In this section, we first offer brief descriptive findings from our literature review and empirical case study. We then describe our theoretical synthesis in the form of a new model of individual adoption of innovations (PHOENIX) which refines and extends Rogers’ original model of individual adoption by integrating relevant psychological theory for each stage.
The structured search phase of our hermeneutic literature review identified 3528 titles, but as described above, after screening these we found that only three met the inclusion criterion (a specific psychological theory relevant to the adoption of innovations by professionals). Using snowball searching, asking experts and consulting textbooks identified an additional 72 sources; after excluding duplicates our final dataset consisted of 75 papers and book chapters. From these, we identified a total of 20 potentially relevant candidate theories or theoretical domains from cognitive, social, organisational, developmental and sports psychology.
Implementation rates for routine HIV screening were low at both sites (10-40% across the A&E and AAU departments), but varied widely between individual clinicians and between departments, and contrasted with some other departments achieving 90% coverage of HIV screening. The overall picture was of clinicians who were faced with an overwhelming volume of information, which they were coping with through strategies for heavily filtering and prioritising information. This guidance on routine HIV screening was by and large viewed positively but was also being evaluated in the wider context of other priorities (including those set by the wider organisation and system), and their role in comparison to other potential providers such as general practitioners. While in principle offering the test was an individual decision for each clinician interacting with a particular patient, the context for those decisions was strongly shaped at the group level, though collective discussion and norms within particular teams. Many departments were using classical environmental cues such as prompts at the bedside to remind clinicians about the test and reassure patients about its acceptability, but these were not sufficient to ensure implementation. For example, one unit had widespread wearing of ‘we test for HIV’ badges and had integrated prominent prompts for the test in the booklets used to record the processing of each patient, and yet still had a testing rate of only 10%.
We found that the stages of adoption from Rogers’ diffusion of innovations model did provide a useful structuring device for disentangling different psychological processes during adoption, for analysing those in relation to psychological theories from the literature and for organising and analysing our empirical data, as we describe below. Each of these stages of adoption appeared to be analytically distinct from a psychological perspective, in that each of the stages involved distinctive psychological processes which related to different wider psychological theories, and there did not appear to be any additional stages required to explain our data. This does not mean that these stages described a universal or linear process; rather, it means that the stages were conceptually and analytically distinct (though we discuss below some concepts that varied from those described by Rogers for each of the adoption stages). Moreover, using these stages of adoption as a structuring device opened up a broader and more useful range of psychological theory.
Theoretical synthesis: the PHOENIX model
We propose the mnemonic PHOENIX to depict the five stages of the refined model of individual adoption of innovations, preceded by prior conditions. Each stage is underpinned by a number of specific psychological theories. The PHOENIX stages are summarised in Table 2 and described in more detail below. Examples of empirical data are provided in an additional file.
Person and History (prior conditions)
Rogers described prior conditions such as age, gender, cultural background, motivation, values, goals and relevant (general or specific) knowledge, all of which set the context for the stages of an individual’s decision to adopt an innovation or not (1).
In psychological terms, these antecedent characteristics can be understood principally in terms of personality traits. Costa and McCrea’s five factor model (24) is the principal psychological model used for describing personality characteristics, and sees personality in five basic dimensions of neuroticism, extraversion, openness, agreeableness and conscientiousness. Several of these may have links to early adoption, such as openness and conscientiousness, but it is unlikely to be either practical or ethical to discriminate between health professionals on the basis of their personality profiles. Accordingly, we did not seek to assess personality profiles as part of our empirical data collection, though the relationship of personality type to innovation adoption is an area of potential future research.
More relevant was the history of each person before this specific situation of potential adoption. Some participants already had experience of the utility of testing for HIV as part of their clinical care, or had come from departments where this was already well-established practice, and they referred to this as a reference point shaping their adoption in this situation. Conversely, some participants had experience of previous innovations that they had not perceived positively, and that also appeared to affect their openness to this proposed change. In almost all cases, the individual profile and history of each individual was clearly relevant in shaping their view of this innovation.
Overload avoidance (knowledge stage)
In Rogers’ model, the first stage in the individual adoption process was ‘knowledge’ – whether and when the potential adopter learnt about the innovation. For health professionals today, this is not a matter of having too little information, but rather facing volumes of information that are beyond the capacity of individual professionals to absorb (25, 26). Reflecting this, we found that the information strategies being employed by health professionals were less concerned with seeking out information than filtering it.
