppliers and providers.
Some authors15 16 suggest that, when coping with vari-
ation in healthcare, managers have to show, discuss and
monitor data to question professional discretion. Hence,
we analysed the following issues:
RQ1: Does variability in the DI rate depend on the
presence of private medical providers?
RQ2: Is there a substitution effect among diagnostic
procedures or a problem of procedure mix?
RQ3: What is the relationship between the volumes
delivered and the waiting times?
The most common strategy to reduce waiting times is
to increase the supply throughout, on the one hand
the enhancement of the production capacity (ie, boost-
ing the opening hours of scans to perform more exami-
nations per scan and increase the number of
examinations per radiologist), and on the other hand
the increase in personnel/equipment or the contract-
ing out.17 Boosting the supply structure may increase
the volumes (indeed, capacity is one of the factors
that could explain the variability, as reported by
scholars6 12 14 18).
RQ4: Does the number of radiologists and scans avail-
able for each LHA or the percentage of services con-
tracted out affect volumes and waiting times?
RQ5: Which tool may support policy makers and man-
agers to cope with demand and waiting times?
This paper presents some first evidence regarding
the questions suggested and proposes a method to
enhance the professional consciousness of specialists
towards better resource allocation and performance
management.
METHODS
This study is based on empirical analyses. Data sources
are both administrative data and surveys. Outpatient
dataset includes all DI services provided to Tuscan inha-
bitants (in or out of the region and by public or private
institutions) with the only exception of those examina-
tions full paid by patients. According to the last popula-
tion survey conducted by the Italian National Institute of
Statistics (Istat), in Tuscany the percentage of these
examinations should be around 16%.
Nuti S, Vainieri M. BMJ Open 2012;2:e001255. doi:10.1136/bmjopen-2012-001255 3
Managing variation in diagnostic medical imaging
 group.bmj.com on December 17, 2012 - Published by bmjopen.bmj.comDownloaded from 
To detect the first five research questions we run
Pearson correlation and analysis of variance (ANOVA)
analyses. In particular, we correlated the use rates and
the percentage of examinations provided by private
institutions for the RQ1, while we correlated the use
rates of four couples of procedures for the RQ2. The
four couples of potential substitute procedures were
selected by radiologists and technical staff considering
only the CT and MRI procedures and they are: the CT
and MRI of the superior abdomen, the CT and MRI
of the head and brain, the CT and MRI of the rachis
and spine or the CT and MRI of the facial massive. We
performed the one-way ANOVA to detect the variabil-
ity across and within the CT (MRI) procedures’ use-
rates (RQ2). Further analyses for RQ2 were executed
considering the 20 more frequent examinations of CT
and MRI. This group approximately constitutes 90% of
the total CT and MRI examinations performed in
Tuscany in 2009. In addition to this analysis we pro-
vided a map of the number of examinations that
exceed the regional median use rate per procedure
across Tuscan LHAs in terms of financial value,
following an approach similar to the one adopted by
Nuti et al.19
For RQ4 we took into consideration: the number of
radiologists and scans per inhabitants, the productivity
scores of radiologists and the percentage of private ser-
vices contracted out.
In particular, to calculate productivity indices two
steps were followed.
The first step was to take into account the recommen-
dations of the workload table developed by the Italian
National Scientific Community of Radiologists published
into an Italian report of 2006.20 In this report radiolo-
gists, on the basis of their expertise and experience,
argued that the time spent running examinations for
young patients (less than 5 years old) is 25%
(weight=1.25) greater than the time spent for patients
between 5 and 79 years, while the time spent for elderly
people (older than 79 years) is 15% (weight=1.15).
Moreover, patients coming from the emergency depart-
ment require the presence of radiologists and techni-
cians for a longer period of time (estimated at 25%,
weight=1.25), while inpatients require 15%
(weight=1.15) of time more than outpatients. The cor-
rected number of examinations was estimated using
these weights. Other aspects presented in the workload
report refer to some organisational issues, such as the
changing room for patients or the presence of nurses,
that can optimise the use of scans and personnel time.
