To provide high quality care for patients, the healthcare industry is dependent upon the provision of complex and expensive medical devices. It is widely accepted that if devices are to be used effectively they must meet the requirements of their users
], however, capturing user requirements for healthcare technology is extremely complex. Although clinical effectiveness and safety are the primary concerns in medicine, many other aspects must also be considered including training needs, storage, labelling, servicing and cleaning
]. Moreover, for the same medical device, the concepts of effectiveness and safety may change according to the specific clinical problem, medical specialization and patient condition.
The topic of user requirements of medical devices is of interest to a wide variety of individuals and organisations that are required to make decisions on the development, purchasing and prescription of these products. However, research has shown that collecting and considering this information is a challenging undertaking; a lack of time and resources may preclude rigorous work into requirements
], as can a lack of knowledge of appropriate methods for data collection and analysis
]. This can result in the collection of data that are incomplete, difficult to interpret or that fail to address the questions of interest
Finally, and most fundamentally, the complex nature of medical device user requirements means that for any one medical device there are likely to be a large number of possible users, potentially including both professional and lay users, all with differing specialities, skills and abilities. Even within seemingly homogeneous user groups, individuals will have received different training and will vary in their working patterns, attitudes and preferences. In addition, how a device is used will vary considerably, according to the particular clinical procedure being performed and the physical and organisational context in which it is used
]. This information must not only be collected and considered, but differences and conflicts between users must also be balanced. This is a critical issue for the developers of medical technology but also for healthcare providers when making purchasing decisions. It is a particular issue for publically funded healthcare providers who must demonstrate that the purchasing decisions about high-cost equipment are transparent and are be based on the best possible evidence available at the time.
The use of scientific quantitative methods to support decision making
is considered necessary in healthcare organizations, where the personnel are committed to follow only the best available evidence according to well-designed trials
] or network meta-analyses
]. Nonetheless, despite the hierarchy of evidence, the complexities of medical device decision-making require a spectrum of qualitative and quantitative information
]. At the start of a user need elicitation problem, a wide-ranging and open-ended study should be conducted to collect data about the needs and priorities of healthcare professionals
]. This type of information is critical to developing a broad understanding of the range of user requirements. In medical decision-making, qualitative methods have a crucial role in examining evidence from previous studies
] and appraising this according to different contexts of use. It has been suggested that improving the methods used in qualitative studies will legitimise this type of data and increase its use in healthcare decision-making
] as advocated by Kaplan
], who concluded: “a plea is made for incorporating qualitative/interpretive/subjectivist methods, without prejudice to other approaches”.
Furthermore, evidence-based care advocates that medical decisions are made with reference to the best available research evidence
However, the nature of qualitative research can limit its use in scientific decision-making tasks such as user needs requirements elicitation for medical devices. The influence that the researcher plays in designing and interpreting studies has resulted in qualitative methods being viewed with scepticism by the medical community
]. In addition, researchers have encountered problems when attempting to use qualitative data in the analytic and scientific decision-making processes that are a fundamental part of healthcare research
]. For example, how can open-ended interview data collected from a number of caregivers with a range of opinions be used to make decisions on the design of a new medical device in a transparent and rigorous way
]. There is need therefore for new approaches that allow the breadth and depth of the topics under investigation to be captured, yet also allow these to be quantified and prioritised, and for the process to be as transparent as possible. This is not only important for the decision makers but also for the healthcare staff; research has shown that successful adoption of new healthcare technology is dependent upon joint ownership of the decisions made during the development process
]. Moreover, the decision outcome should be easy to understand, as intelligibility is strongly appreciated in medical domain decision-making
], especially in the public sector. Finally, although not the primary aim of this study, the use of AHP clearly has implications for device manufacturers and future technology strategy in this area. In fact, medical device companies have also demonstrated an interest in scientific methods to elicit user needs, to enable them to respond to clinical demand and to enter new markets by adapting their products to the requirements of different medical specializations
