over the past two decades , healthcare reforms in western europe have changed primary care systems , reshaping in particular the organisational role of general practitioners ( gps ) and their clinical and managerial activities . a major thesis shared by many countries
is the promotion of cooperation among gps as well as the improvement of inter - professional collaborative team works as a means to spread knowledge , facilitate accountability and , ultimately , improve patient care with limited resources .
for some years now , also in italy primary care organisational models have been frequently reconsidered in order to enhance accessibility and improve coordination , continuity , and comprehensiveness of care , to increase the capacity for efficient , effective and appropriate care , and to provide opportunities for nursing and other healthcare providers to engage in collaborative practice with gps . nevertheless , these changes have consistently been supply - led rather than demand - led and the idea that the redefinition of primary care models should be primarily consistent with population needs and preferences is strengthening .
traditionally , gps in italy have worked in solo practices without any auxiliary staff or institutional links to other gps . over the past 10  years
, many local health authorities ( lhas ) have tried to reshape the traditional model of primary care by encouraging gps to participate in collaborative arrangements such as group practices in which gps share practice space and other resources .
the main idea behind such initiatives was the improvement of care continuity by reinforcing service coordination and information - sharing among gps in a practice .
however , apart from these expedients , and patient loyalty to their physician , there were no formal mechanisms to guarantee longitudinal and vertical continuity of care .
moreover , associated gps did not appear to perform better in terms of meeting lha pharmaceutical budgets because of the connections formed as a consequence of gp networks .
more recently , italian primary healthcare reform has moved towards a more comprehensive and team - based approach to address population - specific needs and to treat chronic diseases more proactively . in this
professionals from various disciplines ( gps , specialists , out - of - hours doctors , nurses , physiotherapists , psychologists , social workers ) provide a broad range of medical and community services covering diagnostic , curative and palliative care , disease prevention , rehabilitation , home care and patient education and self management interventions . to guarantee longitudinal continuity ,
chronic patient outcomes are measured systematically through structured health tracking instruments and recorded in the patient s medical record , in order to prevent a relapse into poor health condition after improvement .
the caregiving team also promotes the creation of networks for vertical continuity , sharing clinical information with other providers serving the same population ( e.g. hospitals or private practices ) .
as , in the most recent community models , members of collaborative teams share the same centralized building ( primary care centres ) , this setting can also benefit from diagnostic and treatment technologies for early disease detection and rehabilitation that could avoid non - urgent access to accident and emergency department ( a&ed ) and considerably reduce the number of unnecessary referrals to specialists . despite the apparent superiority of team - based community models
, these solutions have shown some important limitations , as highlighted in the present trial as well as in the national and international literature . according to lamarche et al . ,
although on the whole such models ( integrated community models in the author s taxonomy ) achieve the best empirical results in terms of effectiveness , cost reduction , care continuity , quality and equity , they encounter difficulties in preserving the individual relationship between the patient and the professional mostly responsible for their care ( relational continuity ) .
besides , the findings of an empirical study using administrative data showed a considerable variation in medical patterns among some italian primary care providers organised on a team - based model .
thus , while it is possible to recognise both strong and weak points in the various primary care models , it is still difficult to determine which solution is the best , especially in terms of patient preference . to design services that are sensitive
to population needs in a context of limited resources it is therefore important to find out which aspects of primary care models users / patients would most like to see improved , given that they can not have the best level of every characteristic .
this implies a necessary trade - off between the most important attributes of the aforementioned models from the population perspective . at present , although several studies have elicited preferences for various aspects of the primary care sector , there is scant evidence on the importance of different primary care models .
many studies have focussed mainly on family practice , investigating patient predilections [ 810 ] and patient and provider preferences   for characteristics connected with a gp appointment  mainly access and type of professional consulted .
the value given by the population to continuity of care   and regarding the provision of nurse - led versus doctor - led primary health care was also taken into account .
concerning out - of - hours services , preferences for general [ 14 , 15 ] and paediatric   out - of - hours primary care services were quantified , as well as the importance of attributes associated to emergency primary care services available during gp hours .
patient relationship in general practice [ 1822 ] . regarding publications investigating provider choices , gps preferences for different job characteristics in general practice
have been elicited [ 2326 ] and community pharmacists priorities for existing and potential new roles in primary care have been examined . recently a swedish study   reported population preferences for alternative primary care settings , which , however , considered a specific primary care system , different from the italian one , and did not take into account factors related to respondents experience .
in this study , a discrete choice experiment ( dce ) was used to elicit patient preferences for different primary care models .
dces are a popular stated preference technique in health economics   that elicit people s preferences on the basis of their stated preferences in hypothetical scenarios or choice sets . through the use of a dce , the relative importance of the different attributes for a sample of tuscan ( italian ) residents and for respondent subgroups
in the 20082010 regional health plan , the tuscan regional health system ( trhs ) introduced the strategic priority of developing a proactive approach to population - based medicine , experimenting with inter - professional team - based arrangements focussed specifically on chronic patients (  primary care units  ) . within this context
, a dce was embedded in the 2009 patient satisfaction and experience survey on primary care services ( sepc ) which was developed by the laboratorio management and sanit of scuola superiore santanna on behalf of tuscany region .
the sepc questionnaire was administered to a sample of tuscan residents over 18  years of age and consisted of four sections .
the first presented questions taken from the sepc survey about respondents experience with primary care services , such as the frequency of gp visits in the last year , the reason for seeing the gp , whether the patient had or had not been listened to carefully by the gp , and whether the gp had given clear explanations about the treatment or not . in the second section ,
the attributes of primary care services selected for the dce experiment were presented , after a short introduction on why the dce was performed . to identify participants that appear unwilling to trade - off the attributes ,
each respondent was invited to rank the attributes in order of importance . in the third section
, participants were asked to make their choices in the context of a consultation for a non - urgent problem , and to express their preference for each choice set presented by selecting one of the unlabelled options a or b , considering that all other characteristics about the consultation were assumed to be equal . this section started with an exhaustive description of each attribute and of its level to clarify their meanings and implications .
the last section consisted of questions on current health status as well as socio - demographic questions , taken from the sepc survey .
although dces in health care have been carried out mainly using self - completed postal questionnaires [ 29 , 33 ] , a computer - aided telephone interview approach was selected as it allowed a wide geographic coverage with higher response rates than postal or internet approaches   and it was considered a viable method if used with a small number of choice sets per respondent .
the sample was stratified into the 34 health districts in the region . in each health district , a sample size of approximately 196 subjects was required , assuming that a proportion of 50  % of the adult population have used primary care services in the last 12  months at a 95  % confidence level with a margin of error of 7  % .
assuming a response rate of approximately 40  % , which is in line with previous studies in that area , oversampling was performed to ensure that the minimum sample size was obtained .
the calculated sample size was then multiplied by 34 to obtain the total sample size of 6,970 . however , there is limited guidance on sample size calculations for dces , and there are no practical well - designed rules to guide the analyst .