The research group considered these factors as part of
the productivity that can be managed by LHAs, thus the
only correction applied to efficiency indices refers to
patient characteristics.
The second step regards the personnel. We conducted
a survey collecting all personnel working in the DI
departments. The personnel dedicated to CT (MRI) ser-
vices was estimated using the workload table of the 2006
reporti for the current DI examinations (both inpatients
and outpatients) per health authority. This table reports
the standard time radiologists spent to execute DI exam-
inations. The number of radiologists who deal only with
CT (MRI) was estimated using the percentage of CT
(MRI) examinations (in terms of time) and the overall
working time. The application of these weights and this
deductive process of personnel identification were dis-
cussed with the research group as well as with the
Italian National Scientific Community of Radiologists
(SNR-SIRM) in 2011.
For the last research question, we followed the guide-
lines of an ‘interventionist research approach’. This
approach aims to solve problems through the construc-
tion of models, diagrams, plans, organisations, etc, by
means of the direct involvement of researchers and
actors in ‘participant observations’ in the field.21 This
method is used in a variety of fields: technical sciences,
mathematics, operation analysis, clinical medicine and
management control.22
Professionals were involved in detangling the DI
variability and the management of waiting times.
Radiologists, technical staff and management staff (such
as the health and cost analysts) were involved in the
research project.
Researchers facilitated the process, conducted the
project while pointing out the questions and the
research hypothesis, looked for articles that may support
the perceived determinants of variability, collected data
and ran statistical analyses to help professionals identify
critical factors.
The calculations were based on the Tuscan outpatient
dataset and on the Health Authority data for DI inpati-
ents services, scans and personnel (collected by
researchers via surveys).
RESULTS
The starting point of this research was the observation of
the large variation in the use rates of outpatient CT and
MRI procedures in Tuscany across the LHAs, which is even
more drastic across districts. Figure 1 reports the appraisal
made by the Tuscan Health System on the CTand MRI use
rates in 2009. The colours identify the evaluation on the
basis of the distance from the median use rate: better
results are positioned closer to the median rate while worst
results are positioned farther from the median because of
the increasing risk of over/under use. The appraisal is
organised into five coloured bands: very good (dark
iThe table used is at page 12 of the report available on internet
(only in Italian, http://www.asppalermo.org/Archivio/circolari/dip_
radiologia/metodo_nomencl_nuovo.pdf access September 2012)). In
this report there are 16 groups of examinations, each group has the
time requested to the specialist for the execution in terms of minutes
and in comparison with the time spent for the chest x-ray that is the
reference exam of this table. For instance, the chest x-ray requires
7.1 min, while the CT without contrast requires 19.8 min (2.8 times
longer than the time required for the chest x-ray).
4 Nuti S, Vainieri M. BMJ Open 2012;2:e001255. doi:10.1136/bmjopen-2012-001255
Managing variation in diagnostic medical imaging
 group.bmj.com on December 17, 2012 - Published by bmjopen.bmj.comDownloaded from 
green); good (green); medium (yellow); bad (organge)
and very bad (red) performance.
Was this difference determined by the presence of
private institutions? (RQ1) The correlation analysis
between the use rates and the percentage of examina-
tions provided by private institutions demonstrates that
high use rates do not significantly depend on the private
sector’s activity: for MRI the correlation was r=−0.11
with a p=0.727 and for CT the correlation was r=0.072
with a p=0.823(see table 4). The fact that the presence
of the private sector does not influence the variability
confirms that it is not up to private providers to increase
volumes, because their production is negotiated with the
Tuscan LHAs which decide the percentage of services to
be provided outside the public structure.
Regarding RQ2, scholars suggest that variability could
be due to substitution effects between different modal-
ities. Low-use rates of one modality in the examination
of specific organs could generally correspond to high
use rates of an alternative modality. However, data do
not support this hypothesis; the correlation matrix
between two possibly substitutable modalities of analysis
indicates that there is no statistically significant correl-
ation between them (table 1). A significant negative cor-
relation would have suggested that high rates of one
modality correspond to low rates of the other, suggesting
a likely substitution effect between them.
