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Community pharmacy forms a major component of the primary healthcare system in most developed nations. Pharmacists have also become the most accessible and conveniently located points of contact for individuals within the healthcare system.[1, 2] Traditionally, pharmacists have been mainly involved in the dispensing of medications. Increasingly, however, their role has diversified and pharmacists are now involved in the provision of a wide range of healthcare services in the community ranging from drug information provision, health screening, medication management, disease-state management and provision of palliative care.[2, 3] Several large community pharmacy-based studies (including some robust randomised controlled trials) have been conducted globally.[4-14] A substantial number of services targeting disease-state management have demonstrated the potential benefit of such pharmacist-delivered services both clinically and/or economically.[4, 5, 8-15] In fact, some of these pharmacy-based services, such as repeat dispensing, smoking cessation and medication reviews, have also been translated into sustainable services in countries like the UK, often as part of their national pharmacy contracts.[16, 17] However, evidence of improvements in health outcomes from pharmacist-led services is often mixed. This, coupled with the diversity of research approaches and methodologies, makes it difficult to reach an overall conclusion about the impact of pharmacists' healthcare service delivery on patient outcomes.
The concept of ‘patient-centred’ health care has gained an important focus especially in the case of patients with chronic conditions. There is increasing evidence to suggest that understanding a patient's preferences, views and needs, and organising healthcare services to match these aspects together with clinical viewpoints, can lead to improved health and economic outcomes. Previous studies have demonstrated that patient preferences for healthcare services and interventions can impact on their willingness to use services. Thus, investigating the patient perspective can also provide an insight into which health-service aspects are perceived to be of value to patients and can influence their decisions to use/uptake the services, which in turn may reflect on the sustainability and economic viability of these healthcare services.
Measurement of patient satisfaction is one of the most commonly employed methods for eliciting the patients' perspectives for healthcare services as well as pharmacy-based services. However, this technique has several drawbacks including the lack of a consensus regarding a theoretical framework for patient satisfaction, the use of self-developed, non-validated ad-hoc instruments for measuring satisfaction, and issues such as high baseline satisfaction that limit the ability to detect real differences in patients' opinions. Besides these methodological constraints, satisfaction surveys are unable to provide information about the potential value of future services, the aspects/attributes of these services that drive satisfaction levels and the relative importance attached to these aspects/attributes; i.e. information that can provide guidance on the optimal allocation of resources especially in a budget-constrained health system. Further, satisfaction surveys cannot be used to inform economic evaluation and thus are limited in their ability to bring the patients perspective into policy decision making.
Novel preference elicitation techniques such as ‘stated preference methods’, where individuals state what they would choose when offered a product or service, are becoming increasingly popular in the health sector.[23, 25, 26] Stated preferences, unlike revealed preferences, get respondents to make choices based on hypothetical scenarios rather than observing them when making an actual or real-life choice.[23, 25, 26] The last decade has seen an increased use of these methods including conjoint analysis and discrete choice experiments (DCEs) to elicit preferences for healthcare products and services.[25, 27-30] These two methods have a common format in terms of the underlying attributes, use of experimental design methods for instrument design and utilisation of statistical models to determine the importance of each attribute to preferences, although they differ substantially with respect to their theoretical framework as well as preference elicitation. Conjoint analysis involves asking individuals to rank or rate the alternatives provided while DCEs elicit preferences by asking individuals to choose one alternative from those presented.
Discrete choice experiments have their origins in mathematical psychology and have been successfully used in market research, transport economics and environmental economics. Applications in health have been relatively recent since the early 1990s.[25, 29] Within the context of health care these techniques have been successfully applied in several areas such as valuing of patient experience factors, valuing health outcomes, trade-offs between health outcomes and experience factors, job-choices, health provider's preferences for treatments or screening and developing priority setting frameworks.