In our case study, we identified four specific filtering mechanisms used by health professionals to avoid information overload:
- getting most of their information through peer networks (different networks offered different kinds of information – and of different quality and detail);
- being selective about additional sources of potentially useful information such as email alerts;
- prioritising certain kinds of information (notably, safety-related messages) from more general NHS information channels; and
- seeking out additional information on the basis of what was of personal interest to them.
These strategies can understood through a combination of two psychological theories: bounded rationality and selective exposure to information.
Bounded rationality was first introduced as a concept by Simon in 1955 (27) as a way of describing the practical strategies of thinking that people use to make choices, rather than the impractical and in many instances impossible attempt to analyse all possible options. In the situation of cognitive overload facing today’s healthcare professionals, finding such ‘satisficing’ strategies for reduced processing of information is arguably essential. This need not mean worse outcomes; as Marewski and Gigerenzer argue, simplified approaches (using heuristics, for example, rather than working exhaustively through each choice) can be “ecologically rational” (28, p80). The heuristic to attend preferentially to information offered by peers, for example, usually means that a similarly qualified professional has already processed this information and considered it relevant.
Selective exposure is a psychological mechanism through which people avoid information likely to challenge their attitudes, beliefs or behaviours (29). The model for selective exposure contrasts two conflicting motivations: a ‘defence motivation’ (avoiding information creating a cognitive dissonance) and an ‘accuracy motivation’ (seeking out the most relevant information whether it is comfortable or not). Information about new practices and techniques can be expected to create some cognitive dissonance (by definition, it is likely to challenge the professional’s current practice). Nevertheless, the accuracy motivation could sometimes overcome this discomfort – as illustrated by the fact that clinicians in our sample were distinctively open to safety-related information.
Evolving attitudes to the innovation (persuasion stage)
The second stage in Rogers’ innovation adoption model was persuasion. In psychological terms, persuasion can be seen as the formation of positive attitudes towards the innovation. Rogers strongly emphasised the social nature of persuasion, especially by others whom the intended adopter considered similar to themselves (peer opinion leaders) or more knowledgeable (expert opinion leaders). In his model of innovation, Rogers emphasised a construct he called “felt need” – that is, a perceived problem for which the intended adopter views the innovation as the solution (1).
While Rogers described felt need as a prior condition for the individual adoption process, our data suggest that the mechanism of attitude formation towards an innovation in a healthcare context is somewhat different. Rather than an individual felt need, there appeared to be a greater or lesser degree of shared acknowledgement among a particular group of clinicians (consultants in a particular department, for example) that untreated HIV was a problem to be addressed. One example of this was the creation of shared awareness of the problem of untreated HIV through a process of audit and feedback.
The theory of socially shared cognition, summarised by Tindale et al (who drew on earlier work by others in social and organisational psychology) (30), holds that attitudes in social groups are not simply the aggregation of individual attitudes, but are also a collective process. The group forms shared representations, understandings and meanings, which shape the attitudes and behaviours of individuals who identify with, and operate within, the group. Shared cognition emerges through a combination of common experience, learning, social interaction, and social comparison (30). Similar processes of social cognition have been described through Gabbay and Le May’s concept of ‘mindlines’(31).
Networked decisions (decision stage)
Psychological theories such as the theory of planned behaviour typically represent decision-making as essentially individual. However, our data painted a picture of a networked decision processes that operated at two levels: collective decision-making combined with individual judgements about its application to particular patients.
Individual participants described being well aware of the collective view of their department, even when they did not necessarily or always agree with it. This collective decision emerged and evolved over time from interactions within the group. This was partly formal, with departments having processes for creating their own guidelines which acted as a mechanism for deciding whether and how to incorporate external guidance. It was also informal, with participants describing collective departmental views on particular practices which might or might not be formally constituted in departmental guidance. This collective departmental decision could be seen as a collectively constructed and maintained social norm (32). Alongside this, though, the individual level of decision-making remained as well. There was a clear sense of clinical autonomy, with individuals making their own decisions about the care required by individual patients, informed by but not wholly determined by the wider expectations of the department.