This result is comforting because there should be spe-
cific guidelines on the use of each diagnostic modality.
The correlation matrix (table 1) also shows that there is
positive and significant correlation between the use of
some procedures, such as MRI backbone and superior
abdomen, across the four groups. Similar results could
be found in the literature.6
Variability across Health Authorities and their district
areas could depend on the over (under) use of specific
examinations. Indeed, the one-way ANOVA analysis high-
lights that there is more variability between procedures
(around 90% for MRI and 71% for CT) than within
them (see table 2).
Hence, the overall variability is mainly due to a differ-
ent mix of procedures applied by the districts. Looking
at the SD across districts for the use-rates of the 20 most
recurring procedures, it emerged that procedures with
the highest level of variability across local areas are: MRI
musculoskeletal; MRI backbone; CT rachis and CT head
(see table 3).
Online supplementary appendix 1 for CT and online
supplementary appendix 2 for MRI examinations high-
lights the critical area of inappropriateness for each
Figure 1 CT and MRI use rates per 1000 inhabitants in the Tuscan districts.
Nuti S, Vainieri M. BMJ Open 2012;2:e001255. doi:10.1136/bmjopen-2012-001255 5
Managing variation in diagnostic medical imaging
 group.bmj.com on December 17, 2012 - Published by bmjopen.bmj.comDownloaded from 
district, providing managers with the amount of leeway
they could obtain if they performed at the regional
median.
For example, in the Apuane district, 90% of its leeway
is concentrated on the musculoskeletal MRI, while there
is no leeway for some other procedures (such as the
Backbone MRI with contrast) because the use-rate is
equal or lower than the regional median. At the regional
level the resources that can be reallocated for MRI
(around 5.5 million Euros) are concentrated (57%) on
the musculoskeletal and backbone examinations, while
42% of resources for CT (around 6 million Euros) are
concentrated on the rachis and complete abdomen with
contrast CT examinations.
Once we found out some of the factors that could
affect variability in terms of volumes, we looked at the
relationship between volumes delivered and waiting
times (RQ3). In both the CT and MRI matrix LHAs
high use rates are not correlated with longer waiting
times. Indeed, the Pearson correlation (table 4) reports
an r=0.238 and p=0.455 for CT and an r=0.11 and
p=0.712 for MRI.
As for the RQ4, table 4 shows that for both CT and
MRI examinations the percentage of services delivered
by private institutions, the number of scans and radiolo-
gists and their efficiency are not correlated with waiting
times. Instead, correlations among capacity factors and
volumes are different for CT and MRI. In the case of
MRI capacity, radiologists and scans per inhabitants and
their efficiency are positively correlated with public-use
rate. Moreover, the significant negative correlation
between the percentage of services delivered by private
institutions and the public use rates can be interpreted
as a designed strategy of contracting out where private
suppliers integrate public offerings.
Unlike MRI services and other studies on variability,14 18
CT services in Tuscany seem not to be supply sensitive:
the number of radiologists, technical staff per inhabitant,
scans and the volumes per inhabitant provided by public
providers appear not to influence the demand. It is
worth noting that radiologists are employed by LHAs
and volumes do not influence their salary.
As regards the strategy of increasing the capacity to
reduce waiting times, this seems not to be effective. In
particular, it is expected that the contracting out is a
strategy applied by LHAs once their production capacity
is saturated. Indeed, data show (table 4) that a higher
percentage of services delivered by private providers cor-
responds to a lower radiologists productivity score (−0.8
for MRI and −0.7 for CT both with a p<0.01).