The DCEs are based on the random utility (RU) framework and assume that a healthcare service can be described by various attributes or characteristics and the extent to which respondents' value the service depends on the level of these attributes.[23, 26] Thus, when offered a choice, respondents choose the alternative that they believe will provide them with the highest value or utility depending on the level and combination of service attributes.[23, 26] The DCE techniques have been used to establish the strength of preferences for healthcare services, to identify which attributes are important to respondents, the relative importance of the different attributes of the service as well as the trade-offs that respondents are willing to make, i.e. choosing one attribute and forsaking another when making a choice.[23, 26] Further, DCEs have also been used in configuring optimal service design, predicting demand and uptake of services under differing scenarios, estimation of willingness-to-pay (WTP) when a monetary/cost attribute is included and informing economic evaluation modelling (for example cost-benefit analysis).[25, 29, 32]
Pharmacy-delivered specialised services are a relatively novel paradigm and are also quite complex in nature. Traditionally, pharmacy practice researchers have often measured patient satisfaction with pharmacy-based services. Measuring patient preferences for such specialised services using techniques such as DCEs can provide important information which can assist in the development of optimal services that patients will use, are willing to pay for, and thus are sustainable and economically viable in the future. An example of a hypothetical DCE design for a pharmacy-delivered specialised asthma service, including possible service attributes and levels, has been illustrated in Figure 1.
Payne and Elliot need to be acknowledged for bringing the DCE technique to the notice of the pharmacy practice community by the publication of their comprehensive review. Their review explains how this technique can be effectively applied in the measurement of preferences for pharmacy services and also identifies applications of DCEs in health care by conducting a systematic search of the literature from January 2003 until May 2004. We believe that publication of this timely review in 2005 may have encouraged the adoption of the DCE technique by several researchers in the field of pharmacy since. Hence, the aim of our study was to conduct an in-depth scoping review of the literature and provide a current overview of the progressive application of DCEs within the field of pharmacy
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We have conducted a scoping review of the current literature and identified and evaluated studies utilising the DCE methodology within the field of pharmacy. Results indicate that the pharmacy profession has adopted the DCE methodology although the number of studies is quite limited. The DCE methodology has been applied to elicit preferences for different aspects of pharmacy products, therapy or services. In the majority of the studies, preferences for particular products or services were elicited from either users (i.e. patients) or providers (i.e. pharmacists), with just two studies incorporating the views of both (patients and pharmacists). Further, most of the studies examined preferences for process-related or provider-related aspects with a lesser focus on health outcomes.
This is one of the first reviews in the literature which explores how the pharmacy-related DCEs have been designed and conducted and evaluates their progressive application in the pharmacy setting. A strength of our study was that the reviewed studies were thoroughly analysed in terms of their quality and implications. The search strategy was extensive and covered a large number of relevant databases. Further, the study highlights the value of the DCE technique and the need for utilising this technique in pharmacy practice research.
Some limitations also need to be considered. One methodological limitation was reliance on published studies, whereby we may not be accurately representing the state of DCE practice in pharmacy because of issues such as publication lag. Also the search strategy used to identify potential articles for this review was limited to the specific search terms and the databases that we used, which may have affected the articles identified. However, every effort was made to ensure that the search strategy was as comprehensive as possible. Another limitation of our study was the exclusion of the grey literature, which may have led to some relevant papers not being included in our review.
Our review of the literature showed that very few pharmacy-related DCE studies have been published in the last decade. This could be because evaluation of pharmacy products and services has been traditionally done using ‘patient satisfaction’ surveys. Whilst the construct of patient satisfaction is important, clearly there exist some issues and drawbacks with its measurement. Further, measurement of patient satisfaction is limited in terms of the information that can be provided with respect to importance of attributes, trade-offs between attributes, prediction of demand and WTP estimation. Satisfaction surveys also cannot be used in economic evaluation to inform pharmacy policy as they do not incorporate opportunity cost or strength of preference. The DCEs, on the other hand, can overcome all these limitations of patient satisfaction measurement and also have the advantage of being used for economic evaluation and policy making, for example, within cost-benefit analyses. This emphasises the need for moving beyond the commonly used satisfaction instruments and the adoption of DCEs in routine pharmacy practice research.
Overall, pharmacy-related DCEs were consistent with DCEs conducted in general health care with respect to the methodology of designing and conducting the choice experiment. Similar trends between pharmacy-related DCEs and health DCEs were noted for design types and design plans used, the number of choice sets per patients, inclusion of monetary attributes in choice sets and validity tests conducted, Trends, however, differed for aspects related to types of attributes selected and models used for estimation.