have suggested that , for dce designs , sample sizes over 100 are able to provide a basis for modelling preference data , and hensher et al .   have suggested a rule of thumb of 50 respondents per question to provide adequate variation in the variables of interest .
telephone interviews were conducted in the spring of 2009 by a team of 13 experienced interviewers . to minimise interviewer effects ,
interviewers were initially trained before taking part in the study . through a review of the existing literature and semi - structured interviews to primary care managers and managers of lhas , attributes and levels describing the different primary care scenarios in the choice experiment were identified .
the number of selected attributes regarding waiting times , kind of primary care provider and presence of diagnostic facilities , was limited to the three most important factors that emerged [ 38 , 39 ] , in order to avoid placing a significant cognitive burden on respondents .
plausible levels were assigned to each of the attributes ( table  1 ) , taking into account also the results of previous choice experiments [ 15 , 28].table  1discrete choice experiment ( dce ) attributes , levels and namesattributeslevelnamewaiting time for visit ( wait)0  minwaiting time90  min180  minprimary care provider ( gp)one s own gpown gpa primary care team ( gp  +  other professionals)primary care teamanother gp in the same practice 
 another gpdiagnostic facilities ( diag)a lot of diagnostic facilitiesmany diagnostic facilitiessome diagnostic facilitiessome diagnostic facilitiesa few diagnostic facilities 
 few diagnostic facilities 
 gp general practitioner 
 denotes the base category discrete choice experiment ( dce ) attributes , levels and names 
 gp general practitioner 
 denotes the base category a full factorial design with 3 ( 27 ) combinations was used . to obtain a more statistically efficient design ,
the exclusion of an opt - out option could be a violation of the underlying welfare measures of the economic experiment , since it makes it impossible to estimate the value of doing nothing , which may be chosen in practice .
nevertheless , this may raise the number of neutral responses , increasing the number of individuals that may choose the opt - out scenario to prevent making difficult choices , even though this would not provide the highest utility .
the feasibility of using an opt - out approach  adding a  neither  option  was tested in the pilot study , that revealed that neutral responses were likely to be obtained in this dce ; therefore a forced choice was chosen as appropriate . adding a status quo alternative would have been another option , but it was rejected for two reasons .
first of all , the  status quo bias  , i.e. the tendency to choose what respondents know best , since respondents were already experienced with primary care services .
secondly , there were possible econometric and interpretation difficulties , due to the fact that the status quo alternative differed among respondents .
a few alternatives to come out of the design may not contain feasible attribute - level combinations ( specifically  own gp  associated with 
many diagnostic facilities  ) . since the pilot testing indicated that individuals did not find any of the combinations in the experimental design implausible , constraints between levels were not applied .
a pre - pilot test was performed to a sample of 34 individuals of different ages and from different geographical locations ( health districts ) .
considering that there is little evidence in the literature about the manageable number of choice sets per respondent with telephone surveys and that , above all , the appropriate number of choice sets is context - specific , a blocked design was used to pre - pilot two different sets of questionnaires , including ten and four choice tasks , respectively .
the 27 choice sets were therefore distributed across three blocks of nine and nine blocks of three , respectively , creating an extra column with a number of levels equal to the number of blocks , which is uncorrelated with every attribute of every alternative .
the sequence of questions was randomised and the first choice set was then repeated as the last choice set , to provide a check of response consistency ( discussed further below ) and to allow for a 
warm - up  question at the beginning of the sequence . as it adds no statistical information , the repeated question was not included in the main data analyses . at the end of the choice experiment ,
respondents were asked if they were taking into consideration other attributes not included in the task when making choices , and to outline them in the affirmative case . on the basis of respondents direct feedback , response rates , item response rates , and rationality tests ,
the pre - piloting indicated that respondents were able to handle a maximum of four choices .
apart from the  consultation length  mentioned by one respondent , no other attributes different from those included in the dce were considered as relevant by the participants during their decision making process .
some changes were made to the wording of the questions and the instructions , integrating in particular the attributes description with examples in order to place the hypothetical scenarios in a more recognisable and realistic setting .
a further pilot study was undertaken with a new sample of 34 subjects of different age and geographical locations . on the whole
thus , in the final questionnaire , each respondent was assigned randomly to one of the nine blocks and was presented with four discrete choices .
to preserve data set orthogonality , the nine subgroups related to each questionnaire version included an equal number of respondents .
the groups were then tested for homogeneity with regard to geographical location , age and sex .
1.fig .  1example of a choice set example of a choice set the preliminary detection of  dominant options 
( where all attributes of the first alternative are preferred to all attributes of the second alternative , or vice versa ) was not feasible in this dce for two reasons .
first , the experiment includes a qualitative attribute (  primary care provider  ) with levels that do not have a clear ordering and that vary systematically across the alternatives .
second , the sample size of the pilot study was inadequate to make reliable prior assumptions on parameters .
nevertheless , potential imprecision in the estimates should predictably be filtered out , since design techniques that also account for statistical efficiency , excluding most of the choice situations with clearly dominant options , were used , and also because of the large sample size of the study .
choice data were modelled using a random utility maximisation framework . each participants choice between pairs , treated as a single observation , was included in the model as the binary dependent variable (  1  represents the option being chosen , while  0  not chosen ) .
the independent variables were the differences between the levels of each attribute in each pair of scenarios .
a random effect probit model was used for the estimation , to represent the distribution of the error term that was assumed normal , and also to account for multiple observations from a single respondent .
having also assumed a linear additive utility function , the follow baseline empirical model was specified:1\documentclass[12pt]{minimal } 
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				 \begin{document}$$\vardelta u_{nc } = \beta_{0 } + \beta_{1 } * \vardelta { \text{wait}}_{c } + \beta_{2 } * \vardelta { \text{gp}}_{c } + \beta_{3 } * \vardelta { \text{diag}}_{c } + \pi_{n } + \lambda_{nc}$$\end{document}u indicates the difference in utility between alternatives of a choice set that is observed indirectly .
the subscripts n and c refer to the individual and the number of choice set , respectively .
wait , gp , diag represent the differences in attribute levels within each choice set . in view of the fact that a shorter waiting time and more diagnostic facilities are intuitively preferable , it was expected that the former attribute would be associated with a negative coefficient and the latter with a positive one . for the remaining qualitative attribute , no a priori assumption was made .
0 is the constant term , included to test and control model misspecifications due to unobserved dimensions or unobserved interactions between respondents socio - economic characteristics and dimensions .