Thus, the recourse to private suppliers appears not to
be cost effective in Tuscany. LHAs with low productivity
Table 1 Substitution effect between four couples
Couples Investigations
CT facial
massive
MRI facial
massive
MRI
backbone
CT
rachis
CT
head
MRI
brain
CT superior
abdomen
MRI
superior
abdomen
1 CT facial
massive
1.00
1 MRI facial
massive
−0.18 1.00
2 MRI backbone −0.11 0.69* 1.00
2 CT rachis 0.13 −0.11 −0.25 1.00
3 CT head 0.44* −0.13 −0.18 0.36* 1.00
3 MRI brain −0.05 0.23 0.21 0.15 0.14 1.00
4 CT superior
abdomen
0.43* 0.11 0.30 0.16 0.20 0.04 1.00
4 MRI superior
abdomen
−0.18 0.56* 0.71* 0.09 −0.21 −0.07 0.31 1.00
*p<0.01
Table 2 Analysis of variance of both CT and MRI use rates (groups are the procedures)
Source SS Per cent df MS F Prob>F
MRI Between groups 0.030646 90 31 0.000989 265.17 0
Within groups 0.003571 10 958 3.73E-06
Total 0.03422 100 989 3.5E-05
CT Between groups 0.005658 71 27 0.00021 74.94 0
Within groups 0.002324 29 831 2.80E-06
Total 0.00798 100 858 9.30E-06
MRI Bartlett’s test for equal variances: χ² (31)=4.1e+03 Prob>χ²=0.000
CT Bartlett’s test for equal variances: χ² (27)=1.5e+03 Prob>χ²=0.000
6 Nuti S, Vainieri M. BMJ Open 2012;2:e001255. doi:10.1136/bmjopen-2012-001255
Managing variation in diagnostic medical imaging
 group.bmj.com on December 17, 2012 - Published by bmjopen.bmj.comDownloaded from 
per radiologist could increase the number of examina-
tions delivered by their public structures reducing the
recourse to private providers. Hence, if all the radiolo-
gists working in public institutions had reached the
maximum level of productivity, the reduction of exami-
nations delivered by private institutions would have led
to savings. Considering the actual number of scans at
their maximum level of productivity and the fares of
examinations, these savings would have reached up to 10
million Euros (6 million for CT and 5 million for MRI).
In conclusion, the overall Pearson correlation matrix
(table 4) highlighted that waiting times do not correlate
with the factors selected (volumes, capacity and effi-
ciency). Similar results could be obtained performing
the ANOVA analysis on waiting times and volumes, cap-
acity and efficiency (adjusted R2 is 18%, residuals are
greater than 50% and no factor has a p<0.05).
The fact that there is no correlation between the
scans/radiologists, volumes and waiting times could
lead to the hypothesis that the management of waiting
times cannot be generalised and will depend on factors
that are strictly related to the local organisational
decisions.
For the last research question (which tools are neces-
sary to help managers coping with volumes and waiting
times), the research team developed a two-dimensional
matrix where the x-axis shows the use rate (the volumes
per inhabitants) and the y-axis reports the waiting times
for CT or MRI.
Using regional medians, the matrix identifies four
quadrants.
1. Short waiting times and high volumes per inhabitant
2. Short waiting times and low volumes delivered
3. Long waiting times and high volumes per inhabitant
4. Long waiting times and low volumes delivered
This first classification yields the following hypothesis:
The institutions that belong to the first quadrant could
risk delivering inappropriate services and/or have an
excessive amount of resources at their disposal.
The institutions belonging to the second quadrant could
risk substantially decreasing their supply of services or
facing a problem in the quality of their services if
their citizens decided to seek such services at other
institutions.
The institutions that belong to the third quadrant could
face problems in terms of appropriateness and pro-
duction efficiency (Figure 2).
Finally, the institutions belonging to the forth quadrant
could face difficulties in terms of efficiency or
inappropriate amount of resources.