The Analytic Hierarchy Process (AHP) is a multi-dimensional, multi-level and multifactorial decision-making method based on the idea that it is possible to prioritize elements by: grouping them into meaningful categories and sub-categories; performing pairwise comparisons; defining a coherent framework of quantitative and qualitative knowledge; measuring intangible domains. This hierarchical approach allows the construction of a consistent framework for step-by-step decision-making, breaking a complex problem into many small less-complex ones that decision-makers can more easily deal with. This paradigm, known as divide et impera
] (divide and rule) and widely investigated in medicine
], has been demonstrated to be effective in healthcare decision-making
The AHP is effective for quantifying qualitative knowledge as it allows intangible dimensions such as subjective preferences and comfort to be measured. This is important in medical decision-making as these factors
], which are normally examined with qualitative research, cannot be measured directly using an absolute scale
]. The AHP is particularly effective for quantifying experts’ opinions
] that are based on personal experience and knowledge to design a consistent decision framework. This is a crucial point in any medical context
], where not all of the relevant information is objective or quantitative. A number of researchers have highlighted the benefits of using AHP to explore user needs in healthcare
], and in particular for including patient opinions in health technology assessment
], choosing treatments
], and improving patient centred healthcare
]. Other methods that have attempted to elicit and quantify user needs in healthcare are conjoint analysis (CA)
] , discrete choice experiments
] and best-worst scaling
]. A growing number of articles have focused on comparing AHP with these methods, and in particular with CA. According to Scholl et al.
], AHP has proven to be more suitable than CA for complex decisions involving many factors. Mulye
] suggested that AHP is more effective than CA when more than 6 attributes have to be prioritized. Ijzerman et al.
] concluded that AHP, when compared with CA, resulted in more flexible, easier to implement and shorter questionnaires, although it may generate some inconsistences and other methods may have a more holistic approach. In another study, Ijzerman et al.
], concluded that AHP lead to the overestimation of some alternatives although the differences found between AHP and CA, were mainly ascribed to the labelling of the attributes and the elicitation of performance judgments.
In our elicitation of user needs, we used AHP rather than the methods mentioned above because this method has been applied to medical decision-making
] at the hospital level for budget allocation
] and medical device purchasing
]. It has been shown to be useful for a range of healthcare related decisions and for individuals from a range of backgrounds. As such, this method has the potential to be effective for the different organisations and individuals that are interested in eliciting user requirements, for example: developers wishing to improve device design, hospital managers who must allocate budgets and clinical engineers that are required to select devices. In addition to assisting each of these isolated tasks, a method that could be shown to be usable by all these groups could also improve communication between them, which is also essential in healthcare decision-making. AHP is normally used within a group decision-making process and requires that the decision-makers meet to compare and discuss their weights and decisions as a means to develop a consensus on group weights and achieve a group decision. However, this was not the purpose of this study, which aimed instead to explore the differences between the needs of clinicians with different specializations and different clinical settings. In summary, the adoption of a common method to elicit and prioritise user requirements could facilitate a wide range of decisions related to the design, selection and purchasing of medical devices.
In this study, we focus on clinical user needs related to the use of a multi-slice Computer Tomography (CT) scanner in a medium size city hospital. The multi-slice CT scanner refers to a special CT system equipped with a multiple-row detector array to collect simultaneously data at different slice locations. The multi-slice CT scanner has the capability of rapidly scanning a large longitudinal volume with high resolution. There are two modes for a CT scan: step-and-shoot CT or helical (or spiral) CT
]. In recent years, developments in CT technology have provided increasing temporal and better spatial resolution. Scan times are much shorter and slice thickness much thinner with increasing rotation speed and increasing number of active detector-rows, from 4 and 16 detector rows to 64-detector CT scanners
]. The different features of this device may significantly affect its costs. For instance, to equip this device with a system for continuous patient monitoring during the examination may be expensive. In addition, the technical performance of the device may strongly vary, affecting the final cost. It is therefore of paramount importance to elicit user needs before the purchasing decision is made to ensure that the right device is chosen and not one with unnecessary and costly features.
In particular, we focus on the application of AHP to identify the differences between the needs of clinical users, stratifying them according to specialization and intervention (elective versus emergency). We describe how the AHP method was adapted to improve its effectiveness for application in healthcare contexts
], while a more general description of the AHP can be found elsewhere