1 , 2 , 3 denote the part - worths estimated from the regression analysis .
n is the individual specific error term whereas nc is the random error term . to quantify the correlation between choices ,
the serial correlation was estimated , or corr [  ,  ]  =  . effects - coding   was used for the attributes  primary care provider  and  diagnostic facilities  ( 1 for the base category , 1 for the presence of another category and 0 otherwise ) .
it was also hypothesised that respondents socio - demographic condition and their past experience with the gp would influence preferences for a primary care service .
the segmented model , which included all main and interaction effects , was reduced stepwise to a more parsimonious one , by excluding insignificant interaction effects one at a time on the basis of the likelihood ratio test with a p value > 0.05 .
the estimated marginal utilities were used to quantify the relative importance of the attributes and the marginal rate of substitution ( mrs ) , calculated by dividing the estimated values of the attributes with the value of the  waiting time  attribute . for each effects - coded variable
the marginal utilities were obtained calculating the difference between the estimated coefficients and the base category coefficient , defined as the negative sum of the coefficients for all other categories of that variable .
furthermore , as previously done in other health care related dces [ 15 , 5760 ] , the part - worth utilities ( s ) and the constant estimated in the eq . 
1 were summed to predict the overall utility for all the combinations of attribute levels in the full factorial design .
in addition to the 27 hypothetical scenarios included in the design , three additional forms of care delivery , which are the most representative of the italian primary care service alternatives previously described (  solo general practice  , 
group general practice  and  primary care centre  ) , were identified . for a visit to a 
, on average , patients have to wait more than 1  h ( 70  min ) to be seen by their own gp exclusively , in a practice with few diagnostic facilities . in a 
group general practiceoften a setting with some diagnostic services  if patients accept to be seen by an associated gp different from their own physician , they usually have to wait less time ( 40  min ) . a consultation in a
 primary care centre  normally implies a short waiting time ( 10  min ) , for a visit provided by a primary care team ( the gp and other professionals ) in a practice with many diagnostic services .
the 95  % confidence intervals ( 95  % cis ) for the  willingness to wait  estimates and predicted utilities were calculated using non - parametric bootstrapping   with 2,000 iterations .
all statistical analyses were performed using stata 10 ( statacorp , college station , tx ) .
internal validity was tested by three approaches : ( 1 ) consistency of preferences , ( 2 ) willingness to trade , and ( 3 ) consistency with theoretical predictions.to measure internal consistency , a test of stability was carried out , by which subjects are asked to consider the same discrete - choice comparison both at the beginning and at the end of the questionnaire .
we expected subjects to make the same choice both times the question was offered.the willingness of respondents to trade - off the attributes was tested through the approach used in scott et al . , identifying respondents with dominant preferences ( individuals that always choose according to the best level of a given attribute ) . in relation to the attributes  waiting time  and  diagnostic capabilities 
best  could be identified , for each attribute was tested whether an individual always chose the option with the best level and ranked the attribute as the most important in a simple ranking of the attributes .
dominant preferences for  primary care provider  were not calculated since the  best  level of this qualitative attribute was not known a priori .
the influence of dominant preferences was then assessed by running a regression analysis twice , including and excluding respondents with dominant preferences.theoretical validity was investigated by examining the sign and significance of parameter estimates . to measure internal consistency ,
a test of stability was carried out , by which subjects are asked to consider the same discrete - choice comparison both at the beginning and at the end of the questionnaire .
the willingness of respondents to trade - off the attributes was tested through the approach used in scott et al . , identifying respondents with dominant preferences ( individuals that always choose according to the best level of a given attribute ) . in relation to the attributes  waiting time  and  diagnostic capabilities  , where the 
best  could be identified , for each attribute was tested whether an individual always chose the option with the best level and ranked the attribute as the most important in a simple ranking of the attributes .
dominant preferences for  primary care provider  were not calculated since the  best  level of this qualitative attribute was not known a priori .
the influence of dominant preferences was then assessed by running a regression analysis twice , including and excluding respondents with dominant preferences .
in the 20082010 regional health plan , the tuscan regional health system ( trhs ) introduced the strategic priority of developing a proactive approach to population - based medicine , experimenting with inter - professional team - based arrangements focussed specifically on chronic patients (  primary care units  ) . within this context
, a dce was embedded in the 2009 patient satisfaction and experience survey on primary care services ( sepc ) which was developed by the laboratorio management and sanit of scuola superiore santanna on behalf of tuscany region .
the sepc questionnaire was administered to a sample of tuscan residents over 18  years of age and consisted of four sections .
the first presented questions taken from the sepc survey about respondents experience with primary care services , such as the frequency of gp visits in the last year , the reason for seeing the gp , whether the patient had or had not been listened to carefully by the gp , and whether the gp had given clear explanations about the treatment or not . in the second section ,
the attributes of primary care services selected for the dce experiment were presented , after a short introduction on why the dce was performed . to identify participants that appear unwilling to trade - off the attributes ,
each respondent was invited to rank the attributes in order of importance . in the third section
, participants were asked to make their choices in the context of a consultation for a non - urgent problem , and to express their preference for each choice set presented by selecting one of the unlabelled options a or b , considering that all other characteristics about the consultation were assumed to be equal . this section started with an exhaustive description of each attribute and of its level to clarify their meanings and implications .
the last section consisted of questions on current health status as well as socio - demographic questions , taken from the sepc survey .
although dces in health care have been carried out mainly using self - completed postal questionnaires [ 29 , 33 ] , a computer - aided telephone interview approach was selected as it allowed a wide geographic coverage with higher response rates than postal or internet approaches   and it was considered a viable method if used with a small number of choice sets per respondent .
the sample was stratified into the 34 health districts in the region . in each health district , a sample size of approximately 196 subjects was required , assuming that a proportion of 50  % of the adult population have used primary care services in the last 12  months at a 95  % confidence level with a margin of error of 7  % .
assuming a response rate of approximately 40  % , which is in line with previous studies in that area , oversampling was performed to ensure that the minimum sample size was obtained .
the calculated sample size was then multiplied by 34 to obtain the total sample size of 6,970 . however , there is limited guidance on sample size calculations for dces , and there are no practical well - designed rules to guide the analyst .
have suggested that , for dce designs , sample sizes over 100 are able to provide a basis for modelling preference data , and hensher et al .   have suggested a rule of thumb of 50 respondents per question to provide adequate variation in the variables of interest .
telephone interviews were conducted in the spring of 2009 by a team of 13 experienced interviewers . to minimise interviewer effects ,
through a review of the existing literature and semi - structured interviews to primary care managers and managers of lhas , attributes and levels describing the different primary care scenarios in the choice experiment were identified .
the number of selected attributes regarding waiting times , kind of primary care provider and presence of diagnostic facilities , was limited to the three most important factors that emerged [ 38 , 39 ] , in order to avoid placing a significant cognitive burden on respondents .
plausible levels were assigned to each of the attributes ( table  1 ) , taking into account also the results of previous choice experiments [ 15 , 28].table  1discrete choice experiment ( dce ) attributes , levels and namesattributeslevelnamewaiting time for visit ( wait)0  minwaiting time90  min180  minprimary care provider ( gp)one s own gpown gpa primary care team ( gp  +  other professionals)primary care teamanother gp in the same practice 
 another gpdiagnostic facilities ( diag)a lot of diagnostic facilitiesmany diagnostic facilitiessome diagnostic facilitiessome diagnostic facilitiesa few diagnostic facilities 
 few diagnostic facilities 
 gp general practitioner 
 denotes the base category discrete choice experiment ( dce ) attributes , levels and names 
 gp general practitioner 
 denotes the base category
a full factorial design with 3 ( 27 ) combinations was used . to obtain a more statistically efficient design ,
the exclusion of an opt - out option could be a violation of the underlying welfare measures of the economic experiment , since it makes it impossible to estimate the value of doing nothing , which may be chosen in practice .