Figure 3 illustrates the matrix of both CT and MRI
volumes and waiting times. Results and conclusion
coming from the analysis of the matrix and correlation
for CT and MRI are similar. The figure 3 shows that
LHAs are positioned in all the four quadrants for both
CT and MRI; this highlights that LHAs can face different
problems. Using the above logical framework, some
LHAs (eg, 106 and 104 for CT or 102 and 107 for MRI)
positioned in the third quadrant (high volumes and
high waiting times) could face problems in terms of
appropriateness and (low) efficiency. Those LHAs posi-
tioned in the forth quadrant (low volume and high
waiting times) may face problems relating to their cap-
acity (few personnel and few scans) or (low) efficiency.
CONCLUSIONS
This paper provides an analysis of the variability in the
use rates of CT and MRI examinations in Tuscany. The
analysis has been performed at the LHA level (including
district areas) to support the decision-making process of
local managers.
Results reveal a high level of variability among the
LHAs’ (and district areas) use rates. Why do some resi-
dents use these DI services much more than others?
Factors that may affect these results do not involve
private providers or substitution effects between similar
procedures (correlation analyses were not significant).
The ANOVA analysis showed that 71% for CT and 90%
for MRI of variability are explained between groups
(procedures); indeed, the analysis of procedure mix
indicates that there are some procedures (eg, CTs of
the head) with high level of standard deviations. Thus,
the relevant issue is about how to share and increase the
responsibility of general practitioners and specialists in
the prescription phase.
Table 3 List of use rate procedures with the highest SD
Use rates of
Across districts SDs
2009
MRI musculoskeletal 7.07
MRI backbone 6.95
CT rachis 5.02
CT head 3.6
CT complete abdomen with
contrast
2.92
CT superior abdomen with
contrast
2.55
CT chest with contrast 2.1
CT chest 2.01
CT lower extremity 1.67
MRI brain 1.6
MRI brain with contrast 1.56
CT facial massive 1.35
CT head with contrast 0.91
MRI backbone with contrast 0.72
CT neck with contrast 0.63
MRI facial massive with contrast 0.49
MRI musculoskeletal with
contrast
0.48
Angio MRI 0.43
MRI superior abdomen with
contrast
0.36
MRI facial massive 0.34
Nuti S, Vainieri M. BMJ Open 2012;2:e001255. doi:10.1136/bmjopen-2012-001255 7
Managing variation in diagnostic medical imaging
 group.bmj.com on December 17, 2012 - Published by bmjopen.bmj.comDownloaded from 
Table 4 Overall correlation matrix among volumes, efficiency, capacity, waiting times and recourse to private providers
MRI CT
Efficiency Capacity Private Volumes Times Efficiency Capacity Private Volumes Times
S
c
a
n
e
f
f
i
c
i
e
n
c
y
R
a
d
i
o
l
o
g
i
s
t
e
f
f
i
c
i
e
n
c
y
S
t
a
f
f
e
f
f
i
c
i
e
n
c
y
S
c
a
n
p
e
r
i
n
h
a
b
i
t
a
n
t
s
R
a
d
i
o
l
o
g
i
s
t
p
e
r