Our study found that most of the reviewed studies focused on process attributes or provider attributes with very few health-outcome attributes. This was not the case in the general health DCE literature where the focus has been equally, or perhaps more so, on health-outcome attributes than on process attributes. Also the majority of the pharmacy DCE studies investigated preferences for ‘generic’ pharmacy service provision and included ‘medication/chronic-disease management provision’ as one of the attributes. There was a lack of studies investigating ‘specific’ medication/chronic-disease management services. On the other hand, DCEs in health care more commonly elicit preferences for specific disease screening/management.[47-51] This could be because specialised service provision is better developed in general health services.
Also, it was interesting to note that one of our reviewed studies included 11 attributes in the design. While there are no design restrictions on the number of attributes that can be included in a DCE, often in practice most DCEs in health care have contained fewer than 10 attributes so as to ensure that all attributes are taken into consideration by respondents when making a choice. Increasing the number of attributes in the study design can increase the complexity of design as well as cognitive difficulty of completing a DCE, which can increase response variability. On the other hand, inclusion of fewer attributes can cause omitted variable bias owing to exclusion of key attributes. Rigorous piloting is thus necessary to get the balance of attributes right.
The DCE models that can be estimated from the choice data often depend on the nature of the choice problem as well as the experimental design used. Published literature indicates that while earlier DCEs in health care used the simple logit and probit models, over the last decade they have progressed towards more flexible and advanced econometric models.[30, 53] This is evident from the increasing applications of nested logit, random parameters logit and latent class models which allow for investigation of heterogeneity of preferences.[54-56] The pharmacy DCE studies were, however, restricted to the use of traditional logit or probit or MNL models with only one study utilising the latent class model to investigate pharmacist preferences for specialised services. Probit or logit models or random effects extensions of these models often report the mean preference weights for the sampled population. However, it is likely that individuals or groups of individuals may have different preferences. Accounting for this heterogeneity is thus important and ignoring it may compromise the behavioural realism of the model. The majority of our reviewed studies did not investigate the existence of preference heterogeneity in the study population and generally reported on the mean preference weights. This highlights the need for pharmacy practice researchers to take a structured approach and gain greater understanding of DCE methodology with respect to both the experimental design as well as the estimation models.
Monetary attributes were considered to be important by most patients and pharmacists in the studies reviewed. With respect to pharmacy services, patients showed a preference for lower costs or co-payments while pharmacists preferred higher incomes. On one hand, this information can be used to determine how much patients value pharmacists and pharmacy-based services and the extent to which they are willing to make investments in their health, while on the other hand it can provide insights into pharmacists' job choices and the financial gain they expect in order to deliver the services. This can be useful information at the policy level and in the development of economically viable services.
The majority of reviewed studies elicited patient preferences or pharmacist preferences, with just two studies examining preferences of both. Previous studies have shown that preferences of patients and providers for aspects of drug therapy and screening programmes do differ, thus highlighting the importance of understanding the perspectives of both, patients and providers, for particular products or services. This may be an important area of future research that will help us understand how well providers' views actually reflect patients' preferences, especially for novel specialised services. Also, understanding both perspectives may help identify similarities as well as mismatches, which in turn may help in the design of future optimal services that pharmacists are willing to deliver and patients are willing to use.
Another important observation in the measurement of patient preferences for pharmacy services was the existence of a status-quo bias where respondents tended to favour their current pharmacy or pharmacy service. Previous studies have shown that patients often value services more highly once they have experienced them.[58, 59] Further, the lack of awareness or information about novel alternatives may prompt patients to choose the status quo. As pharmacy delivered specialised services are a relatively new paradigm, this lack of awareness and experience may haveled to patients preferring their current alternative/service. Future services need to overcome this status-quo bias in order to ensure their continual uptake by patients and long-term sustainability.
External validity testing of DCE responses is important, especially as these responses are made in regards to hypothetical choices. However, there have been relatively fewer tests of external validity in health DCEs. One possible explanation may be that these DCEs have been conducted in countries with publicly funded health care where patients have limited choice and usually do not pay at the point of consumption for many of the health services, thereby making external validity tests difficult to conduct. Consistent with health DCEs, none of the reviewed pharmacy-related DCE studies conducted tests of external validity. It is, however, important to note that the community pharmacy setting can offer a unique opportunity to conduct such external validity tests for hypothetical WTP estimates especially because pharmacy patients often pay at the point of consumption for many pharmacy services and interventions.[24, 60] Pharmacy practice researchers need to take advantage of this opportunity and conduct more research in this area of external validity testing.