nevertheless , this may raise the number of neutral responses , increasing the number of individuals that may choose the opt - out scenario to prevent making difficult choices , even though this would not provide the highest utility .
the feasibility of using an opt - out approach  adding a  neither  option  was tested in the pilot study , that revealed that neutral responses were likely to be obtained in this dce ; therefore a forced choice was chosen as appropriate . adding a status quo alternative would have been another option , but it was rejected for two reasons .
first of all , the  status quo bias  , i.e. the tendency to choose what respondents know best , since respondents were already experienced with primary care services .
secondly , there were possible econometric and interpretation difficulties , due to the fact that the status quo alternative differed among respondents .
a few alternatives to come out of the design may not contain feasible attribute - level combinations ( specifically  own gp  associated with  many diagnostic facilities  ) .
since the pilot testing indicated that individuals did not find any of the combinations in the experimental design implausible , constraints between levels were not applied .
a pre - pilot test was performed to a sample of 34 individuals of different ages and from different geographical locations ( health districts ) .
considering that there is little evidence in the literature about the manageable number of choice sets per respondent with telephone surveys and that , above all , the appropriate number of choice sets is context - specific , a blocked design was used to pre - pilot two different sets of questionnaires , including ten and four choice tasks , respectively .
the 27 choice sets were therefore distributed across three blocks of nine and nine blocks of three , respectively , creating an extra column with a number of levels equal to the number of blocks , which is uncorrelated with every attribute of every alternative .
the sequence of questions was randomised and the first choice set was then repeated as the last choice set , to provide a check of response consistency ( discussed further below ) and to allow for a 
warm - up  question at the beginning of the sequence . as it adds no statistical information , the repeated question was not included in the main data analyses . at the end of the choice experiment ,
respondents were asked if they were taking into consideration other attributes not included in the task when making choices , and to outline them in the affirmative case .
on the basis of respondents direct feedback , response rates , item response rates , and rationality tests , the pre - piloting indicated that respondents were able to handle a maximum of four choices .
apart from the  consultation length  mentioned by one respondent , no other attributes different from those included in the dce were considered as relevant by the participants during their decision making process .
some changes were made to the wording of the questions and the instructions , integrating in particular the attributes description with examples in order to place the hypothetical scenarios in a more recognisable and realistic setting .
a further pilot study was undertaken with a new sample of 34 subjects of different age and geographical locations . on the whole
thus , in the final questionnaire , each respondent was assigned randomly to one of the nine blocks and was presented with four discrete choices . to preserve data set orthogonality ,
the groups were then tested for homogeneity with regard to geographical location , age and sex .
1.fig .  1example of a choice set example of a choice set the preliminary detection of  dominant options 
( where all attributes of the first alternative are preferred to all attributes of the second alternative , or vice versa ) was not feasible in this dce for two reasons .
first , the experiment includes a qualitative attribute (  primary care provider  ) with levels that do not have a clear ordering and that vary systematically across the alternatives .
second , the sample size of the pilot study was inadequate to make reliable prior assumptions on parameters .
nevertheless , potential imprecision in the estimates should predictably be filtered out , since design techniques that also account for statistical efficiency , excluding most of the choice situations with clearly dominant options , were used , and also because of the large sample size of the study .
choice data were modelled using a random utility maximisation framework . each participants choice between pairs , treated as a single observation , was included in the model as the binary dependent variable (  1  represents the option being chosen , while  0  not chosen ) .
the independent variables were the differences between the levels of each attribute in each pair of scenarios .
a random effect probit model was used for the estimation , to represent the distribution of the error term that was assumed normal , and also to account for multiple observations from a single respondent .
having also assumed a linear additive utility function , the follow baseline empirical model was specified:1\documentclass[12pt]{minimal } 
				 \usepackage{amsmath } 
				 \usepackage{wasysym } 
				 \usepackage{amsfonts } 
				 \usepackage{amssymb } 
				 \usepackage{amsbsy } 
				 \usepackage{mathrsfs } 
				 \usepackage{upgreek } 
				 \setlength{\oddsidemargin}{-69pt } 
				 \begin{document}$$\vardelta u_{nc } = \beta_{0 } + \beta_{1 } * \vardelta { \text{wait}}_{c } + \beta_{2 } * \vardelta { \text{gp}}_{c } + \beta_{3 } * \vardelta { \text{diag}}_{c } + \pi_{n } + \lambda_{nc}$$\end{document}u indicates the difference in utility between alternatives of a choice set that is observed indirectly .
the subscripts n and c refer to the individual and the number of choice set , respectively .
wait , gp , diag represent the differences in attribute levels within each choice set . in view of the fact that a shorter waiting time and more diagnostic facilities are intuitively preferable
, it was expected that the former attribute would be associated with a negative coefficient and the latter with a positive one . for the remaining qualitative attribute
0 is the constant term , included to test and control model misspecifications due to unobserved dimensions or unobserved interactions between respondents socio - economic characteristics and dimensions .
1 , 2 , 3 denote the part - worths estimated from the regression analysis .
n is the individual specific error term whereas nc is the random error term . to quantify the correlation between choices ,
the serial correlation was estimated , or corr [  ,  ]  =  . effects - coding   was used for the attributes  primary care provider  and  diagnostic facilities  ( 1 for the base category , 1 for the presence of another category and 0 otherwise ) .
it was also hypothesised that respondents socio - demographic condition and their past experience with the gp would influence preferences for a primary care service .
the segmented model , which included all main and interaction effects , was reduced stepwise to a more parsimonious one , by excluding insignificant interaction effects one at a time on the basis of the likelihood ratio test with a p value > 0.05 .
the estimated marginal utilities were used to quantify the relative importance of the attributes and the marginal rate of substitution ( mrs ) , calculated by dividing the estimated values of the attributes with the value of the  waiting time  attribute . for each effects - coded variable
the marginal utilities were obtained calculating the difference between the estimated coefficients and the base category coefficient , defined as the negative sum of the coefficients for all other categories of that variable .
furthermore , as previously done in other health care related dces [ 15 , 5760 ] , the part - worth utilities ( s ) and the constant estimated in the eq . 