i
n
h
a
b
i
t
a
n
t
s
S
t
a
f
f
p
e
r
i
n
h
a
b
i
t
a
n
t
s
P
e
r
c
e
n
t
a
g
e
o
f
s
e
r
v
i
c
e
d
e
l
i
v
e
r
e
d
b
y
p
r
i
v
a
t
e
U
s
e
r
a
t
e
s
P
u
b
l
i
c
u
s
e
r
a
t
e
s
W
a
i
t
i
n
g
T
i
m
e
s
S
c
a
n
e
f
f
i
c
i
e
n
c
y
R
a
d
i
o
l
o
g
i
s
t
e
f
f
i
c
i
e
n
c
y
S
t
a
f
f
e
f
f
i
c
i
e
n
c
y
S
c
a
n
p
e
r
i
n
h
a
b
i
t
a
n
t
s
R
a
d
i
o
l
o
g
i
s
t
p
e
r
i
n
h
a
b
i
t
a
n
t
s
S
t
a
f
f
p
e
r
i
n
h
a
b
i
t
a
n
t
s
P
e
r
c
e
n
t
a
g
e
o
f
s
e
r
v
i
c
e
d
e
l
i
v
e
r
e
d
b
y
p
r
i
v
a
t
e
U
s
e
r
a
t
e
s
P
u
b
l
i
c
u
s
e
r
a
t
e
s
W
a
i
t
i
n
g
T
i
m
e
s
Efficiency Scan
efficiency
1 1
Radiologist
efficiency
−0.0788 1 0.2216 1
0.8078 0.4888
Staff efficiency −0.5275 0.8131 1 0.4521 0.817 1
0.078 0.0013 0.14 0.0012
Capacity Scan per
inhabitants
−0.4495 0.8416 0.9104 1 −0.6173 0.4171 0.3067 1
0.1427 0.0006 0 0.0325 0.1773 0.3322
Radiologist per
inhabitants
0.1887 0.4507 0.3739 0.596 1 −0.0299 −0.8122 −0.4847 −0.1716 1
0.557 0.1414 0.2312 0.0408 0.9265 0.0013 0.1103 0.5939
Staff per
inhabitants
0.6341 0.4748 0.02 0.2942 0.7496 1 −0.3789 −0.6572 −0.8259 −0.057 0.6346 1
0.0268 0.1189 0.9508 0.3533 0.005 0.2246 0.0202 0.0009 0.8604 0.0266
Private Percentage of
service
delivered by
private
0.28 −0.8058 −0.8455 −0.9191 −0.741 −0.4352 1 −0.1636 −0.7513 −0.7322 −0.5105 0.522 0.6129 1
0.3781 0.0016 0.0005 0 0.0058 0.1573 0.6114 0.0049 0.0068 0.0899 0.0817 0.0341
Volumes Use rates 0.5402 0.5387 0.2023 0.1772 0.192 0.4798 −0.1129 1 0.0957 −0.4271 −0.2436 −0.2243 0.4802 0.1344 0.0725 1
0.0698 0.0707 0.5283 0.5816 0.55 0.1144 0.7268 0.7674 0.1662 0.4456 0.4834 0.1141 0.6772 0.8228
Public use
rates
−0.1335 0.9033 0.8429 0.9149 0.7373 0.5395 −0.9633 0.3634 1 0.263 0.3699 0.5014 0.2821 −0.1249 −0.4652 −0.8201 0.4483 1
0.6792 0.0001 0.0006 0 0.0062 0.0702 0 0.2457 0.4089 0.2366 0.0968 0.3743 0.6988 0.1275 0.0011 0.1439
Waiting
times
Waiting times −0.1195 0.1737 0.188 0.2542 0.0059 −0.0321 −0.0568 0.2384 0.1315 1 −0.0268 −0.1965 −0.1096 −0.274 0.0866 −0.0078 0.4088 0.1193 −0.3175 1
0.7113 0.5892 0.5584 0.4253 0.9856 0.9211 0.8607 0.4556 0.6837 0.9341 0.5404 0.7346 0.3887 0.789 0.9809 0.1871 0.712 0.3145
p Values in italics.
8
NutiS,VainieriM
.BM
J
Open
2012;2:e001255.doi:10.1136/bm
jopen-2012-001255
M
a
n
a
g
in
g
v
a
ria
tio
n
in
d
ia
g
n
o
s
tic
m
e
d
ic
a
l
im
a
g
in
g
 
group.bmj.com
 o
n
 D
ecem
ber 17, 2012 - Published by 
bmjopen.bmj.com
D
ow
nloaded from
 
The monetary value attached to the examinations that
exceed the median use rates suggests that there is a
broad margin of intervention and the need for policy
makers and managers to find new and more effective
ways to control appropriateness.
This analysis of use rates is a fundamental step to cope
with long waiting times, but it is not enough. Strategies
adopted to cope with waiting times usually concern the
enhancement of capacity throughout efficiency, the
number of scans and radiologists per inhabitants and
the contracting out.