The context for innovation adoption is conventionally described in terms of two levels beyond the individual: the organisation within which they work, and the wider system and context (4). However, the term that we have used above is the department, and this is because our findings strongly suggested that it was this smaller grouping that was the principal influence on behaviour, and thus that it should be considered separately from the hospital as a whole. We suggest that this is because of the relative size and shared ties of the smaller departmental group. The theory of personal social networks proposed by Sutcliffe, Dunbar (33) depicts the individual at the centre of a series of concentric circles of relationships, with smaller numbers of closer ties in the centre (in single or double figures) up to a larger active network but with weaker ties of tens or hundreds of people. Given the social and collective processes of forming attitudes and decisions that we describe above, hospitals are simply too large for these collective processes to function throughout them. This is not to say that they do not matter, but rather than they matter in particular ways; in our study, control of overall resources and organisational priorities were two key hospital-level influences. Nevertheless, the primary unit shaping these networked decisions was not the hospital, but the individual departments within it.
We have used the term ‘department’ throughout this paper in this sense, as meaning the functional team within the hospital. What are formally defined as departments vary widely in size, with some also being much larger than the group functioning collectively through the processes defined above. We have opted to use the term department as the generic term for functional units within a hospital.
Implementation support (implementation stage)
This implementation phase was the stage that our participants identified as the key challenge with HIV screening; not a lack of commitment in principle, but difficulties with doing it in practice. The central psychological issue is our limited capacity to think about and act on many things at once (34). We found that implementation was affected by three key strategies for overcoming limited cognitive capacity: prioritisation, routinisation and using external cues.
The extent to which implementation was seen as a priority varied by department as well as by individual, reflecting the networked decisions described above. Some departments had successfully formed a collective view that HIV testing was a core part of their activities. In others it was considered to be a relatively low priority; this was particularly the case in the urgent care departments, where HIV testing was perceived to be a low priority in comparison to more immediate care requirements.
The second theme was routinisation; ‘automation’ of the desired action into a new, regular habit so that less cognitive effort is required (e.g. integration of the new behaviour into an established routine). This requires the effort of rethinking an existing routine (taking bloods, in this instance); in psychological terms, consciously focusing on and adapting a previous pattern of behaviour until the new routine itself becomes habitual (25). How far individual participants undertook that effort itself depended on the priority they perceived to be given to that by the department overall. It also depended on their personal situation – people who had recently joined the department had had to adapt many of their routines and incorporating this HIV testing had been included part of that adaptation.
The third strategy of using external prompts (e.g. reminders, checklists) at the point of implementation aims to reduce the need for conscious effort through the environment providing a cue instead (35). This strategy had been widely used at both sites in this study. For instance, in site 2 a booklet was completed for each patient in AAU (the ‘clerking booklet’) with two specific prompts for HIV screening; prompts being included in the IT systems in both AAU and A&E across both sites; and reminders for HIV testing in trays containing vials for blood tests.Such environmental cues have proved widely successful in other contexts (36). However, given the low implementation rates described above they had clearly not been successful in the sites studied (although other uses of external supports to implementation had been more successful, such as pre-set standard blood tests incorporating HIV).
UneXceptional (confirmation stage)
The final stage in Rogers’ innovation adoption model is called ‘confirmation’. In psychological terms, the confirmation phase is the process of making something part of unexceptional, routine practice. In psychological terms, this can be understood in terms of dual process theory; the transition from the reflective, effortful and consciously controlled new behaviour initially necessary for implementation to an established habit (25, 37).
This process of becoming automatic requires sustained effort over a period of time, though. Other than for particularly motivated individuals, sustaining this effort requires the organisation to support this process of confirmation. Brewster and colleagues describe different ways in which organisations acted to support the confirmation process, with a small number of staff within a team providing sustained support to confirmation for up to a year (38). Our sites took similar approaches, with HIV departments and particularly engaged colleagues in some other departments actively supporting this confirmation process for months or even years. This was needed due to the time needed for individuals for form habits (typically weeks or months, reflecting wider research(39)) together with staff turnover, in particular for junior doctors. One additional method that emerged from our sites was using local evidence; that is evidence that the action was valuable in their local context, rather than general evidence from elsewhere.