1 were summed to predict the overall utility for all the combinations of attribute levels in the full factorial design .
in addition to the 27 hypothetical scenarios included in the design , three additional forms of care delivery , which are the most representative of the italian primary care service alternatives previously described (  solo general practice  , 
group general practice  and  primary care centre  ) , were identified . for a visit to a  solo general practice  , on average , patients have to wait more than 1  h ( 70  min ) to be seen by their own gp exclusively , in a practice with few diagnostic facilities . in a 
group general practiceoften a setting with some diagnostic services  if patients accept to be seen by an associated gp different from their own physician , they usually have to wait less time ( 40  min ) . a consultation in a
 primary care centre  normally implies a short waiting time ( 10  min ) , for a visit provided by a primary care team ( the gp and other professionals ) in a practice with many diagnostic services .
the 95  % confidence intervals ( 95  % cis ) for the  willingness to wait  estimates and predicted utilities were calculated using non - parametric bootstrapping   with 2,000 iterations .
all statistical analyses were performed using stata 10 ( statacorp , college station , tx ) .
internal validity was tested by three approaches : ( 1 ) consistency of preferences , ( 2 ) willingness to trade , and ( 3 ) consistency with theoretical predictions.to measure internal consistency , a test of stability was carried out , by which subjects are asked to consider the same discrete - choice comparison both at the beginning and at the end of the questionnaire .
we expected subjects to make the same choice both times the question was offered.the willingness of respondents to trade - off the attributes was tested through the approach used in scott et al . , identifying respondents with dominant preferences ( individuals that always choose according to the best level of a given attribute ) . in relation to the attributes  waiting time  and  diagnostic capabilities 
best  could be identified , for each attribute was tested whether an individual always chose the option with the best level and ranked the attribute as the most important in a simple ranking of the attributes .
dominant preferences for  primary care provider  were not calculated since the  best  level of this qualitative attribute was not known a priori .
the influence of dominant preferences was then assessed by running a regression analysis twice , including and excluding respondents with dominant preferences.theoretical validity was investigated by examining the sign and significance of parameter estimates . to measure internal consistency ,
a test of stability was carried out , by which subjects are asked to consider the same discrete - choice comparison both at the beginning and at the end of the questionnaire .
the willingness of respondents to trade - off the attributes was tested through the approach used in scott et al . , identifying respondents with dominant preferences ( individuals that always choose according to the best level of a given attribute ) . in relation to the attributes  waiting time  and  diagnostic capabilities 
best  could be identified , for each attribute was tested whether an individual always chose the option with the best level and ranked the attribute as the most important in a simple ranking of the attributes .
dominant preferences for  primary care provider  were not calculated since the  best  level of this qualitative attribute was not known a priori .
the influence of dominant preferences was then assessed by running a regression analysis twice , including and excluding respondents with dominant preferences .
of these participants , 3,263 completed the choice tasks , with a response rate of 47  % .
the respondents were distributed equally and without any significant differences in socio - demographic characteristics and past experience with the gp across the nine versions of the questionnaire used .
details on responders characteristics are given in table  2.table  2characteristics of respondentsattributelevelnamefrequency%age group1849  yearsage 184995529.65069  yearsage 50691,38843.0>69  years 
 age  > 6988627.4genderfemalefemale2,50276.8male 
 male75623.2educationnone / primary leveleduc no1,04732.6secondary leveleduc sec1,83357.1university degree or higher 
 educ uni32810.2employment statusnot working / retiredempl no1,49046.5working ( high - skilled jobs)empl high3089.6working ( medium / low - skilled jobs )  +  students 
 empl low1,40643.9incomehighinc high1,31342.2mediuminc med1,27941.1low 
 inc low52016.7living aloneyesalone40212.5no 
 alone no2,80287.5health statusfair / poorhealth low42013.1excellent / very good / good 
 health high2,77586.9chronic diseaseyeschron1,25438.9no 
 chron no1,97161.1frequency of visits to gp clinic in the last yearnever / from 1 to 3 timesfreq low92131.1more than 3 times 
 freq high2,04368.9reason for seeing the gpgeneral health check / minor illness treatmentreas min50417.3already existing illness checkreas exist66622.8prescriptions / certificates / other 
 reas other1,74859.9the gp works with other gpsyesassoc83928.8no 
 assoc no2,07971.2time you waited in the clinic<1  hwait less2,18077.1>1  h 
 wait more64822.9you have had to put off seeing the gpyes ( waited too much , gp unavailable , clinic closed)putoff2859.8no 
 putoff no2,63390.2the gp listened to you carefullyyeslisten2,87098.6no 
 listen no421.4the gp gave you enough time to discussyesentime2,87098.5no 
 entime no431.5the gp involved you in the decisionsyesinvolv2,83997.7no 
 involv no682.3the gp gave you clear explanationsyesclear2,87198.7no 
 clear
no381.3the gp gave you advicesyesadvice1,96567.4no 
 advice no94932.6you trust in your gpyestrust2,87898.6no 
 trust no401.4 
 denotes the base category characteristics of respondents 
 denotes the base category the age of respondents ranged from 18 to 96  years old with a mean age of 58  years ; 76.8  % were female and 57.1  % had a secondary level of education .
43.9  % were working in medium and low skilled jobs or engaged in a full time education ; 41.1  % had a medium income .
13.1  % were in a fair or poor health status and 38.9  % of them declared a chronic disease .
68.9  % of respondents had visited their gp clinic more than three times in the last year and 77.1  % of them waited < 1  h for a consultation .
the gp was seen mainly in order to get a prescription or certificate ( 59.9  % ) and to check on a pre - existing illness ( 22.8  % ) . during the consultation
, the gp listened carefully to 98.6  % of participants , gave 98.5  % of them enough time to discuss their problem , involved 97.7  % of them in decisions , gave clear explanations to 98.7  % of them and advice on eating or physical activity to 67.4  % of them .
a total of 11  % of respondents failed the stability test , which was considered to be acceptable as the percentage of inconsistent responses usually varies from 1  %   to 25  % .
also , 11  % of respondents always chose the scenario with the best level of a given attribute that they ranked as the most important .
the results of regression analyses indicated that the impact on the coefficient size and direction for each attribute was the same regardless of whether dominant preferences were excluded or included within the data analysis .
considering also that random utility models are robust to violations of compensatory decision making , all respondents were thus included in the final analysis .
the serial correlation obtained from running the random effect models was close to zero and not statistically significant , indicating that respondents treated the decision made in each pair - wise comparison as a separate hypothetical situation and not in association with the choice made in each of the remaining pair - wise comparisons .
hence , all models were re - fitted to the data using the standard probit model . to verify whether the linear representation of the continuous variable  waiting time  was admissible , a univariate smoothed scatter plot   was first performed to show potential non - linearities .
in addition , the probit model was re - estimated using dummy variables replacing the continuous variable .
lastly , a likelihood ratio test was used to assess whether the inclusion of a quadratic term would have improved the explanatory power of the model .
as table  3 shows , the main effects probit model has a good fit ( mcfadden pseudo r  =  0.25 ) and predicts correctly 76  % of the responses .