This paper proposes a logical approach to identify the
critical areas for controlling variability in use rates and
to find out if waiting times are determined by inefficien-
cies, a lack of appropriateness in the prescription phase
or a lack of professional resources. The final aim of the
paper is to help decision makers define the priorities of
intervention. Financial considerations were also added
with the aim to both enhance the specialists’ compe-
tence and the economic consciousness through focus
groups, and exploit the learning opportunities by com-
paring best practices.
The empirical analyses highlighted for both the CT
and MRI examinations that waiting times in Tuscany are
not affected by volumes or capacities. Moreover, unlike
MRI and other studies on specialist care, it seems that
the CT is not supply sensitive. Hence, the management
of waiting times cannot be generalised and depends on
other factors that are strictly related to the local organ-
isational decisions. Moreover, this analysis highlighted
that the Tuscan Region has to tackle with variation in
volumes and high waiting times optimising the product-
ivity of personnel and scans; this t can lead to a
reduction of costs (as a consequence of a reduction of
contracting out).
The matrix supports in this paper helped policy
makers and top managers to analyse the complex task of
coping with long waiting times and appropriateness.
Even if there are some limitations in generalising the
results described in this paper, since they could be
affected by macro (eg, public system based on universal
coverage) and micro (eg, the supply structure) factors
linked to the Tuscan context, the matrix proposed can
be applied outside this context. The matrix and its
logical framework may represent a practical managerial
tool that supports the difficult and multi factors analysis
of waiting times and appropriateness in delivering out-
patient services.
Acknowledgements We thank Dr Claudio Vignali (Director of the radiology
department of the LHA 12 of Viareggio), Daniele Di Feo responsible for the
radiologist technical staff of Meyer teaching hospital) and all the LHAs’
management for their worthy suggestions and collaboration. We also thank
the participants of the ‘Wennberg International Collaborative Conference’ held
in London in September 2010. A particular acknowledgement to Professor
Gwyn Bevan and Professor David C Goodman for their comments on the
earlier version of this paper.
Contributors SN and MV conceived and designed the research. MV analysed
the data. SN and MV wrote the paper. SN and MV contributed to the
interpretation of data. SN and MV approved the final version of the
manuscript.
Funding Funded by Tuscany Region, Italy.
Competing interests None.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement No additional data are available.
REFERENCES
1. Guyatt GH, Tugwell PX, Feeny DH, et al. A framework for clinical
evaluation of diagnostic technologies. CMAJ 1986;134:587–94.
2. Hendel RC. Utilization management of cardiovascular imaging:
pre-certification and appropriateness. J Am Coll Cardiol
2008;1:241–8.
3. Miller MM. MedPAC recommendations on imaging services.
Medicare Payment Advisory Commission, 2005.
4. Blackmore CC. Defining quality in radiology. J Am Coll Radiol
2007;4:217–23.
5. Smith-Bindman R, Miglioretti DL, Larson EB. Rising use of
diagnostic medical imaging in a large integrated health system.
Health Affairs 2008;27:1491–502.
6. Lysdahl KB, Børretzen I. Geographical variation in radiological
services: nationwide survey. BMC Health Ser Res 2007:7–21.
7. Song Y, Skinner J, Bynum J, et al. Regional variation in diagnostic
procedures. N Engl J Med 2010;363:45–53.
8. Cislaghi C, Giuliani F. Out of pocket sanitario nelle regioni italiane. I
uaderni di Monitor 2008;22:161–77.
9. Vainieri M. Che cosa influenza la soddisfazione dei cittadini toscani
nei servizi territoriali di base? Salute e Territorio 2006;156:137–56.
10. Censis. I modelli decisionali nella sanità locale. Roma, 2008.
11. Bhargavan M, Sunshine JH. Utilization of radiology services in the
United States: levels and trends in modalities, regions and
populations. Radiology 2005;234:824–32.
12. Lysdahl KB, Hofmann BM. What causes increasing and
unnecessary use of radiological investigations? A survey of
radiologists’perceptions. BMC Health Ser Res 2009;9:155.
13. Nuti S, Barsanti S. Il Sistema di valutazione della performance della
sanità toscana—REPORT 2009 Società della Salute/Zone-Distretto.
Pisa: Edizioni ETS, 2010.
14. Wennberg EJ. Tracking medicine. Oxford: University Press, 2010.
15. Bevan G, Hollinghurst S, Benton P, et al. Using information on
variation in rates of supply to question professional discretion in
public services. Financ Account Manage 2004;20:1–17.
Figure 3 The matrix for waiting times and volumes of CT
and MRI.
Figure 2 The logical framework to cope with long waiting
times and their relationship with volumes.
Nuti S, Vainieri M. BMJ Open 2012;2:e001255. doi:10.1136/bmjopen-2012-001255 9
Managing variation in diagnostic medical imaging
 group.bmj.com on December 17, 2012 - Published by bmjopen.bmj.comDownloaded from 
16. Lee TH. Turning doctors into leaders. Harvard Bus Rev 2010:50–8.
17. Emery DJ, Forster AJ, Shojania KG, et al. Management of MRI Wait
Lists in Canada. Healthc Policy 2009;4:76–86.
18. Appleby J, Raleigh V, Frosini F, et al. Variation in healthcare.
London: The King’s Fund, 2011.
19. Nuti S, Vainieri M, Bonini A. Disinvestment for re-allocation: a
process to identify priorities in healthcare. Health Policy
2010;95:137–43.
20. Sirm-Snr. Metodologia di determinazione dei volumi di attività e della
produttività dei medici radiologi- Nomenclatore Sirm—Snr Delle
Prestazioni Radiologiche. Genova: OMICRON, 2006.
21. Jonsson S, Lukka K. Doing interventionist research in management
accounting. Goteborg: Gothenburg Research Institute, 2005.
22. Kasanen E, Lukka K, Siitonen A. The constructive approach in
management accounting research. J Manag Acc Res 1993;5
(Fall):243–64.
10 Nuti S, Vainieri M. BMJ Open 2012;2:e001255. doi:10.1136/bmjopen-2012-001255
Managing variation in diagnostic medical imaging
 group.bmj.com on December 17, 2012 - Published by bmjopen.bmj.comDownloaded from 
doi: 10.1136/bmjopen-2012-001255
 2012 2: BMJ Open
 
Sabina Nuti and Milena Vainieri
 
medical imaging
Managing waiting times in diagnostic
 http://bmjopen.bmj.com/content/2/6/e001255.full.html
Updated information and services can be found at: 
These include:
Data Supplement
 http://bmjopen.bmj.com/content/suppl/2012/12/14/bmjopen-2012-001255.DC1.html
"Supplementary Data"
References
 http://bmjopen.bmj.com/content/2/6/e001255.full.html#ref-list-1
This article cites 13 articles, 3 of which can be accessed free at:
Open Access
http://creativecommons.org/licenses/by-nc/2.0/legalcode.
http://creativecommons.org/licenses/by-nc/2.0/ and 
compliance with the license. See:
work is properly cited, the use is non commercial and is otherwise in 
use, distribution, and reproduction in any medium, provided the original
Creative Commons Attribution Non-commercial License, which permits 
This is an open-access article distributed under the terms of the
service
Email alerting
the box at the top right corner of the online article.
Receive free email alerts when new articles cite this article. Sign up in
Collections
Topic
 (18 articles)Radiology and imaging   •
 (161 articles)Health services research   •
 (68 articles)Health policy   •
 (36 articles)Health economics   •
 
Articles on similar topics can be found in the following collections
Notes
 http://group.bmj.com/group/rights-licensing/permissions
To request permissions go to:
 http://journals.bmj.com/cgi/reprintform
To order reprints go to:
 http://group.bmj.com/subscribe/
To subscribe to BMJ go to:
 group.bmj.com on December 17, 2012 - Published by bmjopen.bmj.comDownloaded from