all the attributes had a significant impact on respondents decisions.table  3regression results from dce : main effects modelattributecoefficientsemrs ( min)95  % ci ( lower)95  % ci ( upper)constant0.546***0.016waiting time ( min)0.006***0.000own gp0.611***0.015207.1196.5219.5primary care team0.100***0.014127.1118.1135.7many diagnostic facilities0.534***0.015194.9184.9205.7some diagnostic facilities0.176***0.014138.8129.6148.4 
 n 
 19,340log likelihood10,086.97likelihood ratio test ( c 
 , df ) 
 6,636.99 ( 5)***pseudo r 
  mcfadden 
 0.248 
 se standard error , mrs marginal rate of substitution * * *  p  <  0.001 
 compared to a only constant model regression results from dce : main effects model 
 se standard error , mrs marginal rate of substitution 
 compared to a only constant model other things being equal , participants would be willing to wait up to 207  min for a consultation with their own gp , 195  min longer to be visited in a setting with many diagnostic services and 139  min longer to have some diagnostic facilities in the practice .
therefore , patients preferred to be visited by their own gp and a practice with many diagnostic facilities to a practice with some diagnostic services
. these results are in line with expectations and provide support for the theoretical validity of the model .
compared with the main effects model , the reduced model has a better fit ( pseudo r  =  0.28 ) , with the main effects of a similar sign and significant . the results of the model are given in table  4.table  4regression results from dce : segmented modelattributecoefficientseconstant0.5993***0.018waiting time0.0065***0.000own gp0.6165***0.020primary care team0.1636***0.019many diagnostic facilities0.5507***0.019some diagnostic facilities0.2563***0.028waiting time    age 18490.0006***0.000waiting time    health low0.0003 * 0.000waiting time    wait less0.0004**0.000own gp    empl no0.0740***0.016own gp    chron0.3579***0.018own gp    freq low0.0676***0.018own gp    advice0.1159***0.017primary care team    age 18490.0927***0.023primary care team    age 50690.0591**0.019primary care team    chron0.2379***0.017primary care team    freq low0.0606**0.018primary care team    advice0.0919***0.017a lot of diag .
facilities    reas min0.0481**0.018some diag . facilities    putoff0.0829***0.024 
 n 
 16,120log likelihood8,055.42likelihood ratio test ( c 
 , df ) 
 6,236.23 ( 25)***pseudo r 
  mcfadden 
 0.279 * * *  p 
<  0.001 , * *  0.01  >  p    0.001 , *  0.05    p    0.01 
 compared to a only constant model regression results from dce : segmented model * * *  p  <  0.001 , * *  0.01  >  p    0.001 , *  0.05   
p    0.01 
 compared to a only constant model the marginal utilities indicate that younger respondents ( under 45  years ) and people that waited in the gp practice < 1  h for the last visit would prefer short waiting times . chronic patients and those who are retired or are not working preferred to be visited by their own gp .
this provider had also a higher marginal utility for respondents who , in their last consultation , received advice from the gp on eating or physical activity .
on the other hand , the primary care team was the favourite option for younger respondents who went less frequently to the gp clinic in the last year . concerning
the impact of respondent characteristics on preferences for the diagnostic setting , a visit in a practice with many diagnostic facilities was the preferred option for female respondents and for those that reported a chronic condition .
a setting with some diagnostic services was more likely to be preferred by younger respondents and by people who are retired or are not working .
those who saw a gp in the last year for treatment of a minor illness or for a general health check and those who have had to put off seeing the gp in the last year also preferred to be visited in a practice with some diagnostic facilities . with regards to the choices between the different service configuration , table  5 presents a utility - based ranking for all the configuration hypothesized.table  5predicted utilities for alternative scenarios of care deliveryscenariowaiting timecaregiverdiagnostic facilitiesindirect utility 
 95  % ci ( lower)95  % ci ( upper)220  minown gpmany3.113.033.19250  minown gpsome2.752.682.83180  minprimary care teammany2.602.522.68primary care centre10  min 
 primary care teammany 
 2.54 

 2.46 

 2.61 
 1390  minown gpmany2.542.462.6160  minprimary
care teamsome2.242.172.32790  minown gpsome2.182.112.252690  minprimary care teammany2.031.952.102180  minown gpmany1.961.882.05200  minown gpfew1.871.821.92100  minanother gpmany1.791.741.841290  minprimary care teamsome1.671.591.7516180  minown gpsome1.601.521.699180  minprimary care teammany1.451.361.53140  minanother gpsome1.431.381.48solo gp 
 70  min 
 own gpfew1.421.371.4710  minprimary care teamfew1.361.311.40590  minown gpfew1.291.241.351990  minanother gpmany1.221.161.27group gp 
 40  min 
 another gpsome1.181.131.2321180  minprimary care teamsome1.091.011.18390  minanother gpsome0.860.800.912390  minprimary care teamfew0.780.730.8311180  minown gpfew0.720.650.784180  minanother gpmany0.640.580.7180  minanother gpfew0.550.550.5524180  minanother gpsome0.280.220.3515180  minprimary care teamfew0.210.140.271790  minanother gpfew0.030.050.0027180  minanother gpfew0.600.660.55 
 indirect utility  =  0.546  +  [ 0.006    waiting time ( min ) ]  +  ( 1.321    own gp )  +  ( 0.811    primary care team )  +  ( 1.243    many diag .
facilities ) predicted utilities for alternative scenarios of care delivery 
 indirect utility  =  0.546  +  [ 0.006    waiting time ( min ) ]  +  ( 1.321    own gp )  +  ( 0.811    primary care team )  +  ( 1.243    many diag . facilities )  +  ( 0.885    some diag .
facilities ) among the existing care models , the  primary care centre  would be the most preferred scenario , followed by the  solo general practice  and the  group general practice  . despite 
own gp  being the most preferred respondents caregiver , the actual context in which such physicians have to operate (  solo general practice)with few diagnostic facilities and long waiting times for the visits  would not be considered the best service alternative .
with reference to the  primary care centre  , most of all , its superiority over the 
solo general practice  derived from a grater diagnostic potential , rather than a shorter waiting time . indeed , assuming that the former service would not maintain a certain diagnostic power , even with no waiting time for the visit ( scenario 1 ) it would have a lower benefit score compared with the utility of the  solo general practice  . regarding the  group general practice  , to have a higher benefit score with such service configuration compared with the utility of the 
solo general practice  , respondents need to be compensated with a 40-min reduction in waiting time ( scenario 14 ) or , assuming no changes in waiting time , with more diagnostic services .
the age of respondents ranged from 18 to 96  years old with a mean age of 58  years ; 76.8  % were female and 57.1  % had a secondary level of education .
43.9  % were working in medium and low skilled jobs or engaged in a full time education ; 41.1  % had a medium income .
13.1  % were in a fair or poor health status and 38.9  % of them declared a chronic disease .
68.9  % of respondents had visited their gp clinic more than three times in the last year and 77.1  % of them waited < 1  h for a consultation .
the gp was seen mainly in order to get a prescription or certificate ( 59.9  % ) and to check on a pre - existing illness ( 22.8  % ) . during the consultation
, the gp listened carefully to 98.6  % of participants , gave 98.5  % of them enough time to discuss their problem , involved 97.7  % of them in decisions , gave clear explanations to 98.7  % of them and advice on eating or physical activity to 67.4  % of them .
a total of 11  % of respondents failed the stability test , which was considered to be acceptable as the percentage of inconsistent responses usually varies from 1  %   to 25  % .
also , 11  % of respondents always chose the scenario with the best level of a given attribute that they ranked as the most important .
the results of regression analyses indicated that the impact on the coefficient size and direction for each attribute was the same regardless of whether dominant preferences were excluded or included within the data analysis . considering also that random utility models are robust to violations of compensatory decision making , all respondents
the serial correlation obtained from running the random effect models was close to zero and not statistically significant , indicating that respondents treated the decision made in each pair - wise comparison as a separate hypothetical situation and not in association with the choice made in each of the remaining pair - wise comparisons .
hence , all models were re - fitted to the data using the standard probit model . to verify whether the linear representation of the continuous variable  waiting time  was admissible , a univariate smoothed scatter plot   was first performed to show potential non - linearities .
in addition , the probit model was re - estimated using dummy variables replacing the continuous variable .
lastly , a likelihood ratio test was used to assess whether the inclusion of a quadratic term would have improved the explanatory power of the model .
as table  3 shows , the main effects probit model has a good fit ( mcfadden pseudo r  =  0.25 ) and predicts correctly 76  % of the responses .
all the attributes had a significant impact on respondents decisions.table  3regression results from dce : main effects modelattributecoefficientsemrs ( min)95  % ci ( lower)95  % ci ( upper)constant0.546***0.016waiting time ( min)0.006***0.000own gp0.611***0.015207.1196.5219.5primary care team0.100***0.014127.1118.1135.7many diagnostic facilities0.534***0.015194.9184.9205.7some diagnostic facilities0.176***0.014138.8129.6148.4 
 n 
 19,340log likelihood10,086.97likelihood ratio test ( c 
 , df ) 
 6,636.99 ( 5)***pseudo r 
  mcfadden 
 0.248 
 se standard error , mrs marginal rate of substitution * * *  p  <  0.001 
 compared to a only constant model regression results from dce : main effects model 
 se standard error , mrs marginal rate of substitution 
 compared to a only constant model other things being equal , participants would be willing to wait up to 207  min for a consultation with their own gp , 195  min longer to be visited in a setting with many diagnostic services and 139  min longer to have some diagnostic facilities in the practice .
therefore , patients preferred to be visited by their own gp and a practice with many diagnostic facilities to a practice with some diagnostic services .
these results are in line with expectations and provide support for the theoretical validity of the model .
compared with the main effects model , the reduced model has a better fit ( pseudo r  =  0.28 ) , with the main effects of a similar sign and significant . the results of the model are given in table  4.table  4regression results from dce : segmented modelattributecoefficientseconstant0.5993***0.018waiting time0.0065***0.000own gp0.6165***0.020primary care team0.1636***0.019many diagnostic facilities0.5507***0.019some diagnostic facilities0.2563***0.028waiting time    age 18490.0006***0.000waiting time    health low0.0003 * 0.000waiting time    wait less0.0004**0.000own gp    empl no0.0740***0.016own gp    chron0.3579***0.018own gp    freq low0.0676***0.018own gp    advice0.1159***0.017primary care team    age 18490.0927***0.023primary care team    age 50690.0591**0.019primary care team    chron0.2379***0.017primary care team    freq low0.0606**0.018primary care team    advice0.0919***0.017a lot of diag .
facilities    putoff0.0829***0.024 
 n 
 16,120log likelihood8,055.42likelihood ratio test ( c 
 , df ) 
 6,236.23 ( 25)***pseudo r 
  mcfadden 
 0.279 * * * 
p  <  0.001 , * *  0.01  >  p    0.001 , *  0.05    p    0.01 
 compared to a only constant model regression results from dce : segmented model * * *  p  <  0.001 , * *  0.01  >  p    0.001 , *  0.05    p    0.01 
 compared to a only constant model the marginal utilities indicate that younger respondents ( under 45  years ) and people that waited in the gp practice < 1  h for the last visit would prefer short waiting times .
chronic patients and those who are retired or are not working preferred to be visited by their own gp .
this provider had also a higher marginal utility for respondents who , in their last consultation , received advice from the gp on eating or physical activity .
on the other hand , the primary care team was the favourite option for younger respondents who went less frequently to the gp clinic in the last year . concerning the impact of respondent characteristics on preferences for the diagnostic setting , a visit in a practice with many diagnostic facilities was the preferred option for female respondents and for those that reported a chronic condition .
a setting with some diagnostic services was more likely to be preferred by younger respondents and by people who are retired or are not working .
those who saw a gp in the last year for treatment of a minor illness or for a general health check and those who have had to put off seeing the gp in the last year also preferred to be visited in a practice with some diagnostic facilities .
with regards to the choices between the different service configuration , table  5 presents a utility - based ranking for all the configuration hypothesized.table  5predicted utilities for alternative scenarios of care deliveryscenariowaiting timecaregiverdiagnostic facilitiesindirect utility 
 95  % ci ( lower)95  % ci ( upper)220  minown gpmany3.113.033.19250  minown gpsome2.752.682.83180  minprimary care teammany2.602.522.68primary care centre10  min 
 primary care teammany 
 2.54 

 2.46 

 2.61 
 1390  minown gpmany2.542.462.6160  minprimary care teamsome2.242.172.32790  minown gpsome2.182.112.252690  minprimary care teammany2.031.952.102180  minown gpmany1.961.882.05200  minown gpfew1.871.821.92100  minanother gpmany1.791.741.841290  minprimary care teamsome1.671.591.7516180  minown gpsome1.601.521.699180  minprimary care teammany1.451.361.53140  minanother
gpsome1.431.381.48solo gp 
 70  min 
 own gpfew1.421.371.4710  minprimary care teamfew1.361.311.40590  minown gpfew1.291.241.351990  minanother gpmany1.221.161.27group gp 
 40  min 
 another gpsome1.181.131.2321180  minprimary care teamsome1.091.011.18390  minanother gpsome0.860.800.912390  minprimary care teamfew0.780.730.8311180  minown gpfew0.720.650.784180  minanother gpmany0.640.580.7180  minanother gpfew0.550.550.5524180  minanother gpsome0.280.220.3515180  minprimary care teamfew0.210.140.271790  minanother gpfew0.030.050.0027180  minanother gpfew0.600.660.55 
 indirect utility  =  0.546  +  [ 0.006    waiting time ( min ) ]  +  ( 1.321    own gp )  +  ( 0.811    primary care team )  +  ( 1.243    many diag . facilities )  +  ( 0.885    some diag . facilities ) predicted utilities for alternative scenarios of care delivery 
 indirect utility  =  0.546  +  [ 0.006    waiting time ( min ) ]  +  ( 1.321    own gp )  +  ( 0.811    primary care team )  +  ( 1.243    many diag .
facilities )  +  ( 0.885    some diag . facilities ) among the existing care models , the  primary care centre  would be the most preferred scenario , followed by the  solo general practice  and the  group general practice  . despite 
own gp  being the most preferred respondents caregiver , the actual context in which such physicians have to operate (  solo general practice)with few diagnostic facilities and long waiting times for the visits  would not be considered the best service alternative . with reference to the  primary care centre  , most of all , its superiority over the  solo general practice  derived from a grater diagnostic potential , rather than a shorter waiting time . indeed , assuming that the former service would not maintain a certain diagnostic power ,
even with no waiting time for the visit ( scenario 1 ) it would have a lower benefit score compared with the utility of the  solo general practice  .
group general practice  , to have a higher benefit score with such service configuration compared with the utility of the 
solo general practice  , respondents need to be compensated with a 40-min reduction in waiting time ( scenario 14 ) or , assuming no changes in waiting time , with more diagnostic services .
the results presented in this paper provide useful insights regarding community preferences for different primary care models . to the authors knowledge , this is the first large - scale study in this context that takes into account the impact of the diagnostic facilities .
the  willingness to wait  values have shown that a consultation with one s own gp is more important than being seen by a primary care team and than a practice with many diagnostic services .
this highlights an important finding given the tendency to limit relational continuity in current health policies . the respondents predilection for their own gp was also highlighted in similar studies [ 8 , 10 , 22 ] .
preferences differed also by respondents characteristics and past experiences , and some of the interaction effects that emerged were similar to those described by previous authors . consistently with the interactions found we can suppose that people who preferred to be visited by their own gp and who did not choose to be visited by a primary care team might be those with high and continuous healthcare needs , probably living in a certain degree of isolation , which makes it difficult to seek new or alternative care providers .
such group could see their own gp as a stable reference point . on the other hand ,
respondents who preferred a consultation with a primary care team regardless of their own gp are young people , with low healthcare needs , that probably have not yet developed a type of dependency on their own gp , and could therefore be more sensitive to service innovations . regarding subgroup preferences for diagnostic technologies , we can presume that people who preferred to have many diagnostic facilities in the practice are probably those with a greater willingness to pay and with high healthcare needs .
on the other hand , individuals who had a preference for some diagnostic technologies and who did not choose a practice with many diagnostic facilities seem to be those with a lower willingness to pay , with minor healthcare needs and without a propensity to wait too long for a diagnostic test .
the results obtained from the predicted utilities of different service configurations would need to be combined with the costs of different combinations of attributes to establish the most cost - effective model of care .
nevertheless , these results have important implications for the demand for new primary care models . considering that the primary care centre would perform better than the  solo gp  even with some diagnostic services ( for example scenario 6 ) , for a more rapid diffusion of this model
, policymakers and managers , at least in the first phase , may direct care provided by primary care centres towards a younger population with low healthcare needs .
this group , indeed , has demonstrated a strong preference for this specific service configuration .
this strategy , of course , would only partially consolidate team - based community models , because such organisations were particularly designed to tackle the needs of the chronic sick and of elderly people .
however , it could be considered a valid alternative to the inappropriate use of a&ed services ( non urgent access ) .
future policies to improve primary care organisations should be based on a broader framework that takes into account the different needs of population sub - groups , balancing responsiveness with care continuity , equity , and appropriateness . in this context , the present study , following on from previous research , provides evidence on how the tuscan healthcare system is moving toward a pro - active approach in order to provide differentiated services specific to different healthcare needs .
first of all , the relatively small number of attributes used in the study and the exclusion of factors that appeared as relevant in other studies , such as  consultation length  [ 11 , 24 ] , in order to avoid placing a considerable cognitive burden on respondents , may have led to the omission of other characteristics that may have been captured within the constant term .
there is little discussion on this in the literature , and where a significant constant is identified , the problem tends to be ignored .
while attempts were made to select the attributes in an unbiased way , it is not possible to establish whether other qualitative approaches would generate the same attributes and levels . for a better evaluation of the significance of the attributes found , comparative qualitative approaches identifying attributes and levels for
secondly , even though the response rate achieved was in line with other dces in similar settings [ 14 , 16 , 18 , 19 ] , a comparison with tuscan population data revealed that older respondents and women were overrepresented .
this result may be expected in an  in - home  interview survey of this type .
a more ample evaluation of sample selection bias was not feasible because age and sex were the only available data on the tuscan population .
the potential for such biases needs to be addressed in future studies , by allocation of more resources at the recruitment phase .
thirdly , potential perceptions of implausible attribute - level combinations could have reduced response efficiency , deteriorating the precision and accuracy of parameter estimates .
although the pilot revealed that individuals did not find the alternatives in the choice tasks unrealistic , identification of this potential bias was not practical given the lack of data on the actual primary care services received by respondents .
future dces should attempt to follow up model estimation with focus groups to address the validity of the results achieved by asking respondents if their preferences are consistent with the findings of the estimated model .
furthermore , collecting information on the respondent s current primary care service would also prevent the adoption of a forced choice design , for instance , using such information for the construction of a status quo alternative in the choice set .
fourthly , the rich set of respondents characteristics , incorporated into the model through the interaction with the attributes , allowed us to show several aspects of preference heterogeneity .
however , some differences in tastes will probably remain random to the extent that it can not be related to observed characteristics .
future analyses should explore the added value of discrete choice models that relax the assumption of taste homogeneity ( e.g. mixed logit and latent class model ) by allowing for random taste variation .
 objectivesthe shift toward more innovative and sustainable primary care models in italy leads policy makers and clinicians to face difficult decisions between options that are all regarded as potentially beneficial . in this study , 
 patient preferences for different primary care models in the tuscany region of italy were elicited . the relative importance of different attributes to the surveyed respondents 
 was then examined , as well as the rate at which individuals trade between attributes and the relative value of different service configurations.methodsa discrete choice experiment survey explored the following attributes in a stratified random sample of 6,970 adults : primary care provider , diagnostic facilities and waiting time for the visit.resultsrespondents ( 3,263 ) were likely to prefer a consultation by their own general practitioner ( gp ) and a practice with many diagnostic facilities . 
 the predicted utilities of different service configurations have shown that a  primary care centre  with many diagnostic facilities 
 was preferable to a  solo gp  model or a  group general practice.conclusionsthe study demonstrated how a patient choice model could be used by decision makers for developing successful policies that takes into account different healthcare needs , balancing responsiveness with care continuity , equity and appropriateness . considering that a primary care centre would perform better than a  
 solo gp  , especially for younger respondents and for those with minor healthcare needs , for a more rapid diffusion of this model policymakers and managers could direct the care of primary care centres towards these targeted subgroups , at least in the first phase . 
Introduction and background
Methods
Measurement of preferences
Selection of attributes, levels, and scenarios
Experimental design and construction of choice sets
Estimation procedure
Tests of the validity of responses
Results
Sample characteristics
Internal validity
Main effects model
Segmented model
Predicted utilities
Discussion and conclusion